ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Computer Science


Computer Science and Engineering

First Advisor

Gautam Das


Ranked retrieval model has rapidly replaced the traditional Boolean retrieval model as the de facto way for query processing when a large portion of (big) data matches a given query. Returning all the query results in these cases is not efficient nor informative. Unlike the Boolean retrieval model, the ranked retrieval model orders the matching tuples according to an often proprietary ranking function and returns the top-k of them. In this dissertation, we study ranked retrieval model and propose exact and approximate algorithms for (i) building representatives for fast query processing, and (ii) online processing of ranking queries. We study the problem both in the general cases and in the special environment of web databases, a natural fit for the ranked retrieval model. We start the dissertation by building representatives that serve as indices for ranking query processing. A critical observation is that skyline, also known as Pareto-optimal, (resp. k sky-band) is a set that contains the top-1 (resp. top-k) for every possible ranking function following the monotonic order of attribute values. Thus, first, we study the problem crowdsourcing Pareto-optimal object finding, in the case where objects do not have explicit attributes and preference relations on objects are strict partial orders. Then, we initiate the research into the novel problem of skyline discovery over hidden web databases, which enables a wide variety of innovative third-party applications over one or multiple web databases. A major problem with the ranking queries representatives, i.e., skyline and convex hull, is that as in real-world applications the representative can be a significant portion of the data, its performance in the ranking query processing is greatly reduced. Thus, computing a subset limited to r tuples that minimize the user’s dissatisfaction with the result from the limited set is of interest. We make several fundamental theoretical as well as practical advances in developing such a compact set. Finally, considering the limitations of top-k indices, while focusing on the client-server databases, we propose query reranking third-party service that uses public interface of the database to enable the on-the-fly processing of ranking queries.


Top-k query processing, Skyline, Convex hull, Regret-ratio minimizing set, Query reranking, Web databases, Ranked retrieval model


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington