Browsing by Author "Ouzzani, Mourad"

Now showing items 1-7 of 7

    • Approving updates in collaborative databases 

      Mershad, Khaleel; Malluhi, Qutaibah M.; Ouzzani, Mourad; Tang, Mingjie; Aref, Walid G. ( Institute of Electrical and Electronics Engineers Inc. , 2015 , Conference)
      Data curation activities in collaborative databases mandate that collaborators interact until they converge and agree on the content of their data. Typically, updates by a member of the collaboration are made visible to ...

    • Efficient parallel skyline query processing for high-dimensional data 

      Tang, Mingjie; Yu, Yongyang; Aref, Walid G.; Malluhi, Qutaibah M.; Ouzzani, Mourad ( IEEE Computer Society , 2019 , Conference)
      Given a set of multidimensional data points, skyline queries retrieve those points that are not dominated by any other points in the set. Due to the ubiquitous use of skyline queries, such as in preference-based query ...

    • Efficient processing of hamming-distance-based similarity-search queries over MapReduce 

      Tang, Mingjie; Yu, Yongyang; Aref, Walid G.; Malluhi, Qutaibah M.; Ouzzani, Mourad ( OpenProceedings.org, University of Konstanz, University Library , 2015 , Conference)
      Similarity search is crucial to many applications. Of particular interest are two flavors of the Hamming distance range query, namely, the Hamming select and the Hamming join (Hamming-select and Hamming-join, respectively). ...

    • LocationSpark: A distributed in-memory data management system for big spatial data 

      Tang, Mingjie; Yu, Yongyang; Malluhi, Qutaibah M.; Ouzzani, Mourad; Aref, Walid G. ( VLDB Endowment , 2015 , Conference)
      We present LocationSpark, a spatial data processing system built on top of Apache Spark, a widely used distributed data processing system. LocationSpark offers a rich set of spatial query operators, e.g., range search, ...

    • LocationSpark: In-memory Distributed Spatial Query Processing and Optimization 

      Tang, Mingjie; Yu, Yongyang; Mahmood, Ahmed R.; Malluhi, Qutaibah M.; Ouzzani, Mourad; ... more authors ( Frontiers Media S.A. , 2020 , Article)
      Due to the ubiquity of spatial data applications and the large amounts of spatial data that these applications generate and process, there is a pressing need for scalable spatial query processing. In this paper, we present ...

    • Similarity Group-By operators for multi-dimensional relational data 

      Tang, Mingjie; Tahboub, Ruby Y.; Aref, Walid G.; Atallah, Mikhail J.; Malluhi, Qutaibah M.; ... more authors ( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference)
      The SQL group-by operator plays an important role in summarizing and aggregating large datasets in a data analytics stack. The Similarity SQL-based Group-By operator (SGB, for short) extends the semantics of the standard ...

    • The similarity-aware relational database set operators 

      Al Marri, Wadha J.; Malluhi, Qutaibah; Ouzzani, Mourad; Tang, Mingjie; Aref, Walid G. ( Elsevier , 2016 , Article)
      Identifying similarities in large datasets is an essential operation in several applications such as bioinformatics, pattern recognition, and data integration. To make a relational database management system similarity-aware, ...