{"id":666,"date":"2017-12-21T13:17:21","date_gmt":"2017-12-21T12:17:21","guid":{"rendered":"https:\/\/testwebsite.2018.eswc-conferences.org\/?page_id=666"},"modified":"2017-12-21T13:19:56","modified_gmt":"2017-12-21T12:19:56","slug":"semantic-data-management-and-big-data","status":"publish","type":"page","link":"\/subtracks\/semantic-data-management-and-big-data\/","title":{"rendered":"Semantic Data Management and Big Data"},"content":{"rendered":"
Semantic data management refers to approaches that focus on maintaining and using data in terms of its meaning. While there exist effective solutions for semantic data management, Big Data characteristics like volume, variety, velocity, and veracity prevent such solutions from being used on a large scale. In particular, data management tasks that require automated inferencing may be affected negatively by Big Data characteristics, and novel methods are required to address these issues efficiently.
\nThe aim of this track is to gather researchers and developers from the Semantic Web, Databases, and Artificial Intelligence fields to discuss research issues, experiences, and results in designing, implementing, deploying, and evaluating theories, techniques, and applications related to semantic data management on Big Data sources.<\/p>\n
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Topics of interest include, but are not limited to:<\/p>\n
Description Semantic data management refers to approaches that focus on maintaining and using data in terms of its meaning. While there exist effective solutions for semantic data management, Big Data characteristics like volume, variety, velocity, and veracity prevent such solutions from being used on a large scale. In particular, data management tasks that require automated […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":155,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"yoast_head":"\n