Microblogs data management systems: Querying, analysis, and visualization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Microblogs data, e.g., tweets, reviews, news comments, and social media comments, has gained considerable attention in recent years due to its popularity and rich contents. Nowadays, microblogs applications span a wide spectrum of interests, including analyzing events and users activities and critical applications like discovering health issues and rescue services. Consequently, major research efforts are spent to manage, analyze, and visualize microblogs data to support different applications. In this tutorial, we give a 1.5 hours overview about microblogs data management, analysis, visualization, and systems. The tutorial gives a comprehensive review for research on core data management components to support microblogs queries at scale. This includes system-level issues and on-going work on supporting microblogs data through the rising wave of big data systems. In addition, the tutorial reviews research on microblogs data analysis and visualization. Through its different parts, the tutorial highlights the challenges and opportunities in microblogs data research.

Original languageEnglish (US)
Title of host publicationSIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages2219-2222
Number of pages4
ISBN (Electronic)9781450335317
DOIs
StatePublished - Jun 26 2016
Event2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
Duration: Jun 26 2016Jul 1 2016

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
Volume26-June-2016
ISSN (Print)0730-8078

Other

Other2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
Country/TerritoryUnited States
CitySan Francisco
Period6/26/167/1/16

Bibliographical note

Funding Information:
This research is capitally supported by NSF grants IIS-0952977, IIS-1218168, IIS-1525953, CNS-1512877, and the University of Minnesota Doctoral Dissertation Fellowship.

Publisher Copyright:
© 2016 ACM.

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