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QueRIE: Collaborative Database Exploration
IEEE Transactions on Knowledge and Data Engineering (2014)
  • Magdalini Eirinaki, San Jose State University
  • Suju Abraham, Lucille Packard’s Children Hospital
  • Neoklis Polyzotis, University of California, Santa Cruz
  • Naushin Shaikh, Data Domain, EMC
Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach.
  • Data mining,
  • Information Technology and Systems,
  • Database Management,
  • Database Applications,
  • Interactive data exploration and discovery,
  • Personalization
Publication Date
July, 2014
Publisher Statement
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The definitive version of this article may be found at IEEE Explore Digital Library. DOI: 10.1109/TKDE.2013.79
Citation Information
Magdalini Eirinaki, Suju Abraham, Neoklis Polyzotis and Naushin Shaikh. "QueRIE: Collaborative Database Exploration" IEEE Transactions on Knowledge and Data Engineering Vol. 26 Iss. 7 (2014) p. 1778 - 1790 ISSN: 1041-4347
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