10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

A user-friendly framework for database preferences

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2014.257659,
        author={Roxana Gheorghiu and Alexandros Labrinidis and Panos Chrysanthis},
        title={A user-friendly framework for database preferences},
        proceedings={10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        keywords={big data query personalization group preferences},
  • Roxana Gheorghiu
    Alexandros Labrinidis
    Panos Chrysanthis
    Year: 2014
    A user-friendly framework for database preferences
    DOI: 10.4108/icst.collaboratecom.2014.257659
Roxana Gheorghiu1, Alexandros Labrinidis1,*, Panos Chrysanthis1
  • 1: University of Pittsburgh
*Contact email: labrinid@cs.pitt.edu


Data drives all aspects of our society, from everyday life, to business, to medicine, and science. It is well known that query personalization can be an effective technique in dealing with the data scalability challenge, primarily from the human point of view. In order to customize their query results, users need to express their preferences in a simple and user- friendly manner. There are two types of preferences: qualitative and quantitative. Each preference type has advantages and disadvantages with respect to expressiveness. In this paper, we present a graph-based theoretical framework and a prototype system that unify qualitative and quantitative preferences, while eliminating their disadvantages. Our integrated system allows for (1) the specification of database preferences and creation of user preference profiles in a user-friendly manner and (2) the manipulation of preferences of individuals or groups of users. A key feature of our hybrid model is the ability to convert qualitative preferences into quantitative preferences using intensity values and without losing the qualitative information. This feature allows us to create a total order over the tuples in the database, matching both qualitative and quantitative preferences, hence significantly increasing the number of tuples covered by the user preferences. We confirmed this experimentally by comparing our preference selection algorithm with Fagin’s TA algorithm.