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Description
Skyline queries are a well-established technique used in multi criteria decision applications. There is a recent interest among the research community to efficiently compute skylines but the problem of presenting the skyline that takes into account the preferences of the user is still open. Each user has varying interests towards

Skyline queries are a well-established technique used in multi criteria decision applications. There is a recent interest among the research community to efficiently compute skylines but the problem of presenting the skyline that takes into account the preferences of the user is still open. Each user has varying interests towards each attribute and hence "one size fits all" methodology might not satisfy all the users. True user satisfaction can be obtained only when the skyline is tailored specifically for each user based on his preferences.



This research investigates the problem of preference aware skyline processing which consists of inferring the preferences of users and computing a skyline specific to that user, taking into account his preferences. This research proposes a model that transforms the data from a given space to a user preferential space where each attribute represents the preference of the user. This study proposes two techniques "Preferential Skyline Processing" and "Latent Skyline Processing" to efficiently compute preference aware skylines in the user preferential space. Finally, through extensive experiments and performance analysis the correctness of the recommendations and the algorithm's ability to outperform the naïve ones is confirmed.
ContributorsRathinavelu, Sriram (Author) / Candan, Kasim Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2014
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Description
A volunteered geographic information system, e.g., OpenStreetMap (OSM), collects data from volunteers to generate geospatial maps. To keep the map consistent, volunteers are expected to perform the tedious task of updating the underlying geospatial data at regular intervals. Such a map curation step takes time and considerable human effort. In

A volunteered geographic information system, e.g., OpenStreetMap (OSM), collects data from volunteers to generate geospatial maps. To keep the map consistent, volunteers are expected to perform the tedious task of updating the underlying geospatial data at regular intervals. Such a map curation step takes time and considerable human effort. In this thesis, we propose a framework that improves the process of updating geospatial maps by automatically identifying road changes from user-generated GPS traces. Since GPS traces can be sparse and noisy, the proposed framework validates the map changes with the users before propagating them to a publishable version of the map. The proposed framework achieves up to four times faster map matching performance than the state-of-the-art algorithms with only 0.1-0.3% accuracy loss.
ContributorsVementala, Nikhil (Author) / Papotti, Paolo (Thesis advisor) / Sarwat, Mohamed (Thesis advisor) / Kasim, Selçuk Candan (Committee member) / Arizona State University (Publisher)
Created2017