{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:38:46Z","timestamp":1789717126385,"version":"4.0.1"},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Existing data integration frameworks are poorly suited for the special requirements of scientists. To answer a specific research question, often, excerpts of data from different sources need to be integrated. The relevant parts and the set of underlying sources may differ from query to query. The analyses also oftentimes involve frequently changing data and exploratory querying. Additionally, The data sources not only store data in different formats, but also provide inconsistent data access functionality. The classic Extract-Transform-Load (ETL) approach seems too complex and time-consuming and does not fit well with interest and expertise of the scientists.<\/jats:p>\n          <jats:p>With QUIS (QUery In-Situ), we provide a solution for this problem. QUIS is an open source heterogeneous in-situ data querying system. It utilizes a federated query virtualization approach that is built upon plugged-in adapters. QUIS takes a user query and transforms appropriate portions of it into the corresponding computation model on individual data sources and executes it. It complements the segments of the query that the target data sources can not execute. Hence, it guarantees full syntax and semantic support for its language on all data sources. QUIS's in-situ querying facility almost eliminates the time to prepare the data while maintaining a competitive performance and steady scalability.<\/jats:p>\n          <jats:p>The present demonstration illustrates interesting features of the system: virtual Schemas, heterogeneous joins, and visual query results. We provide a realistic data processing scenario to examine the system's features. Users can interact with QUIS using its desktop workbench, command line interface, or from any R client including RStudio Server.<\/jats:p>","DOI":"10.14778\/3137765.3137798","type":"journal-article","created":{"date-parts":[[2017,9,7]],"date-time":"2017-09-07T13:35:53Z","timestamp":1504791353000},"page":"1877-1880","source":"Crossref","is-referenced-by-count":8,"title":["QUIS"],"prefix":"10.14778","volume":"10","author":[{"given":"Javad","family":"Chamanara","sequence":"first","affiliation":[{"name":"Friedrich Schiller University of Jena, Jena, TH, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Birgitta","family":"K\u00f6nig-Ries","sequence":"additional","affiliation":[{"name":"Friedrich Schiller University of Jena, Jena, TH, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. V.","family":"Jagadish","sequence":"additional","affiliation":[{"name":"University of Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2845915"},{"key":"e_1_2_1_2_1","volume-title":"China-North America Library Conference","author":"Borgman C. L.","year":"2010","unstructured":"C. L. Borgman . Research data : Who will share what, with whom, when, and why ? China-North America Library Conference , 2010 . C. L. Borgman. Research data: Who will share what, with whom, when, and why? China-North America Library Conference, 2010."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/362384.362685"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"e_1_2_1_5_1","volume-title":"Principles of data integration","author":"Doan A.","year":"2012","unstructured":"A. Doan , A. Halevy , and Z. Ives . Principles of data integration . Elsevier , 2012 . A. Doan, A. Halevy, and Z. Ives. Principles of data integration. Elsevier, 2012."},{"key":"e_1_2_1_6_1","volume-title":"Managing scientific data: From data integration to scientific workflows","author":"Lud\u00e4scher B.","year":"2006","unstructured":"B. Lud\u00e4scher Managing scientific data: From data integration to scientific workflows . Geological Society of America Special Papers , 2006 . B. Lud\u00e4scher et al. Managing scientific data: From data integration to scientific workflows. Geological Society of America Special Papers, 2006."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.729736"},{"key":"e_1_2_1_8_1","volume-title":"Efficient Analytics on Ordered Datasets Using MapReduce","author":"Yin J.","year":"2013","unstructured":"J. Yin , Y. Liao , M. Baldi , L. Gao , and A. Nucci . Efficient Analytics on Ordered Datasets Using MapReduce . 2013 . J. Yin, Y. Liao, M. Baldi, L. Gao, and A. Nucci. Efficient Analytics on Ordered Datasets Using MapReduce. 2013."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3137765.3137798","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:04:46Z","timestamp":1672221886000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3137765.3137798"}},"subtitle":["in-situ heterogeneous data source querying"],"short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":8,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2017,8]]}},"alternative-id":["10.14778\/3137765.3137798"],"URL":"https:\/\/doi.org\/10.14778\/3137765.3137798","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2017,8]]}}}