Dataset Open Access
Haoxiang Yu;
Jie Hua;
Christine Julien
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nmm##2200000uu#4500</leader> <datafield tag="999" ind1="C" ind2="5"> <subfield code="x">Haoxiang Yu, Jie Hua, and Christine Julien. 2021. Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems (SenSys '21). Association for Computing Machinery, New York, NY, USA, 537–541. 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Any opinions, findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect the views of the NSF.</subfield> </datafield> <controlfield tag="001">5572861</controlfield> <datafield tag="711" ind1=" " ind2=" "> <subfield code="d">November 15--17, 2021</subfield> <subfield code="g">SenSys '21</subfield> <subfield code="a">The 19th ACM Conference on Embedded Networked Sensor Systems</subfield> <subfield code="c">Coimbra, Portugal</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">University of Texas at Austin</subfield> <subfield code="0">(orcid)0000-0003-2901-3510</subfield> <subfield code="a">Jie Hua</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">University of Texas at Austin</subfield> <subfield code="0">(orcid)0000-0002-4131-4642</subfield> <subfield code="a">Christine Julien</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">8246183</subfield> <subfield code="z">md5:3b1cf3fe2d0fc196b21d896db2b3ffe2</subfield> <subfield code="u">https://zenodo.org/record/5572861/files/Analysis on the dataset.ipynb</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">12361</subfield> <subfield code="z">md5:4716dba8cc6eb1e08c0f52db37d53e03</subfield> <subfield code="u">https://zenodo.org/record/5572861/files/categories_with_service.json</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">282</subfield> <subfield code="z">md5:27d5389e17100ec415445cb975df791b</subfield> <subfield code="u">https://zenodo.org/record/5572861/files/licenses.txt</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">27194</subfield> <subfield code="z">md5:535c47c1fe19d2da4afb7551bc26519a</subfield> <subfield code="u">https://zenodo.org/record/5572861/files/service_with_categories.json</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">136602625</subfield> <subfield code="z">md5:5886e746d6b6d9fae2ef3b508c69f579</subfield> <subfield code="u">https://zenodo.org/record/5572861/files/Step1_Raw_Data.csv</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">34284181</subfield> <subfield code="z">md5:2b7d772525f4bcc6e98133ad68fa01d7</subfield> <subfield code="u">https://zenodo.org/record/5572861/files/Step2_Popular_Rules.csv</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">8048047</subfield> <subfield code="z">md5:6de4c88657febb5af19d97ee9f2e00c9</subfield> <subfield code="u">https://zenodo.org/record/5572861/files/Step3_IoT_Rules.csv</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">7664681</subfield> <subfield code="z">md5:3ef5e65d8efc75cad554e448ef5322e8</subfield> <subfield code="u">https://zenodo.org/record/5572861/files/Step4_Single_Trigger_IoT_Rules.csv</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="y">Conference website</subfield> <subfield code="u">https://sensys.acm.org/2021/</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2021-10-18</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire_data</subfield> <subfield code="p">user-workshopdata</subfield> <subfield code="o">oai:zenodo.org:5572861</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">University of Texas at Austin</subfield> <subfield code="0">(orcid)0000-0002-3518-946X</subfield> <subfield code="a">Haoxiang Yu</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-workshopdata</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution Non Commercial Share Alike 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>This archive contains the files submitted to the 4th&nbsp;International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled &quot;Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices&quot;</p> <p>With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the &#39;&#39;smartness&#39;&#39; of those devices to address daily needs in people&#39;s lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: &#39;&#39;What kinds of behaviors do humans expect from their IoT devices?&#39;&#39; The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.</p> <p>For more detail about this dataset, please refer to the paper listed above.</p></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isSupplementTo</subfield> <subfield code="a">10.1145/3485730.3494115</subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.5281/zenodo.5572860</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.5281/zenodo.5572861</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">dataset</subfield> </datafield> </record>
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