{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T15:01:41Z","timestamp":1648911701505},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,8,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Using graph data models for business intelligence applications is a novel and promising approach. In contrast to\ntraditional data warehouse models, graph models enable the mining of relationship patterns. In our prior work, we\nintroduced an approach to graph-based data integration and analytics called BIIIG (Business Intelligence with Integrated\nInstance Graphs). In this work, we compare state-of-the-art systems for graph data management and analytics with regard to\nthe support for our approach in Big Data scenarios. To exemplify the analytical value of graph models for business\nintelligence, we propose an analytical workflow to extract knowledge from graph-integrated business data. Finally, we show\nhow we use Gradoop, a novel framework for distributed graph analytics, to implement our approach.<\/jats:p>","DOI":"10.1515\/itit-2016-0006","type":"journal-article","created":{"date-parts":[[2016,6,27]],"date-time":"2016-06-27T16:14:52Z","timestamp":1467044092000},"page":"166-175","source":"Crossref","is-referenced-by-count":1,"title":["Scalable business intelligence with graph collections"],"prefix":"10.1515","volume":"58","author":[{"given":"Andr\u00e9","family":"Petermann","sequence":"first","affiliation":[{"name":"Universit\u00e4t Leipzig, Institut f\u00fcr Informatik, D-04109 Leipzig, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Junghanns","sequence":"additional","affiliation":[{"name":"Universit\u00e4t Leipzig, Institut f\u00fcr Informatik, D-04109 Leipzig, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2016,6,24]]},"container-title":["it - Information Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/itit.2016.58.issue-4\/itit-2016-0006\/itit-2016-0006.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0006\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0006\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T11:44:48Z","timestamp":1624448688000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0006\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,24]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,6,24]]},"published-print":{"date-parts":[[2016,8,28]]}},"alternative-id":["10.1515\/itit-2016-0006"],"URL":"https:\/\/doi.org\/10.1515\/itit-2016-0006","relation":{},"ISSN":["1611-2776","2196-7032"],"issn-type":[{"value":"1611-2776","type":"print"},{"value":"2196-7032","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,24]]}}}