Advanced Ranking Queries on Composite Data

Advanced Ranking Queries on Composite Data - Title Author(s...

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Title Advanced ranking queries on composite data Author(s) Qi, Shuyao; Citation Qi, S. [ ]. (2016). Advanced ranking queries on composite data. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Issued Date 2016 URL http://hdl.handle.net/10722/235931 Rights The author retains all proprietary rights, (such as patent rights) and the right to use in future works.
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Advanced Ranking Queries on Composite Data by Shuyao Qi Department of Computer Science The University of Hong Kong Supervised by Prof. Nikos Mamoulis A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at The University of Hong Kong September 2016
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Abstract of thesis entitled “Advanced Ranking Queries on Composite Data” Submitted by Shuyao Qi for the degree of Doctor of Philosophy at The University of Hong Kong in September 2016 Ranking and retrieving the best objects from a database based on a set of criteria is a fundamental problem and has received extensive research efforts. With the vast development of data science and engineering, modern data have become increasingly more complex and composite , i.e., objects are routinely assigned multiple types of information. This thesis studies several advanced ranking queries over composite data. In particular, three novel ranking queries are investigated in detail. First, we introduce and study the problem of top- k joins over complex data types . Top- k joins have been extensively studied in relational databases, for the case where the join predicate is equality and the proposed algorithms aim at minimizing the number of accesses from the inputs. However, when collections of complex data types (e.g., spatial or string datasets) are top- k joined, computational cost can easily become the bottleneck. In view of this, we propose a novel evaluation paradigm that minimizes the computational cost without compromising the access cost. The proposed paradigm is applied for the cases of top- k joins on spatial and string attributes, and an analysis is conducted on how to optimize the paradigm for each case. Finally, the proposal is evaluated by extensive experimentation on both real and synthetic data. Next, the problem of point-based trajectory search is investigated. Trajectory data capture the traveling history of moving objects. With the vastly increased volume of trajectory collections, applications such as route recommendation and traveling behavior mining call for efficient trajectory retrieval. This thesis firstly studies distance-to-points trajectory search (DTS) which retrieves the top- k trajec- tories that pass as close as possible to a given set of query points. For this, the state-of-the-art is advanced by a hybrid method combining existing approaches and an alternative yet more efficient spatial range-based approach. Second, the con- tinuous counterpart of DTS is investigated where the query is long-standing and the results need to be maintained whenever updates occur to the query and/or the data. Third, two practical variants of DTS, which take into account the temporal characteristics of the searched trajectories, are proposed and studied. Extensive
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experiments are conducted to evaluate the proposed algorithms.
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