2024-03-29T11:04:48Z
https://zenodo.org/oai2d
oai:zenodo.org:997603
2020-01-20T15:15:50Z
user-deg
user-eu
Konstantopoulos, Stasinos
Charalambidis, Angelos
Troumpoukis, Antonis
Mouchakis, Giannis
Karkaletsis, Vangelis
2017-09-20
<p>Dataset description vocabularies focus on provenance, ver-sioning, licensing, and similar metadata. VoID is a notable exception, providing some expressivity for describing subsets and their contents and can, to some extent, be used for discovering relevant resources and for optimizing querying. In this paper we describe the Sevod vocabulary, an extension of VoID that provides the expressivity needed in order to support the query planning methods typically used in federated querying. We also present a tool for automatically scraping such metadata from RDF dumps and give statistics about the size of the descriptions for the FedBench datasets<em>.</em></p>
https://doi.org/10.5281/zenodo.997603
oai:zenodo.org:997603
Zenodo
http://ceur-ws.org/Vol-1927/paper4.pdf
https://www.researchgate.net/publication/320054741
https://zenodo.org/communities/deg
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.997602
info:eu-repo/semantics/openAccess
Other (Attribution)
PROFILES 2017, 4th International Workshop on Dataset Profiling and Federated Search for Web Data, Vienna, Austria, 22 October 2017
RDF store histograms
RDF vocabulary
Optimizing federated SPARQL query processing
The Sevod Vocabulary for Dataset Descriptions for Federated Querying
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6761774
2022-06-27T16:18:34Z
user-deg
user-eu
Konstantopoulos, Stasinos
2021-06-15
<p>Demonstration video of the Fair for Fusion Demonstrator II.</p>
https://doi.org/10.5281/zenodo.6761774
oai:zenodo.org:6761774
eng
Zenodo
https://zenodo.org/communities/deg
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.6761773
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Fair For Fusion Demonstrator II
info:eu-repo/semantics/other
oai:zenodo.org:61017
2020-01-20T17:43:52Z
user-deg
user-radio
user-eu
Zamani, Katerina
Charalambidis, Angelos
Konstantopoulos, Stasinos
Dagioglou, Maria
Karkaletsis, Vangelis
2016-08-23
<p>The insights gained by the large-scale analysis of health-related data can have an enormous impact in public health and medical research, but access to such personal and sensitive data poses serious privacy implications for the data provider and a heavy data security and administrative burden on the data consumer. In this paper we present an architecture that fills the gap between the statistical tools ubiquitously used in medical research on the one hand, and privacy-preserving data mining methods on the other. This architecture foresees the primitive instructions needed to re-implement the elementary statistical methods so that they only access data via a privacy-preserving protocol. The advantage is that more complex analysis and visualisation tools that are built upon these elementary methods can remain unaffected. Furthermore, we introduce RASSP, a secure summation protocol that implements the primitive instructions foreseen by the architecture. An open-source reference implementation of this architecture is provided for the R language. We use these results to argue that the tension between medical research and privacy requirements can be technically alleviated and we outline a research plan towards a system that covers further requirements on computation efficiency and on the trust that the medical researcher can place on the statistical results obtained by it.</p>
Published as Springer LNCS 9817.
https://doi.org/10.1007/978-3-319-45507-5_16
oai:zenodo.org:61017
Zenodo
http://link.springer.com/chapter/10.1007%2F978-3-319-45507-5_16
https://zenodo.org/communities/deg
https://zenodo.org/communities/eu
https://zenodo.org/communities/radio
info:eu-repo/semantics/openAccess
Other (Attribution)
PAML 2016, Workshop on Privacy Aware Machine Learning for Health Data Science, Salzburg, Austria, 31 August 31 - 2 September 2016
Privacy-Preserving statistical analysis
Secure summation protocol
Statistical processing of health records
A Peer-to-Peer Protocol and System Architecture for Privacy-Preserving Statistical Analysis
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:159568
2020-01-20T16:30:54Z
user-deg
user-eu
Troumpoukis, Antonis
Charalambidis, Angelos
Mouchakis, Giannis
Konstantopoulos, Stasinos
Siebes, Ronald
de Boer, Victor
Soiland-Reyes, Stian
Digles, Daniela
2016-10-18
<p>This paper presents work in progress towards developing a new benchmark for federated query processing systems. Unlike other popular benchmarks, our queryset is not driven by technical evaluation, but is derived from workflows established by the pharmacology community. The value of this queryset is that it is realistic but at the same time it comprises complex queries that test all features of modern query processing systems.</p>
https://doi.org/10.5281/zenodo.159568
oai:zenodo.org:159568
Zenodo
http://ceur-ws.org/Vol-1700/paper-04.pdf
http://ceur-ws.org/Vol-1700
https://zenodo.org/communities/deg
https://zenodo.org/communities/eu
https://doi.org/
info:eu-repo/semantics/openAccess
Other (Attribution)
BLINK, Workshop on Benchmarking Linked Data, Kobe, Japan, 18 October 2016
Triple store benchmarking
Pharmacology data
Distributed and federated querying
Developing a Benchmark Suite for Semantic Web Data from Existing Workflows
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:1213624
2020-01-20T17:42:47Z
user-deg
user-eu
Mouchakis, Giannis
Stavrinos, Georgios
Konstantopoulos, Stasinos
Charalambidis, Angelos
Ermilov, Ivan
Langens, Jonathan
2018-01-31
<p>This report documents the deployment of the Big Data Integrator Platform and provides instructions on how to reproduce identical deployments. Various configurations and component mixtures of this generic platform will be used in the BDE pilots to serve exemplary use cases of the Horizon 2020 Societal Challenges.</p>
<p>The instructions in this document include (a) installation of the base system; (b) network topology and configuration; and (c) the components available as docker images and how they can be spawned and accessed to create pilot applications.</p>
https://doi.org/10.5281/zenodo.1213624
oai:zenodo.org:1213624
eng
Zenodo
https://zenodo.org/communities/deg
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.1213623
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
BigDataEurope D5.3: Generic Big Data Integrator Instance
info:eu-repo/semantics/technicalDocumentation
oai:zenodo.org:61018
2020-01-20T16:42:07Z
user-deg
Papantoniou, Katerina
Konstantopoulos, Stasinos
2016-08-01
<p>In this paper we explore the correlation between the sound of words and their meaning, by testing if the polarity (‘good guy’ or ‘bad guy’) of a character’s role in a work of fiction can be predicted by the name of the character in the absence of any other context. Our approach is based on phonological and other features pro- posed in prior theoretical studies of fictional names. These features are used to construct a predictive model over a manually annotated corpus of characters from motion pictures. By experimenting with different mixtures of features, we identify phonological features as being the most discriminative by comparison to social and other types of features, and we delve into a discussion of specific phonological and phonotactic indicators of a character’s role’s polarity.</p>
https://doi.org/10.5281/zenodo.61018
oai:zenodo.org:61018
Zenodo
https://aclweb.org/anthology/P/P16/P16-1203.pdf
https://aclweb.org/anthology/attachments/P/P16/P16-1203.Datasets.zip
https://zenodo.org/communities/deg
https://doi.org/
info:eu-repo/semantics/openAccess
Other (Open)
ACL 2016, 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 7-12 August 2016
Unravelling Names of Fictional Characters
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:242155
2020-01-20T13:35:16Z
user-deg
user-eu
Ikonomopoulos, Andreas
Konstantopoulos, Stasinos
2016-12-23
<p>A brief overview of the crowd-sourcing tools developed within the analytical platform of the PREPARE project is presented. Crowd sourcing relates to methods that: offer a Web Content Discovery Service that automatically acquires and analyses content from social networks and the Web to extract information during nuclear or radiological (NR) events as well as the public opinion and perspective regarding an event occurrence and development; support an Ask the Expert Service for the public to have access to information relevant to NR events. Crowd sourcing provides the means to enhance the relevance and quality of publicly available information that is to be distributed through the analytical platform. To that end, processes have been formulated to collect: Web content relevant to a NR event and analyse it for trend identification regarding public opinions and concerns expressed in Web forums. Large-scale content acquisition and text analytics for Web-forum monitoring within social networks provides results at a scale that cannot be manually achieved; feedback from the user interaction with the system and analyse it to identify documents of interest and relevance to the queries posed.</p>
The original publication is available at www.edpsciences.org/radiopro
https://doi.org/10.1051/radiopro/2016070
oai:zenodo.org:242155
Zenodo
http://www.radioprotection.org/articles/radiopro/abs/2016/06/radiopro160070s/radiopro160070s.html
https://zenodo.org/communities/deg
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Other (Attribution)
Radioprotection, 51(HS2), 187-189, (2016-12-23)
nuclear or radiological emergency
Web crawling
PREPARE analytical platform
Crowd-sourcing tools within the PREPARE analytical platform
info:eu-repo/semantics/article
oai:zenodo.org:160170
2020-01-20T15:41:38Z
user-deg
user-eu
Konstantopoulos, Stasinos
Charalambidis, Angelos
Mouchakis, Giannis
Troumpoukis, Antonis
Jakobitsch, Jürgen
Karkaletsis, Vangelis
2016-10-19
<p>The ability to cross-link large scale data with each other and with structured Semantic Web data, and the ability to uniformly process Semantic Web and other data adds value to both the Semantic Web and to the Big Data community. This paper presents work in progress towards integrating Big Data infrastructures with Semantic Web technologies, allowing for the cross-linking and uniform retrieval of data stored in both Big Data infrastructures and Semantic Web data. The technical challenges involved in achieving this, pertain to both data and system inter-operability: we need a way to make the semantics of Big Data explicit so that they can interlink and we need a way to make it transparent for the client applications to query federations of such heterogeneous systems. The paper presents an extension of the Semagrow federated SPARQL query processor that is able to seamlessly federated SPARQL endpoints, Cassandra databases, and Solr databases, and discusses future directions of this line of work.</p>
https://doi.org/10.5281/zenodo.160170
oai:zenodo.org:160170
Zenodo
http://ceur-ws.org/Vol-1690/paper33.pdf
http://ceur-ws.org/Vol-1690
https://zenodo.org/communities/deg
https://zenodo.org/communities/eu
https://doi.org/
info:eu-repo/semantics/openAccess
Other (Attribution)
ISWC, 15th International Semantic Web Conference, Kobe, Japan, 19-21 October 2016
Federated query processing
SPARQL
Big Data infrastructures
Semantic Web Technologies and Big Data Infrastructures: SPARQL Federated Querying of Heterogeneous Big Data Stores
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:159131
2020-01-20T17:21:52Z
user-deg
user-eu
Zamani, Katerina
Charalambidis, Angelos
Konstantopoulos, Stasinos
Zoulis, Nickolas
Mavroudi, Effrosyni
2016-09-10
<p>Query processing systems typically rely on histograms, data structures that approximate data distribution, in order to optimize query execution. Histograms can be constructed by scanning the database tables and aggregating the values of the attributes in the table, or, more efficiently, progressively refined by analysing query results. Most of the relevant literature focuses on histograms of numerical data, exploiting the natural concept of a numerical range as an estimator of the volume of data that falls within the range. This, however, leaves Semantic Web data outside the scope of the histograms literature, as its most prominent datatype, the URI, does not offer itself to defining such ranges. This article first establishes a framework that formalises histograms over arbitrary data types and provides a formalism for specifying value ranges for different datatypes. This makes explicit the properties that ranges are required to have, so that histogram refinement algorithms are applicable. We demonstrate that our framework subsumes histograms over numerical data as a special case by using to formulate the state-of-the-art in numerical histograms. We then proceed to use the Jaro-Winkler metric to define URI ranges by exploiting the hierarchical nature of URI strings. This greatly extends the state of the art, where strings are treated as categorical data that can only be described by enumeration. We then present the open-source STRHist system that implements these ideas. We finally present empirical evaluation results using STRHist over a real dataset and query workload extracted from AGRIS, the most popular and widely used bibliographic database on agricultural research and technology.</p>
https://doi.org/10.1007/978-3-662-53455-7_6
oai:zenodo.org:159131
Zenodo
http://link.springer.com/chapter/10.1007%2F978-3-662-53455-7_6
http://link.springer.com/book/10.1007/978-3-662-53455-7
http://tldks.faw.at/volume/32/
https://zenodo.org/communities/deg
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Other (Attribution)
Transactions on Large-Scale Data- and Knowledge-Centered Systems, 28, 133-156, (2016-09-10)
Database management
Data Mining and Knowledge Discovery
Artificial Intelligence
Workload-Aware Self-Tuning Histograms for the Semantic Web
info:eu-repo/semantics/article