OCT Angiography

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that visualizes the microvasculature in the retina and choroid by detecting motion contrast from flowing blood. Developed by Jia and colleagues in 2012, OCTA uses repeated OCT scans of the same tissue location to identify blood flow based on the decorrelation signal. It has become a critical diagnostic tool in ophthalmology for detecting retinal and macular diseases without requiring fluorescein injection.

Optical imaging technique for vasculature visualization

Jia, Y., Tan, O., Tokayer, J., et al. (2012). Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Optics Express, 20(4), 4710-4725.

Wang, R. K., An, L. (2012). Doppler optical micro-angiography for volumetric imaging of vascular perfusion in vivo. Optics Express, 14(17), 7881-7895.

Spaide, R. F., Fujimoto, J. G., Waheed, N. K. (2015). Image artifacts in optical coherence tomography angiography. Retina, 35(11), 2163-2180.

pet-kinetic-modeling ct-iterative-reconstruction dti-tractography quantitative-susceptibility-mapping radiomics


OSDI

The OSDI is a 12-item symptom questionnaire designed to screen for and grade the severity of dry eye disease and other ocular surface disorders. Developed by Schiffman, Christianson, and colleagues (2000), it quantifies patient-reported ocular irritation and visual function limitations across frequency and impact domains. The OSDI is the most widely used screening tool for dry eye in clinical practice, clinical trials, and epidemiological studies worldwide.

Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D., & Reis, B. L. (2000). Reliability and validity of the Ocular Surface Disease Index. Arch Ophthalmol, 118(5), 615-621.

Begley, C. G., Chalmers, R. L., Abetz, L., & Vensel, B. (2003). The relationship between habitual patient-reported symptoms and clinical signs among patients with dry eye of varying severity. Invest Ophthalmol Vis Sci, 44(10), 4753-4761.

nei-vfq-25 visual-function-index impact-vision-impairment low-vision-quality-of-life


OFDM

Orthogonal Frequency Division Multiplexing

OFDM is a multicarrier modulation technique that divides a wideband channel into many narrowband orthogonal subcarriers. Introduced by Weinstein and Ebert in 1971, it exploits the duality between time and frequency domains to efficiently use spectrum while mitigating intersymbol interference in frequency-selective channels. OFDM is now the standard for high-speed wireless systems including WiFi, cellular LTE, and digital broadcasting.

Weinstein, S. B., & Ebert, P. M. (1971). Data transmission by frequency-division multiplexing using the discrete Fourier transform. IEEE Transactions on Communication Technology, 19(5), 628-634.

Alves, H., Nouri, M., & Latva-aho, M. (2015). Performance analysis of orthogonal frequency division multiplexing for wireless networks. IEEE Access, 3, 1627-1640.

mimo alamouti-code zf-mmse-equalization turbo-code shannon-capacity


The OHIP-14 is a 14-item, validated instrument measuring the impact of oral conditions on quality of life and functional well-being. Developed by Slade in 1997, it is a shortened form of the original 49-item OHIP and has become the gold standard for assessing oral health-related quality of life in clinical research and practice. It captures patient-centred outcomes across functional, psychological, and social dimensions.

Slade, G. D. (1997). Derivation and validation of a short-form oral health impact profile. Community Dentistry and Oral Epidemiology, 25(4), 284-290.

xerostomia-inventory dental-anxiety-modified-scale child-oral-health-qol oral-impacts-daily-performance


Okumura-Hata Model

The Okumura-Hata model is an empirical propagation model for predicting path loss in mobile radio systems. Developed by Okumura (1968) and mathematically formalized by Hata (1980), it is one of the most widely used models for cellular network planning. The model predicts median path loss as a function of frequency, distance, and antenna heights, with environment-specific correction factors. Despite its age, the Okumura-Hata model remains a standard in 2G/3G planning and is often used as a baseline for more sophisticated models.

Okumura, Y., Ohmori, E., Kawano, T., & Fukuda, K. (1968). Field strength and its variability in VHF and UHF land mobile radio service. Review of the Electrical Communication Laboratory, 16(9-10), 825-873.

Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, VT-29(3), 317-325.

ray-tracing-propagation ofdm mimo shannon-capacity


OLAP Cube Design

Online Analytical Processing Cube Design

E. F. Codd and colleagues (Arbor Software)

OLAP (Online Analytical Processing) cube design is the practice of structuring multidimensional data for interactive analysis. Formalized by Codd and colleagues in 1993, OLAP cubes organize facts (measurements) along multiple dimensions (attributes) enabling rapid pivoting, drilling, and aggregation for business analysis.

Codd, E. F., Codd, S. B., & Salley, C. T. (1993). Providing OLAP to user-analysts: An IT mandate. Arbor Software.

Pedersen, T. B., Jensen, C. S., & Dyreson, C. E. (1999). A foundation for capturing the semantics of temporal multidimensional databases. Proceedings of the 5th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

Kimball, R. (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. New York: John Wiley & Sons.

data-warehousing dimensional-modeling aggregation-strategies star-schema fact-tables


Oldenburg Burnout Inventory

Evangelia Demerouti, Arnold B. Bakker, Friedhelm Nachreiner, Wilmar B. Schaufeli

The Oldenburg Burnout Inventory (OLBI) is a brief, two-factor assessment of occupational burnout developed by Demerouti and colleagues in 2003. The instrument measures exhaustion (physical, emotional, cognitive) and disengagement (cynicism, reduced motivation) in working populations. It is grounded in the Job Demands-Resources (JD-R) theory and is widely used in European occupational health research and practice.

Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2003). The job demands-resources model of burnout. Journal of Vocational Behavior, 63(1), 141-145.

copenhagen-burnout-inventory effort-reward-imbalance-scale areas-of-worklife-scale recovery-experience-questionnaire maslach-burnout-inventory


OLS Regression

ordinary least squares
classical linear regression
linear regression
en küçük kareler regresyonu

Wooldridge (textbook treatment); classical least squares

Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).

Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning.

ridge-regression lasso-regression quantile-regression logistic-regression panel-fixed-effects


McDonald's Omega

McDonald's Omega Reliability Coefficient

McDonald's Omega (ω) — Güvenilirlik Katsayısı

McDonald's omega is a factor-analysis-based reliability coefficient introduced by Roderick P. McDonald (1999) that quantifies the internal consistency of a composite score without requiring the restrictive assumption that all items contribute equally to the latent factor. It yields two complementary indices: ω_total, which captures overall reliability of the sum score, and ω_hierarchical (ωh), which reports how much of the composite's variance is explained specifically by a single general factor.

Reliability coefficient / latent variable model

ω_total (overall reliability) and ω_hierarchical (proportion of composite variance attributable to the general factor)

Congeneric (unequal factor loadings allowed)

McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates.

Dunn, T. J., Baguley, T. & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412.

cronbach-alpha confirmatory-factor-analysis exploratory-factor-analysis cfa icc-intraclass-correlation rasch-model


One-class SVM

One-Class Support Vector Machine (Novelty and Anomaly Detection)

OCSVM
one-class support vector machine
novelty SVM
unsupervised SVM

Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.

One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.

Anomaly / novelty detection (unsupervised)

Continuous numeric features; high-dimensional vectors

Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.

Tax, D. M. J., & Duin, R. P. W. (2004). Support vector data description. Machine Learning, 54(1), 45–66.

gaussian-mixture-model isolation-forest autoencoder-anomaly-detection support-vector-machine k-nearest-neighbors local-outlier-factor


One-sample t-test

single-sample t-test
one-group t-test
one-sample t
Student one-sample t-test

The one-sample t-test is a parametric hypothesis test that determines whether the mean of a single sample differs significantly from a known or hypothesized population value. Derived from Student's (Gosset's) 1908 t-distribution, it assumes continuous, approximately normally distributed data and is one of the most fundamental tests in applied statistics.

Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.

independent-samples-t-test paired-samples-t-test wilcoxon-signed-rank-test z-test one-way-anova mann-whitney-u-test


One-way ANOVA

one-factor ANOVA
single-factor ANOVA
analysis of variance
tek yönlü ANOVA

One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.

Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.

independent-t-test kruskal-wallis two-way-anova welch-anova tukey-hsd


Online Active learning

Online Active Learning (Streaming Active Learning)

streaming active learning
online query-by-committee
sequential active learning
incremental active learning

Cesa-Bianchi, N. and others (multiple contributors)

Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled data is expensive and examples arrive sequentially rather than all at once.

Hybrid learning paradigm (online + active)

Streaming labeled and unlabeled instances

Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230.

Sculley, D. (2007). Online active learning methods for fast label-efficient spam filtering. Proceedings of the Fourth Conference on Email and Anti-Spam (CEAS 2007).

online-learning active-learning semi-supervised-learning online-logistic-regression online-random-forest few-shot-learning


Online Association Rules

Online (Incremental) Association Rule Mining

Incremental association rule mining
Streaming association rules
Online ARM
Incremental ARM

Online association rule mining discovers if-then patterns (e.g., buying bread implies buying butter) from transactional data that arrives incrementally or as a stream, updating existing rules and item counts without re-scanning the entire historical database each time new records arrive.

Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. (1996). Maintenance of discovered association rules in large databases: an incremental updating technique. In Proceedings of the 12th International Conference on Data Engineering (ICDE 1996), pp. 106–114. IEEE.

association-rules apriori-algorithm online-learning semi-supervised-association-rules fp-growth streaming-clustering


Online Autoencoder Anomaly Detection

Online Autoencoder Anomaly Detection (Incremental Autoencoder for Streaming Anomaly Detection)

incremental autoencoder anomaly detection

Various (online/incremental deep learning community)

Online Autoencoder Anomaly Detection trains an autoencoder incrementally on a continuous data stream, flagging observations whose reconstruction error exceeds an adaptive threshold as anomalies. This approach combines the representational power of deep autoencoders with the incremental update capability of online learning, making it suitable for real-time or high-volume streaming scenarios where batch retraining is impractical.

Streaming or sequentially arriving numeric/multivariate data

An, J. & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. SNU Data Mining Center, 2015-2.

Zenati, H., Foo, C. S., Lecouat, B., Manek, G. & Chandrasekhar, V. R. (2018). Efficient GAN-Based Anomaly Detection. ICLR 2018 Workshop.


Online Bagging

Online Bagging (Incremental Bootstrap Aggregating)

incremental bagging
streaming bagging
online bootstrap aggregating
OzaBag

Online Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset.

Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112.

Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). MOA: Massive Online Analysis. Journal of Machine Learning Research, 11, 1601–1604.

random-forest bagging online-boosting hoeffding-tree adaptive-random-forest gradient-boosting


Online Boosting

Online Boosting (Streaming Ensemble Boosting)

streaming boosting
incremental boosting
online AdaBoost
online ensemble boosting

Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.

Streaming or sequentially arriving labeled tabular data

Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.

boosting online-learning online-random-forest online-bagging semi-supervised-boosting gradient-boosting


Online cluster sampling

internet cluster sampling
web cluster sampling
digital cluster sampling

Late 1990s–2000s (as internet surveys became prevalent)

Adapted from cluster sampling (Mahalanobis, Hansen & Hurwitz, 1940s) to online survey contexts

Online cluster sampling applies the classic cluster sampling logic to internet-based research: naturally occurring digital groups — such as online communities, email lists, forum memberships, or institutional user registries — serve as clusters, and selected clusters are surveyed in full or partially via web-based instruments. It offers a practical route to probability-based online samples when no complete list of individuals exists but lists of digital groups are accessible.

Quantitative or mixed-mode survey data collected via internet

Couper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.

cluster-sampling online-stratified-sampling online-systematic-sampling multistage-sampling online-simple-random-sampling online-quota-sampling


Online convenience sampling

web-based convenience sampling
internet convenience sampling
digital convenience sampling
online accidental sampling

Evolved from convenience sampling; internet applications documented from mid-1990s onward

Online convenience sampling is a non-probability technique in which participants are recruited via internet channels — survey platforms, social media, email lists, or research panels — simply because they are accessible and willing to respond. It is the online analogue of traditional convenience sampling, offering fast, low-cost data collection at the expense of known representativeness. It is among the most widely used approaches in social, behavioral, and health sciences research conducted through web-based surveys.

Survey or questionnaire responses collected via online platforms, social media, or email

Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004). Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires. American Psychologist, 59(2), 93–104.

convenience-sampling online-quota-sampling online-snowball-sampling purposive-sampling quota-sampling snowball-sampling


Online DBSCAN

Online Density-Based Spatial Clustering of Applications with Noise

Incremental DBSCAN
Streaming DBSCAN
Online density-based clustering
iDBSCAN

Online DBSCAN extends the classic density-based clustering algorithm to handle continuously arriving data points without re-clustering the entire dataset from scratch. Each new observation is integrated into the existing cluster structure by local neighborhood queries, making it practical for streaming and data-warehousing scenarios where data grows incrementally.

Continuous numerical, streaming or batch-growing tabular data

Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X. (1998). Incremental Clustering for Mining in a Data Warehousing Environment. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), pp. 323–333.

Cao, F., Ester, M., Qian, W., & Zhou, A. (2006). Density-Based Clustering over an Evolving Data Stream with Noise. In Proceedings of the 2006 SIAM International Conference on Data Mining (SDM), pp. 328–339.

dbscan hdbscan online-k-means online-gaussian-mixture-model online-learning gaussian-mixture-model


Online Decision Tree

Online Decision Tree (Incremental / Streaming Decision Tree Learning)

Hoeffding Tree
VFDT
Very Fast Decision Tree
incremental decision tree

An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time classification on potentially infinite data streams.

Streaming or sequentially arriving tabular data

Domingos, P., & Hulten, G. (2000). Mining very fast data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 71–80). ACM.

Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 97–106). ACM.

decision-tree online-learning online-random-forest semi-supervised-decision-tree online-gradient-boosting online-naive-bayes


Online Delphi Technique

e-Delphi
electronic Delphi
web-based Delphi
internet Delphi

Original Delphi: 1950s–1960s; Online variant: mid-1990s onwards

Olaf Helmer, Norman Dalkey, Nicholas Rescher (RAND Corporation); online adaptation emerged in the 1990s–2000s

The Online Delphi Technique (e-Delphi) is an iterative, web-mediated consensus method in which a geographically dispersed panel of experts responds to successive rounds of structured questionnaires distributed and collected via email or a web platform. Anonymous feedback and controlled statistical summaries are fed back between rounds, guiding panellists toward convergence on priorities, predictions, or recommendations without the social pressures of face-to-face group discussion.

Iterative expert consensus method (online)

Structured questionnaire responses (quantitative ratings + qualitative comments) from expert panels

Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.

Donohoe, H., Stellefson, M., & Tennant, B. (2012). Advantages and limitations of the e-Delphi technique: Implications for health education researchers. American Journal of Health Education, 43(1), 38–46.

delphi-technique online-survey online-focus-group online-structured-interview nominal-group-technique expert-panel


Online Deviant Case Sampling

online extreme case sampling
internet-based deviant case sampling
online outlier sampling
web-based atypical case sampling

1990s–2000s (deviant case strategy); online variant ~2000s–2010s

Patton, M. Q. (deviant case strategy); online adaptation via web-based qualitative research practice

Online deviant case sampling is a purposive qualitative sampling strategy in which the researcher deliberately seeks out and recruits participants who represent extreme, unusual, or outlier instances of the phenomenon under study, using online channels such as forums, social media, specialist communities, or digital registries. It inherits the logic of Patton's deviant (extreme) case sampling and applies it in internet-mediated research contexts where rare or hard-to-reach atypical cases can be located more efficiently than through face-to-face methods.

Purposive qualitative sampling strategy (online variant)

Qualitative data: interviews, focus groups, documents, digital trace data recruited via online channels

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Chapter 5: Purposeful Sampling, deviant/extreme case strategy, pp. 231-234]

Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219-245.

deviant-case-sampling online-purposive-sampling online-snowball-sampling maximum-variation-sampling online-convenience-sampling typical-case-sampling


Online Diary Method

e-diary method
digital diary study
web-based diary method
online daily diary study

2000s (online adaptation); diary method roots in social research circa 1970s–1990s

Adaptation of the traditional diary method; Bolger, Davis & Rafaeli (2003) systematized the daily diary design; online delivery emerged through web survey tools in the early 2000s

The online diary method is a longitudinal data collection technique in which participants record their thoughts, experiences, behaviors, or events in structured or semi-structured entries submitted via digital platforms — such as web forms, email, or dedicated apps — at regular or event-contingent intervals. It combines the ecological validity of traditional diary research with the logistical advantages of remote, automated data collection.

Longitudinal self-report data collection technique

Self-reported text, ratings, or structured entries submitted via web forms, email, or online platforms

Alaszewski, A. (2006). Using Diaries for Social Research. Sage Publications.

Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.

diary-method experience-sampling-method mobile-experience-sampling online-participant-observation longitudinal-diary-method research-diary


Online Disinhibition Scale

Online Disinhibition Effect Scale (ODES)

The Online Disinhibition Effect Scale measures the tendency for individuals to express themselves less inhibitedly online compared to face-to-face contexts, exhibiting increased aggression, profanity, emotional expression, and self-disclosure in digital environments. Developed by John Suler in 2004, this construct explains a core phenomenon of internet behavior: the reduced social constraint and increased behavioral extremity that characterize many online interactions.

Suler, J. (2004). The online disinhibition effect. CyberPsychology & Behavior, 7(3), 321–326.

social-media-disorder-scale smartphone-addiction-scale-short technoference-scale fear-of-missing-out-scale


Online Document Collection

digital document collection
web document gathering
online archival data collection
digital records collection

Adapted from traditional document analysis; digital form emerged with widespread internet adoption

Online document collection is the systematic process of identifying, retrieving, and compiling digital documents — including web pages, institutional publications, social media posts, policy documents, and digital archives — as primary or supplementary research data. It extends classical document analysis into internet-mediated environments, enabling researchers to access large, geographically dispersed corpora without fieldwork travel or physical archive access.

Qualitative / mixed-methods data collection technique

Digital text documents, web pages, PDFs, social media posts, institutional records, digital archives

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.

Prior, L. (2003). Using Documents in Social Research. Sage Publications.

document-collection web-scraping api-based-data-collection content-analysis archival-research online-non-participant-observation


Online Federated Learning

Online Federated Learning (Sequential Distributed Learning without Centralised Data)

OFL
federated online learning
streaming federated learning
real-time federated learning

McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021

Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time.

Sequential / streaming tabular, image, or text data distributed across edge devices

Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys).

McMahan, B., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 54, 1273–1282.

federated-learning online-learning stochastic-gradient-descent differential-privacy transfer-learning continual-learning


Online Few-shot Learning

Online Few-shot Learning (Streaming Meta-Learning from Scarce Labels)

online meta-learning
streaming few-shot learning
continual few-shot learning
incremental few-shot learning

Finn, C. et al. (online meta-learning formalization)

Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset.

Sequential labeled episodes with very few examples per class

Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019). Online Meta-Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1920–1930.

Javed, K., & White, M. (2019). Meta-Learning Representations for Continual Learning. Advances in Neural Information Processing Systems (NeurIPS), 32.

few-shot-learning online-learning semi-supervised-learning transfer-learning meta-learning continual-learning


Online Field Notes

Online Field Notes in Digital and Virtual Research

digital field notes
virtual field notes
e-field notes
netnographic field notes

1990s–2000s (digital extension of classical field notes practice)

Extended from ethnographic field note tradition (Emerson, Fretz & Shaw); digital adaptation via Kozinets and virtual ethnography scholars

Online field notes are structured, researcher-authored records of observations made within digital environments — social media platforms, online communities, virtual worlds, forums, and video-mediated spaces. Adapted from the classical ethnographic field note tradition, they capture not only what is observed but how the researcher interprets and situates those observations in real time, forming a primary data source for virtual ethnography and netnography.

Textual, visual, and multimedia observations from online environments

Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes (2nd ed.). University of Chicago Press.

Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.

field-notes netnography online-participant-observation online-non-participant-observation online-research-diary virtual-ethnography


Online Focus Group

virtual focus group
internet focus group
OFG
web-based focus group

1946 (focus groups); online variant ~1990s–2000s

Robert Merton & Patricia Kendall (focus group origins); online adaptation by scholars including Stewart & Shamdasani in the 1990s

An online focus group is a moderated group discussion conducted via internet-based platforms — video conferencing, text chat, or asynchronous forums — to explore shared perceptions, attitudes, and experiences on a defined topic. It inherits the group-interaction dynamics of the traditional focus group while removing geographic barriers and enabling data collection from dispersed or hard-to-reach populations.

Text, audio, or video from group discussion

Stewart, D. W., & Shamdasani, P. N. (2017). Online Focus Groups. Journal of Advertising, 46(1), 48–60.

Morgan, D. L. (1997). Focus Groups as Qualitative Research (2nd ed.). Sage Publications.

focus-group online-semi-structured-interview online-structured-interview online-in-depth-interview online-participant-observation online-survey


Online FP-growth

Online Frequent Pattern Growth (Incremental FP-tree Mining)

Incremental FP-growth
Online FP-tree
stream FP-growth
OFP-growth

Online FP-growth is an incremental extension of the FP-growth algorithm that mines frequent itemsets from continuously arriving transaction streams without rebuilding the full FP-tree from scratch. It updates an existing compact tree structure as new transactions arrive, making it suitable for real-time and high-velocity data environments where a full database scan is impractical.

Incremental frequent pattern mining algorithm

Cheung, W. & Zaiane, O. R. (2004). Incremental Mining of Frequent Patterns Without Candidate Generation or Support Thr esholding. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), pp. 111–118. IEEE.

Lee, G., Yun, U. & Ryu, K. H. (2014). Sliding window based weighted maximal frequent pattern mining over data streams. Expert Systems with Applications, 41(2), 694–708.

fp-growth apriori association-rule-learning stream-mining incremental-learning sequential-pattern-mining


Online Gaussian Mixture Model

Online Gaussian Mixture Model (Incremental / Streaming GMM)

Online GMM
Incremental GMM
Streaming Gaussian Mixture Model
Sequential GMM

Cappé, O. & Moulines, E. (online EM formulation)

Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.

Probabilistic clustering / density estimation (incremental)

Continuous numeric (streaming or large-batch)

Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613.

Sato, M. & Ishii, S. (2000). On-line EM algorithm for the normalized Gaussian network. Neural Computation, 12(2), 407–432.

gaussian-mixture-model online-learning online-k-means semi-supervised-gaussian-mixture-model bayesian-gaussian-mixture-model k-means


Online Gaussian Process

Online Gaussian Process Regression and Classification

OGP
sparse online GP
sequential Gaussian process
incremental Gaussian process

Online Gaussian Process (OGP) extends the Bayesian nonparametric GP framework to streaming or sequentially arriving data. Instead of recomputing the full GP posterior from scratch as each observation arrives, OGP maintains a compact summary — a sparse set of inducing points — and updates it incrementally, making probabilistic regression and classification feasible in real-time and large-scale settings.

Bayesian nonparametric model (sequential/online)

Continuous inputs; continuous or binary outputs

Csató, L. & Opper, M. (2002). Sparse on-line Gaussian processes. Neural Computation, 14(3), 641–668.

Engel, Y., Mannor, S. & Meir, R. (2004). The kernel recursive least-squares algorithm. IEEE Transactions on Signal Processing, 52(8), 2275–2285.

gaussian-process-regression bayesian-linear-regression kernel-ridge-regression variational-inference stochastic-gradient-descent support-vector-machine


Online Gradient Boosting

Online Gradient Boosting (Streaming Gradient Boosted Ensembles)

OGB
streaming gradient boosting
incremental gradient boosting
online boosting with gradient descent

Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.

Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible.

Online ensemble (sequential boosting on streaming data)

Continuous, categorical, or mixed tabular features arriving in a stream

Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216.

Beygelzimer, A., Hazan, E., Langford, J. & Zheng, T. (2015). Online-to-Batch Conversions and Applications. Advances in Neural Information Processing Systems (NeurIPS), 28.

gradient-boosting online-learning xgboost boosting online-random-forest semi-supervised-gradient-boosting


Online HDBSCAN

Online Hierarchical Density-Based Spatial Clustering of Applications with Noise

incremental HDBSCAN
streaming HDBSCAN
online hierarchical density clustering
dynamic HDBSCAN

Campello, R. J. G. B. et al. (base); incremental extensions by Hassani, M. et al.

Online HDBSCAN extends the HDBSCAN hierarchical density-based clustering algorithm to incrementally process streaming or sequentially arriving data. Rather than rebuilding the full hierarchy from scratch with each new observation, it maintains and locally updates the mutual reachability graph, minimum spanning tree, condensed cluster tree, and stability-based cluster extraction, enabling continuous density-based clustering without full-dataset reprocessing.

Incremental hierarchical density-based clustering

Streaming or incrementally arriving continuous feature data

Hassani, M., Seidl, T. (2017). Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam Journal of Computer Science, 4(3), 171–183.

Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), Article 5.

hdbscan dbscan online-learning ensemble-hdbscan robust-hdbscan spectral-clustering


Online Isolation Forest

Online Isolation Forest (Streaming Anomaly Detection with Isolation Trees)

streaming isolation forest
incremental isolation forest
online iForest
adaptive isolation forest

Tan, S. C.; Ting, K. M.; Liu, T. F. (streaming variant); original iForest by Liu et al.

Online Isolation Forest extends the Isolation Forest anomaly-detection algorithm to streaming or continuously arriving data. Instead of rebuilding isolation trees from scratch when new observations arrive, the forest is updated incrementally so that anomaly scores remain current without reprocessing the entire history. This makes it practical for real-time monitoring, fraud detection, and sensor-data surveillance where data volumes grow indefinitely.

Streaming anomaly detection (online ensemble)

Continuous or mixed tabular streaming data

Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.

Tan, S. C., Ting, K. M., & Liu, T. F. (2011). Fast Anomaly Detection for Streaming Data. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pp. 1511–1516.

isolation-forest online-learning one-class-svm autoencoder-anomaly-detection online-random-forest semi-supervised-isolation-forest


Online K-means

Online K-means Clustering (Sequential / Streaming K-means)

sequential k-means
streaming k-means
incremental k-means
online clustering

1967 (online update rule); 2010 (mini-batch variant)

MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)

Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical.

Continuous numeric features; streaming or batch tabular data

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press.

Sculley, D. (2010). Web-scale k-means clustering. In Proceedings of the 19th International Conference on World Wide Web (WWW 2010), pp. 1177–1178. ACM.

k-means-clustering mini-batch-k-means dbscan gaussian-mixture-model hierarchical-clustering self-organizing-map


Online K-nearest neighbors

Online K-Nearest Neighbors (Incremental KNN for Data Streams)

Online KNN
Incremental KNN
Streaming KNN
KNN with concept drift adaptation

2010s (formalized in streaming-learning literature)

Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016)

Online K-Nearest Neighbors (Online KNN) adapts the classic KNN algorithm to a data-stream setting where observations arrive sequentially and the model must update incrementally without full retraining. Instead of storing all historical instances, it maintains a bounded sliding window or adaptive memory, using the most recent and most representative examples to classify or predict each incoming point by proximity.

Continuous and mixed feature vectors arriving as a stream

Losing, V., Hammer, B., & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), pp. 291–300. IEEE.

Gama, J. (2010). Knowledge Discovery from Data Streams. CRC Press / Chapman & Hall.

k-nearest-neighbors online-learning semi-supervised-k-nearest-neighbors online-decision-tree online-random-forest online-naive-bayes


Online Learning

Online Learning (Sequential / Incremental Machine Learning)

incremental learning
sequential learning
streaming learning
online machine learning

Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)

Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.

Learning paradigm (sequential model update)

Streaming or sequentially arriving labeled/unlabeled data

Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.

Cesa-Bianchi, N. & Lugosi, G. (2006). Prediction, Learning, and Games. Cambridge University Press.

semi-supervised-learning transfer-learning few-shot-learning active-learning self-supervised-learning federated-learning


Online LightGBM

Online / Incremental LightGBM (Light Gradient-Boosting Machine with Streaming Updates)

Incremental LightGBM
LightGBM incremental training
streaming LightGBM
continual LightGBM

2017 (LightGBM); 2000s (online boosting)

Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)

Online LightGBM applies the Light Gradient-Boosting Machine framework incrementally: instead of requiring all training data at once, the model is updated in mini-batches or data chunks as they arrive. This allows LightGBM's efficient histogram-based boosting to be deployed in streaming, continual-learning, and data-expansion scenarios without retraining from scratch.

Online ensemble (incremental gradient boosting)

Tabular, streaming / sequential numeric and categorical features

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30.

Bifet, A., & Gavalda, R. (2009). Adaptive Learning from Evolving Data Streams. Advances in Intelligent Data Analysis VIII. Lecture Notes in Computer Science, vol 5772. Springer.

online-learning online-gradient-boosting online-xgboost lightgbm gradient-boosting online-random-forest


Online Linear Regression

Online Linear Regression (Incremental Least-Squares)

incremental linear regression
streaming linear regression
recursive least squares regression
stochastic gradient descent regression

Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)

Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data-generating process can shift over time.

Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.

Haykin, S. (2002). Adaptive Filter Theory (4th ed.). Prentice Hall.

linear-regression-ml regularized-linear-regression online-learning stochastic-gradient-descent ridge-regression online-logistic-regression


Online Logistic Regression

Online Logistic Regression (Incremental Stochastic Gradient Descent)

incremental logistic regression
streaming logistic regression
SGD logistic classifier
online binary classifier

1960s (perceptron); formalized for logistic loss ~2000s

Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.

Online Logistic Regression fits a logistic classifier one sample (or mini-batch) at a time via stochastic gradient descent, updating model weights as each observation arrives rather than waiting to see the full dataset. This makes it the standard choice for high-volume, streaming, or memory-constrained binary classification problems where batch training is infeasible.

Tabular (continuous and binary features); streaming or large-scale batch data

Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.

logistic-regression-ml online-learning semi-supervised-logistic-regression regularized-logistic-regression support-vector-machine online-linear-regression


Online Metric Learning

Online Metric Learning (Incremental Distance Metric Learning from Streaming Data)

OML
incremental metric learning
streaming metric learning
online distance metric learning

Shalev-Shwartz, S.; Singer, Y.; and others

Online Metric Learning adapts a Mahalanobis distance metric incrementally as new labeled examples or pairwise constraints arrive one at a time, without storing the full dataset. It merges the efficiency of online learning with the representational power of metric learning, making it suitable for streaming, large-scale, or continually changing environments where retraining from scratch is impractical.

Online / incremental learning of distance metrics

Labeled pairwise or triplet constraints from streaming observations

Shalev-Shwartz, S., Singer, Y., & Ng, A. Y. (2004). Online and batch learning of pseudo-metrics. Proceedings of the 21st International Conference on Machine Learning (ICML 2004), pp. 94. ACM.

Jin, R., Wang, S., & Zhou, Y. (2009). Regularized distance metric learning: Theory and algorithm. Advances in Neural Information Processing Systems (NIPS 2009), 22, 862–870.

metric-learning large-margin-nearest-neighbor siamese-network online-learning k-nearest-neighbors contrastive-learning


Online Mobile Experience Sampling

Online Mobile Experience Sampling Method

Ecological Momentary Assessment via Mobile

1983 (original ESM); smartphone/online variant widely adopted ~2007–2010

Online Mobile Experience Sampling (Online ESM) is a data collection technique that uses internet-connected smartphones or tablets to prompt participants multiple times per day and record their thoughts, feelings, behaviors, and context in the moment they occur. By gathering data in real time across daily life rather than retrospectively in a lab, it dramatically reduces recall bias and captures the natural variation of psychological and behavioral states as they unfold.

Intensive longitudinal data collection technique

Repeated self-report data (ratings, text, photos) collected via internet-connected mobile devices

Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536.

Larson, R., & Csikszentmihalyi, M. (2014). The Experience Sampling Method. In M. R. Leary & J. P. Tangney (Eds.), Handbook of Research Methods in Social and Personality Psychology (2nd ed.). Cambridge University Press.

mobile-experience-sampling experience-sampling-method ecological-momentary-assessment online-survey diary-method mobile-survey


Online Naive Bayes

Online (Incremental) Naive Bayes Classifier

Incremental Naive Bayes
Streaming Naive Bayes
Naive Bayes with partial_fit
Online NB

Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)

Online Naive Bayes is an incremental adaptation of the classical Naive Bayes classifier that updates its class-conditional statistics one observation (or one mini-batch) at a time, making it well suited to data streams, very large datasets that cannot be held in memory, and settings where the model must adapt continuously as new labeled examples arrive.

Categorical, count, or continuous features; streaming or sequentially arriving data

Domingos, P. & Hulten, G. (2000). Mining high-speed data streams. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71–80. ACM.

naive-bayes online-learning semi-supervised-naive-bayes logistic-regression-ml online-logistic-regression online-decision-tree


Online Non-participant Observation

digital non-participant observation
passive online observation
covert online observation
online unobtrusive observation

Christine Hine; Robert Kozinets (digital/online adaptation)

Online non-participant observation is a qualitative data collection technique in which the researcher watches and records naturally occurring behaviour in digital settings — forums, social media platforms, chat groups, comment sections, or online communities — without joining, interacting with, or disclosing their presence to participants. The approach transplants the classical non-participant observation tradition into internet-mediated spaces, enabling study of authentic discourse and interaction as it unfolds organically.

Text, images, video, interaction logs from online communities and platforms

Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.

Hine, C. (2000). Virtual Ethnography. Sage.

non-participant-observation online-participant-observation online-focus-group netnography web-scraping online-document-collection


Online One-class SVM

Online OC-SVM
Incremental One-Class SVM
Online SVDD
Sequential One-Class SVM

2006 (incremental/online variant); 1999 (base method)

Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM)

Online One-Class SVM is an incremental extension of the classical One-Class Support Vector Machine that updates its decision boundary as new data arrive one sample at a time, making it suitable for streaming environments and real-time anomaly or novelty detection without retraining from scratch.

Online anomaly detection / novelty detection

Continuous, streaming or sequentially arriving feature vectors

Laskov, P., Gehl, C., Krueger, S., & Muller, K.-R. (2006). Incremental support vector learning: Analysis, implementation and applications. Journal of Machine Learning Research, 7, 1909–1936.

Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NIPS), 12, 582–588.

one-class-svm support-vector-machine isolation-forest autoencoder local-outlier-factor incremental-learning


Online Participant Observation

virtual participant observation
digital ethnographic observation
cyber participant observation
internet participant observation

Christine Hine (virtual ethnography); Robert Kozinets (netnography)

Online participant observation is a qualitative data collection method in which the researcher enters a digital community or online environment — forums, social media groups, multiplayer games, virtual workplaces — both as a participant and as an observer, systematically documenting social interactions, practices, and meanings as they naturally unfold in the digital space.

Online interactions, text posts, digital artifacts, multimedia

Hine, C. (2000). Virtual Ethnography. SAGE Publications.

Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. SAGE Publications.

participant-observation non-participant-observation online-non-participant-observation online-focus-group netnography virtual-ethnography


Online Purposive Sampling

internet-based purposive sampling
web purposive sampling
online criterion-based sampling
digital purposive sampling

1990s–2000s (with growth of internet-based research)

Adaptation of purposive sampling (Patton, 1987) to online/digital research contexts

Online purposive sampling applies the logic of criterion-based participant selection to digital recruitment channels — including social media platforms, online communities, email lists, and research recruitment websites. Researchers intentionally seek individuals who possess the characteristics, experiences, or expertise directly relevant to the research question, using internet-based tools to locate and screen them. The method preserves the defining feature of purposive sampling — deliberate selection based on fitness for purpose — while leveraging the reach and accessibility of online environments.

Qualitative or quantitative data collected via online channels (interviews, surveys, forums, social media)

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.

Andrews, D., Nonnecke, B., & Preece, J. (2003). Electronic survey methodology: A case study in reaching hard-to-involve Internet users. International Journal of Human-Computer Interaction, 16(2), 185-210.

purposive-sampling snowball-sampling online-snowball-sampling theoretical-sampling maximum-variation-sampling convenience-sampling


Online Random Forest

Online Random Forest (Incremental Ensemble of Decision Trees)

ORF
streaming random forest
incremental random forest
adaptive random forest

Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time.

Incremental ensemble (streaming decision trees)

Streaming or sequentially arriving tabular data

Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE.

Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfharinger, B., Holmes, G., & Abdessalem, T. (2017). Adaptive random forests for evolving data stream classification. Machine Learning, 106(9), 1469–1495.

random-forest online-learning online-decision-tree online-gradient-boosting online-bagging semi-supervised-random-forest


Online Research Diary

digital research diary
e-diary research
online reflective journal
web-based research diary

Late 1990s–2000s (digital adaptation of diary methods dating to early 20th century social research)

Adapted from traditional diary methods; online variant emerged with widespread internet adoption (late 1990s–2000s)

The online research diary method is a data collection technique in which participants document their experiences, thoughts, or behaviours in structured or open-ended digital diary entries over a defined period. Delivered via email, web forms, blogging platforms, or dedicated apps, it captures temporally proximate, naturalistic data that retrospective interviews cannot provide. It is widely used in health research, education, psychology, and social sciences.

Qualitative / mixed-methods data collection

Text entries, audio/video uploads, images, structured logs submitted via digital platforms

Alaszewski, A. (2006). Using Diaries for Social Research. Sage Publications.

Hyers, L. L. (2018). Diary Methods. In M. C. Blanco & A. J. Breckenridge (Eds.), Understanding Research Methods in Psychology. Sage.

research-diary diary-method online-participant-observation experience-sampling-method online-semi-structured-interview field-notes


Online Self-supervised Learning

Online Self-supervised Learning (Continual Self-supervised Representation Learning from Streaming Data)

online SSL
continual self-supervised learning
streaming self-supervised learning
incremental self-supervised learning

Multiple contributors (Gidaris, Fini et al., among others)

Online Self-supervised Learning (online SSL) trains neural networks on unlabeled data that arrives sequentially or in streams, using automatically generated supervisory signals (pretext tasks) instead of human labels. By updating the model continuously as new data flows in, it enables perpetually evolving representations without storing the full dataset — critical for real-time systems, edge devices, and privacy-constrained settings.

Online unsupervised representation learning

Unlabeled streaming or sequentially arriving data (images, text, time-series)

Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2021). OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6830–6840.

Fini, E., Da Costa, V. G. T., Alameda-Pineda, X., Ricci, E., Alahari, K., & Mairal, J. (2022). Self-Supervised Models are Continual Learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9621–9630.

self-supervised-learning contrastive-learning continual-learning online-learning transfer-learning representation-learning


Online Semi-structured Interview

Late 1990s–2000s (systematic treatment by ~2010)

Adapted from face-to-face semi-structured interviewing; online variant emerged with internet adoption in research (Salmons, Mann, Stewart)

An online semi-structured interview is a qualitative data collection technique in which a researcher conducts a guided but flexible conversation with a participant over a digital medium — video call, telephone, chat, or email — using a prepared interview guide with open-ended questions while remaining free to probe, reorder, or add follow-up questions as the dialogue unfolds. It combines the accessibility of remote communication with the depth and adaptability of semi-structured inquiry.

Text transcripts, audio/video recordings from synchronous or asynchronous online exchanges

Brinkmann, S., & Kvale, S. (2015). InterViews: Learning the Craft of Qualitative Research Interviewing (3rd ed.). Sage.

Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage.

semi-structured-interview online-in-depth-interview online-structured-interview online-focus-group online-survey face-to-face-semi-structured-interview


Online Semi-supervised learning

Online Semi-supervised Learning (Stream-based Learning with Partial Labels)

stream-based semi-supervised learning
incremental semi-supervised learning
online SSL
semi-supervised online learning

Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)

Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-time classification of web content, sensor readings, or social media posts.

Incremental / stream-based semi-supervised learning framework

Sequential/streaming data with mixed labeled and unlabeled observations

Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 393–407. Springer.

semi-supervised-learning online-learning self-supervised-learning active-learning transfer-learning label-propagation


Online Sensor Data Collection

Online (Networked) Sensor Data Collection

networked sensor data collection
IoT data collection
remote sensor monitoring
wireless sensor data acquisition

Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000)

Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000)

Online sensor data collection is a systematic technique for gathering continuous or event-triggered measurements from physical sensors that transmit readings in real time over a network — the internet, a local wireless network, or a dedicated IoT protocol. It is used widely in environmental monitoring, health informatics, smart-city research, industrial systems, and behavioral science to capture objective, high-frequency data without requiring manual recording by participants or observers.

Quantitative / mixed-mode data collection technique

Continuous or event-triggered numeric/binary readings transmitted over a network

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.

sensor-data-collection mobile-sensor-data-collection api-based-data-collection mobile-experience-sampling web-scraping longitudinal-sensor-data-collection


Online simple random sampling

web simple random sampling
internet SRS
digital random sampling
online SRS

Adapted from classical simple random sampling (Neyman, 1934) for web/digital survey contexts; operationalised by survey methodology researchers from the late 1990s onward

Online simple random sampling applies the logic of classical simple random sampling (SRS) to digital data collection: every member of a defined online population has an equal and independent probability of being selected, and the survey is administered via web platform, email link, or online panel. The approach combines the statistical rigour of probability sampling with the speed and cost advantages of internet-based survey delivery.

Couper, M. P. (2008). Designing Effective Web Surveys. Cambridge University Press.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.

simple-random-sampling stratified-sampling online-stratified-sampling systematic-sampling online-cluster-sampling quota-sampling


Iowa-Netherlands Social Comparison Orientation Scale

The Iowa-Netherlands Comparison Orientation Measure (INCOM) is an 11-item self-report scale that assesses individual differences in the tendency to engage in social comparison—comparing oneself to others on abilities, attributes, and outcomes. Developed by Gibbons and Buunk in 1999, it captures both upward comparison (to those perceived as superior) and downward comparison (to those perceived as inferior), providing a trait-like measure of this fundamental social motivation.

Gibbons, F. X., & Buunk, B. P. (1999). Individual differences in social comparison: Development of a scale of social comparison orientation. Journal of Personality and Social Psychology, 76(1), 129–142.

fear-of-missing-out-scale social-media-disorder-scale social-comparison-scale-online passive-social-media-use-scale


Online Social Support Scale

OSSS
Online Social Support
Internet Social Support

Joana Vilelas, Carla Tomás; Emma Nick, David Cole et al.

The Online Social Support Scale measures the perceived availability and quality of emotional, informational, and practical support received through digital channels—social media, online communities, forums, messaging apps, and digital platforms. Developed by Vilelas and Tomás (2011) for patients with chronic illness and refined by Nick and colleagues (2017), the scale recognizes that social support increasingly flows through digital networks, particularly for geographically dispersed, stigmatized, or medically complex populations who benefit from asynchronous, text-based support.

Vilelas, J. M., & Tomás, C. C. (2011). Internet social support: An instrument for studying virtual communities of patients with fibromyalgia. Computers, Informatics, Nursing, 29(10), 576–585.

Nick, E. A., Cole, D. A., Cho, S. J., & Smith, D. K. (2018). The online social support scale: Measure development and validation. Psychological Assessment, 30(9), 1127–1143.


Online Structured Interview

web-based structured interview
virtual structured interview
digital structured interview
e-interview (structured)

Late 1990s–2000s (with widespread adoption post-2010)

Emerged from structured interview methodology adapted for internet-mediated communication

An online structured interview applies the classical structured interview protocol — a fixed set of predetermined questions asked in a fixed order — via internet-mediated channels such as video conferencing, synchronous chat, or email. Every participant receives the exact same questions, enabling systematic comparison across respondents while eliminating geographic barriers. It combines the standardization benefits of face-to-face structured interviewing with the reach, cost efficiency, and scheduling flexibility of online data collection.

Quantitative/standardized data collection technique

Closed-ended and fixed-format verbal or typed responses

Salmons, J. (2015). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage Publications.

Couper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494.

structured-interview online-survey online-semi-structured-interview online-in-depth-interview online-focus-group computer-assisted-interviewing


Online Support Vector Machine

Online Support Vector Machine (Incremental SVM for Streaming Data)

Online SVM
Incremental SVM
LASVM
Pegasos SVM

Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM)

Online SVM adapts the classical support vector machine to streaming or sequentially arriving data by updating the decision boundary one example at a time rather than solving a global quadratic program. Algorithms such as Pegasos and LASVM make this tractable at large scale, preserving the margin-maximising spirit of SVMs with sub-linear time per update.

Continuous, binary, or encoded categorical features; binary or multiclass labels

Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30.

Bordes, A., Ertekin, S., Weston, J., & Bottou, L. (2005). Fast kernel classifiers with online and active learning. Journal of Machine Learning Research, 6, 1579–1619.

support-vector-machine online-learning online-logistic-regression online-gradient-boosting sgd-classifier kernel-perceptron


Online Survey

web survey
internet survey
e-survey
computer-assisted web interviewing

Mid-1990s (widespread scholarly adoption ~1995–2000)

Mick P. Couper, Don A. Dillman (early systematic frameworks)

An online survey is a structured data collection instrument hosted on a web platform and completed by respondents via internet-connected devices. It enables large-scale, geographically dispersed data gathering at low cost and with rapid turnaround. Respondents self-administer the questionnaire at their convenience, which reduces interviewer bias and permits automatic data capture. Online surveys are the dominant mode of survey research in social, behavioural, health, and market research today.

Quantitative / mixed-methods data collection technique

Self-reported structured responses (Likert scales, multiple choice, open text)

survey structured-interview mobile-survey face-to-face-survey delphi-technique longitudinal-survey


Online Systematic Sampling

web systematic sampling
digital systematic sampling
interval sampling online
e-survey systematic sampling

Adapted from classical systematic sampling (Madow & Madow, 1944) for web survey contexts

Online systematic sampling applies the classical every-k-th-element rule to digital survey contexts — selecting respondents from a web panel, membership database, or visitor stream at a fixed interval. It combines the operational simplicity of systematic sampling with the reach and speed of online data collection, producing a roughly representative sample without requiring complex randomisation infrastructure.

Digital survey responses; web panel records; visitor or membership lists

Lohr, S. L. (2022). Sampling: Design and Analysis (3rd ed.). CRC Press / Chapman & Hall.

systematic-sampling online-simple-random-sampling online-stratified-sampling online-cluster-sampling online-quota-sampling proportional-systematic-sampling


Online theoretical sampling

internet-based theoretical sampling
digital theoretical sampling
web-based theoretical sampling

1967 (theoretical sampling); online adaptation ~2000s–2010s

Glaser & Strauss (theoretical sampling); adapted to online contexts by internet qualitative researchers

Online theoretical sampling applies the logic of theoretical sampling — selecting participants or data sources based on emerging theory rather than predetermined criteria — within digital environments. Researchers iteratively recruit from online communities, forums, social media, or virtual networks, guided at each step by conceptual gaps identified during concurrent analysis. It is most commonly used in grounded theory studies conducted wholly or partially over the internet.

Qualitative data from online sources (forums, social media, virtual communities, online interviews)

Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.

Salmons, J. (2015). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). SAGE Publications.

theoretical-sampling snowball-sampling online-snowball-sampling purposive-sampling grounded-theory online-purposive-sampling


Online Transfer learning

Online Transfer Learning (Streaming Transfer Learning)

OTL
streaming transfer learning
incremental transfer learning
online domain adaptation

Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled target datasets upfront.

Online learning with source-domain knowledge transfer

Sequential/streaming labeled and unlabeled instances, with source-domain data

Zhao, P., & Hoi, S. C. H. (2010). OTL: A Framework of Online Transfer Learning. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 1231–1238. Omnipress.

Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

transfer-learning online-learning semi-supervised-learning domain-adaptation few-shot-learning meta-learning


Online Trust Scale

The Online Trust Scale measures consumer confidence and belief in online platforms (e-commerce websites, digital services, social platforms). Developed by researchers including Jarvenpaa, Tractinsky, and Vitale (2000) and Walker and Johnson (2006), this scale captures dimensions of perceived security, vendor reliability, privacy protection, and transaction confidence. Online trust is a critical antecedent to behavioral intention and actual purchase/usage in digital commerce contexts.

Walker, D., & Johnson, R. (2006). Why consumers use online and visit physical stores. Journal of Retailing and Consumer Services, 13(2), 143-151.

Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1(1-2), 45-71.

technology-readiness-index tam-questionnaire elearning-satisfaction-scale social-media-engagement-scale


Online Voting Ensemble

Online Voting Ensemble (Incremental Majority-Vote Ensemble for Data Streams)

streaming voting ensemble
incremental voting ensemble
online majority-vote ensemble
data-stream voting classifier

Oza, N. C. & Russell, S.; extended by Bifet et al.

Online Voting Ensemble is an incremental ensemble method that maintains a pool of base classifiers — each updated continuously on arriving data — and combines their predictions through a weighted or unweighted majority vote. Designed for data streams, it adapts to non-stationary distributions without retraining from scratch, making it well-suited to real-time classification tasks where data arrives sequentially and concept drift may occur.

Online ensemble (incremental majority vote)

Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236.

Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., & Gavaldà, R. (2009). New ensemble methods for evolving data streams. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 139–148.

voting-ensemble online-learning online-bagging online-boosting online-random-forest semi-supervised-voting-ensemble


Online Weighted Sampling

web-based weighted sampling
internet survey weighting
online panel weighting
weighted internet sampling

Survey methodology practitioners; systematized via probability-based online panels (e.g., Knowledge Networks, founded late 1990s)

Online weighted sampling is the practice of recruiting respondents via internet platforms and then applying statistical weights to correct for unequal selection probabilities, coverage gaps, and differential non-response. It enables researchers to draw valid population inferences from web surveys by compensating for the structural biases inherent in online recruitment — including the fact that not all members of a target population have equal internet access or equal likelihood of joining a panel.

Probability-adjusted online sampling technique

Quantitative survey data collected via internet platforms

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.

Bethlehem, J., & Biffignandi, S. (2012). Handbook of Web Surveys. Wiley.

weighted-sampling online-stratified-sampling quota-sampling online-simple-random-sampling post-stratification propensity-score-weighting


Open Access Publishing Models

Open Access Publishing Models and Licensing

OA Publishing
Gold Open Access
Green Open Access
Diamond OA

Budapest Open Access Initiative (2002); open science movement

Open access (OA) publishing removes subscription paywalls, making research freely available to all readers online without subscription fees. The Budapest Open Access Initiative (2002) defined OA as the right to read, download, copy, distribute, print, search, and link research freely. Multiple OA models exist: Gold OA (immediate free access, often author-funded via APCs), Green OA (free self-archiving in repositories), and Diamond OA (free to both authors and readers). OA expands research impact, enables global participation in science, and aligns with public funding mandates. However, OA models vary in sustainability and are sometimes exploited by predatory publishers.

Budapest Open Access Initiative (2002, revised 2012). Budapest Open Access Initiative.

Suber, P. (2012). Open Access. MIT Press.

Directory of Open Access Journals (2023). DOAJ. Public database of open access journals.

predatory-journals peer-review-process preprint-servers cope-guidelines


Open Coding

initial coding
open categorisation
substantive coding

1967 (Glaser & Strauss); refined 1990 (Strauss & Corbin)

Barney G. Glaser & Anselm L. Strauss (classic grounded theory); elaborated by Anselm Strauss & Juliet Corbin

Open coding is the first, exploratory phase of qualitative data analysis in which raw text — interviews, field notes, or documents — is broken into discrete segments and labelled with short descriptive codes. Developed within grounded theory by Glaser and Strauss and later elaborated by Strauss and Corbin, the procedure is deliberately open and inductive: the analyst reads line-by-line without imposing a predetermined framework, allowing concepts to emerge directly from the data. The resulting codes are then compared and grouped into provisional categories that become the building blocks for subsequent, more selective analysis.

Interview transcripts, field notes, documents, observational records

15–30 interviews or equivalent text units

Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.

grounded-theory thematic-analysis content-analysis narrative-analysis discourse-analysis phenomenology


Open Information Extraction

Open IE
OpenIE
open relation extraction
Açık Bilgi Çıkarma (Open IE)

Banko, Cafarella, Soderland, Broadhead & Etzioni

Open Information Extraction (Open IE) is a text-mining task that automatically extracts subject-relation-object triples from text without requiring a predefined relation schema. Introduced by Banko and colleagues (2007) for extraction over the open web, it converts free-running text into structured assertions used to build knowledge graphs and to mine large text collections.

Output: Subject-relation-object triples

Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M. & Etzioni, O. (2007). Open Information Extraction from the Web. Proceedings of IJCAI 2007, 2670-2676.

Mausam (2016). Open Information Extraction Systems and Downstream Applications. Proceedings of IJCAI 2016, 4074-4077.

named-entity-recognition entity-linking knowledge-graph constituency-parsing


Operator Performance Assessment Scale

William W. Wierwille, Frank T. Eggemeier

The Operator Performance Assessment Scale (OPAS), formalized by Wierwille and Eggemeier in 1993, is a structured rating method for assessing operator task performance on multiple dimensions (primary task accuracy, secondary task accuracy, task completion time, error rate, procedure adherence) in applied settings. OPAS bridges subjective workload perception (NASA-TLX, situational awareness) and objective behavioral metrics by capturing expert judgment of performance quality across multiple performance channels, enabling holistic evaluation of how well operators managed task demands.

Wierwille, W. W., & Eggemeier, F. T. (1993). Recommendations for mental workload measurement in a test and evaluation environment. Human Factors, 35(2), 263–281.

Vidulich, M. A., & Tsang, P. S. (1988). The role of output modality in performance and mental workload during concurrent spatial and verbal tasks. Human Factors, 30(5), 613–623.

situational-awareness-rating workload-profile nasa-task-load-index human-error-assessment


Opinion Mining

Opinion Mining (Aspect-Based Sentiment Extraction)

aspect-based sentiment analysis
opinion extraction
Görüş Madenciliği (Opinion Mining)

Opinion mining is a natural-language-processing task that systematically extracts and analyses user opinions about a product, service, or topic — identifying the specific features (aspects) being discussed, the sentiment expressed toward each, and the opinion holders. Consolidated by Bing Liu (2012), it goes beyond a single document-level label to produce structured aspect–opinion–holder records.

Output: Aspect–opinion–holder triples with polarity

Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool.

Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.


ORT

The ORT is a brief, 10-item self-report screening instrument designed to identify patients at elevated risk for opioid misuse, addiction, or aberrant drug-related behaviors prior to initiating opioid therapy. Developed by Webster and Webster in 2005, it stratifies patients into low, moderate, and high risk categories based on personal and family history of substance abuse, psychiatric comorbidity, and psychosocial factors. The ORT is widely used in pain management and primary care settings to guide shared decision-making and risk mitigation strategies when prescribing opioids.

Webster, L. R., & Webster, R. M. (2005). Predicting aberrant behaviors in opioid-treated patients: preliminary validation of the Opioid Risk Tool. Pain Medicine, 6(6), 432–442.

dudit sadq brief-addiction-monitor substance-abuse-subtle-screening


Lucas-Kanade Optical Flow

The Lucas-Kanade method, introduced by Bruce Lucas and Takeo Kanade in 1981, is a foundational technique for estimating optical flow—the apparent motion of objects in image sequences. By computing pixel-level motion vectors, the Lucas-Kanade algorithm tracks feature displacements between consecutive frames, enabling object tracking, motion estimation, and video analysis.

Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI), 674–679.

Bouguet, J. Y. (2001). Pyramidal implementation of the Lucas Kanade feature tracker. OpenCV Documentation.

harris-corner-detection sift-feature-detection scale-space-theory template-matching blob-detection


Optically Stimulated Luminescence Dating (OSL)

Optically stimulated luminescence (OSL) dating is a chronometric method that determines the age of sedimentary materials by measuring light-induced electron release from mineral grains. Developed by David Huntley and colleagues in the 1980s, it measures the time elapsed since sediment was last exposed to sunlight. This technique is widely used in archaeology, geology, and paleoenvironmental studies to date deposits ranging from a few decades to several hundred thousand years old.

Huntley, D. J., Godfrey-Smith, D. I., & Thewalt, M. L. (1985). Thermoluminescence dating of ocean sediments. Canadian Journal of Earth Sciences, 22(3), 423-427.

Rhodes, E. J. (1994). Optical dating of quartz using the 320 nm TL emission. Radiation Measurements, 23(2-3), 371-378.

Wintle, A. G., & Murray, A. S. (2006). A review of quartz optically stimulated luminescence characteristics and their relevance to single-aliquot regeneration dating protocols. Radiation Measurements, 41(4), 369-391.

thermoluminescence-dating electron-spin-resonance-dating uranium-thorium-dating archaeomagnetic-dating


OPTICS

OPTICS: Ordering Points To Identify the Clustering Structure

density-based clustering with reachability plot

Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J.

OPTICS (Ordering Points To Identify the Clustering Structure) is a density-based clustering algorithm introduced by Ankerst, Breunig, Kriegel, and Sander in 1999. It generalizes DBSCAN by processing points in an ordering that encodes the full density-based cluster structure of a dataset, enabling the detection of clusters of varying densities through a reachability plot rather than requiring a fixed global density threshold.

Density-based clustering (reachability ordering)

Unsupervised clustering with varying-density support

minPts (minimum neighbourhood size), epsilon (upper bound on neighbourhood radius)

Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2), 49–60.

Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231.

Aggarwal, C. C., & Reddy, C. K. (Eds.) (2013). Data Clustering: Algorithms and Applications (Ch. 4). CRC Press.

dbscan hdbscan kmeans hierarchical-clustering gaussian-mixture-model


Optimal Experimental Design

Optimal Experimental Design (D-Optimal, I-Optimal)

Optimal Deneme Deseni (D-Optimal, I-Optimal)

Optimal experimental design is a computer-aided approach to constructing experiments that maximises statistical efficiency for a given model and run budget. Formalised by V. V. Fedorov in 1972, it selects experimental points from a candidate set so that the information matrix M = X'X is optimised according to a chosen criterion — most commonly D-optimality (maximising the determinant) or I-optimality (minimising average prediction variance). It is the preferred strategy whenever classical designs such as central composite or Box-Behnken cannot be applied because the experimental region is constrained or factor ranges are irregular.

constrained regions, irregular factor spaces

Coordinate exchange, simulated annealing

Fedorov, V.V. (1972). Theory of Optimal Experiments. Academic Press.

Atkinson, A.C., Donev, A.N., & Tobias, R.D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press.

response-surface-methodology central-composite-design box-behnken-design plackett-burman factorial-design d-optimal-design


Optimal Power Flow

Optimal Power Flow for Economic and Security-Constrained Dispatch

Optimal Power Flow (OPF) is a fundamental optimization framework for computing the most economical and secure operating point of an electrical power system. Introduced by Jean Carpentier in 1962, OPF minimizes operational costs (fuel, losses, or other expenses) while satisfying physical and operational constraints. Modern electric grids depend on OPF for real-time economic dispatch, security analysis, and planning, making it one of the most important problems in power systems engineering.

Nonlinear constrained optimization for power system operation

Carpentier, J. (1962). Contribution à l'étude du dispatching économique. Bulletin de la Société Française des Électriciens, 8(3), 431-447.

Momoh, J. A. (2014). Optimal Power Flow Solutions. IEEE Transactions on Power Systems, 62(2), 576-585.

Alsac, O., & Stott, B. (1974). Optimal load flow with steady-state security. IEEE Transactions on Power Apparatus and Systems, 93(3), 745-751.


Optimality Theory

Optimality Theory (OT) is a constraint-based framework for modeling phonology and syntax, developed by Alan Prince and Paul Smolensky in 1993. The core idea is that languages produce the optimal output that best satisfies a ranked hierarchy of universal constraints. Rather than listing rules, OT explains linguistic phenomena as solutions to conflicting pressures—sounds and structures emerge as the least bad compromise among competing demands. This framework has revolutionized phonological theory and is widely applied to morphophonology, segmental and suprasegmental analysis, and cross-linguistic variation.

Prince, A., & Smolensky, P. (1993). Optimality Theory: Constraint Interaction in Generative Grammar. Blackwell Publishers.

Kager, R. (1999). Optimality Theory. Cambridge: Cambridge University Press.

McCarthy, J. D. (2008). Doing Optimality Theory: Applying Theory to Data. Malden, MA: Blackwell.


Life Orientation Test Revised

The Life Orientation Test – Revised (LOT-R) is a 10-item measure of dispositional optimism developed by Scheier, Carver, and Bridges in 1994. It assesses the general expectancy that good things (versus bad things) will happen in the future. Optimism, as measured by the LOT-R, predicts coping success, health outcomes, and psychological well-being independent of self-efficacy or other personality factors. The revised version addressed psychometric concerns in the original 1985 LOT, improving clarity and reducing item ambiguity.

Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing coping strategies from coping styles: A health psychological perspective. Journal of Personality and Social Psychology, 67(6), 1061–1078.

hope-scale flourishing-scale perma-scale subjective-wellbeing-scale


BBD with optimization
Box-Behnken design optimization
RSM-BBD optimization
Box-Behnken response optimization

1960 (BBD); optimization integration established 1980s–1990s

Box & Behnken (design); Derringer & Suich (desirability optimization)

Optimization-assisted Box-Behnken design (BBD) combines the Box-Behnken three-level experimental design with a formal optimization step to locate factor settings that maximize, minimize, or hit a target for one or more responses. BBD fits a second-order response surface model using fewer runs than a full factorial, and the optimization stage — typically via desirability functions or numerical search — then exploits that fitted model to identify the true optimum within the experimental region.

Experimental design with post-modeling optimization

Continuous numeric response data from designed experiments

Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.

Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.


Optimization-Assisted Central Composite Design

central composite design with response optimization

1951 (CCD); optimization coupling formalized 1970s–1990s

Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues

Optimization-assisted central composite design (CCD) combines the rotatable, second-order experimental layout of central composite design with mathematical optimization algorithms — typically desirability functions, response surface optimization, or metaheuristics — to find the factor settings that simultaneously maximize, minimize, or hit target values for one or more response variables. It is the most widely applied response-surface optimization workflow in chemical, pharmaceutical, food science, and manufacturing engineering.

Experimental design with mathematical optimization

Continuous process factors and quantitative response variables

Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley.

central-composite-design response-surface-methodology box-behnken-design full-factorial-design fractional-factorial-design taguchi-method


Optimization-Assisted Design of Experiments

1980 (desirability approach); broader integration through 1990s–2000s

Derringer & Suich (desirability function); extended by Myers, Montgomery, and Anderson-Cook

Optimization-assisted design of experiments (OA-DoE) couples a structured experimental plan with a mathematical optimization engine to locate factor settings that simultaneously satisfy multiple response objectives. Rather than stopping at fitting a response surface model, the analyst applies desirability functions, genetic algorithms, or other optimizers to the fitted model to identify the global or near-global optimum across all responses of interest.

Continuous response variables from designed experiments (numerical)

Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.

design-of-experiments response-surface-methodology central-composite-design box-behnken-design taguchi-method full-factorial-design


Optimization-Assisted Event Tree Analysis

optimization-enhanced event tree analysis

1975 (ETA); optimization integration ~1990s–2000s

Event tree analysis originated at the U.S. Nuclear Regulatory Commission (WASH-1400, 1975); optimization integration developed through risk engineering literature from the 1990s onward

Optimization-assisted event tree analysis couples the structured probability logic of classical event tree analysis (ETA) with an optimization layer — typically mathematical programming or metaheuristic search — to identify the best combination of safety barriers, mitigation strategies, or resource allocations that minimizes risk or cost while satisfying engineering constraints. It is used in industrial risk engineering, nuclear safety, process industries, and infrastructure reliability.

Hybrid risk analysis and optimization method

Event probabilities, system logic diagrams, failure rate data, resource/cost constraints

Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press.


Optimization-assisted failure mode and effects analysis

Optimization-Assisted Failure Mode and Effects Analysis

Optimization-assisted FMEA
FMEA with optimization
OA-FMEA
Optimized risk priority ranking

1949 (FMEA origin); optimization-assisted variants: 1990s–2000s

Extension of FMEA (U.S. Military, MIL-STD-1629, 1949); optimization integration developed in reliability and quality engineering literature from the 1990s onward

Optimization-assisted FMEA extends classical Failure Mode and Effects Analysis by embedding mathematical optimization algorithms — such as linear programming, multi-objective optimization, or metaheuristics — into the risk prioritization step. Rather than relying solely on the Risk Priority Number (RPN = Severity × Occurrence × Detectability), the approach frames corrective-action selection and resource allocation as an optimization problem, enabling more defensible, constraint-aware ranking and mitigation of failure modes.

Reliability and risk analysis technique with embedded optimization

Expert judgment scores (Severity, Occurrence, Detectability), quantitative failure data, engineering specifications

Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.

Liu, H.-C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems with Applications, 40(2), 828–838.


Optimization-Assisted Fractional Factorial Design

computer-aided fractional factorial design

1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995)

A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms)

Optimization-assisted fractional factorial design (OA-FFD) combines classical fractional factorial screening with algorithmic optimality criteria — such as D-, I-, or A-optimality — to construct experiment matrices that maximize statistical efficiency. Instead of relying solely on standard orthogonal-array tables, a computer algorithm selects the best subset of runs from a candidate set, enabling experimenters to handle irregular factor constraints, mixed factor types, and custom run sizes that standard tables cannot accommodate.

Optimal experimental design / computer-generated DOE

Continuous and/or categorical factor levels; quantitative response data

Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press.

Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.

fractional-factorial-design full-factorial-design central-composite-design box-behnken-design design-of-experiments response-surface-methodology


Optimization-Assisted Full Factorial Design

full factorial design with response optimization

1980s–1990s (formalized with desirability functions by Derringer & Suich, 1980)

Integrated from D. C. Montgomery (DoE) and classical optimization literature

Optimization-assisted full factorial design is a structured engineering workflow that runs a complete full factorial experiment — covering every combination of factor levels — and then applies a formal optimization method to identify the factor settings that best satisfy one or more performance targets. It combines the exhaustive data coverage of full factorial design with numerical or analytical optimization to turn experimental results into actionable optimal configurations.

Continuous or categorical factor levels; measured numeric responses

full-factorial-design fractional-factorial-design response-surface-methodology taguchi-method design-of-experiments multi-response-full-factorial-design


Optimization-Assisted Process Capability Analysis

V. E. Kane (capability indices, 1986); integrated with optimization frameworks by quality engineering researchers in the 1990s–2000s

Optimization-assisted process capability analysis combines classical capability indices (Cp, Cpk, Cpm) with mathematical optimization to identify process parameter settings that simultaneously satisfy engineering specifications and maximize process capability. Rather than simply measuring whether a process is capable, it prescribes the control factor levels — mean, variance, tolerances — that push capability above a target threshold. It is widely applied in manufacturing, chemical processing, and quality engineering contexts where multiple process variables must be tuned jointly.

Continuous process measurement data (dimensional, chemical, mechanical)

Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41–52.

Montgomery, D. C. (2019). Introduction to Statistical Quality Control (8th ed.). Wiley.

process-capability-analysis response-surface-methodology design-of-experiments statistical-process-control taguchi-method six-sigma-dmaic


Optimization-assisted quality function deployment

Optimization-Assisted Quality Function Deployment

Optimization-integrated QFD
QFD with optimization
Mathematical programming QFD
OA-QFD

Yoji Akao (QFD); optimization extensions by various researchers (1990s–2000s)

Optimization-assisted QFD extends the classic House of Quality framework by embedding mathematical optimization — linear programming, multi-objective optimization, or metaheuristics — directly into the QFD process. This allows engineers to simultaneously maximize customer satisfaction and minimize cost or resource constraints when setting target values for engineering characteristics, going beyond the largely subjective priority rankings of traditional QFD.

Customer requirements (voice of the customer), engineering characteristics, relationship matrices, optimization constraints

Akao, Y. (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press, Cambridge, MA.

Lai, X., Xie, M., & Tan, K. C. (2004). Optimizing product design using the Kano model and QFD. Engineering Management Conference, 2004 IEEE International, 1085-1089.


Enevoldsen, Sørensen, Der Kiureghian (foundational RBDO formulations, 1990s)

Optimization-assisted reliability analysis couples probabilistic reliability assessment with mathematical optimization to simultaneously identify failure probabilities and find design configurations that satisfy reliability targets at minimum cost or weight. Widely applied in structural, mechanical, and aerospace engineering, it integrates methods such as FORM, SORM, or Monte Carlo simulation within an optimization loop so that design decisions are driven by quantified risk rather than deterministic safety factors alone.

Probabilistic (failure probabilities, limit state functions, design variable distributions)

Haukaas, T., & Der Kiureghian, A. (2006). Strategies for finding the design point in non-linear finite element reliability analysis. Probabilistic Engineering Mechanics, 21(2), 133–147.

Enevoldsen, I., & Sørensen, J. D. (1994). Reliability-based optimization in structural engineering. Structural Safety, 15(3), 169–196.

reliability-analysis failure-mode-and-effects-analysis fault-tree-analysis design-of-experiments response-surface-methodology robust-reliability-analysis