Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 92-96). Also available in electronic version. Access restricted to campus users.

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 58-61).

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves 85-86).

Not available / text

Pointer analysis is a fundamental enabling technology for program analysis. By improving the scalability of precise pointer analysis we can make a positive impact across a wide range of program analyses used for many different purposes, including program verification and model checking, optimization and parallelization, program understanding, hardware synthesis, and more. In this thesis we present a suite of new algorithms aimed at improving pointer analysis scalability. These new algorithms make inclusion-based analysis (the most precise flow- and context-insensitive pointer analysis) over 4x faster while using 7x less memory than the previous state-of-the-art; they also enable flow-sensitive pointer analysis to handle programs with millions of lines of code, two orders of magnitude greater than the previous state-of-the-art. We present a formal framework for describing the space of pointer analysis approximations. The space of possible approximations is complex and multidimensional, and until now has not been well-defined in a formal manner. We believe that the framework is useful as a method to meaningfully compare the precision of the multitude of existing pointer analyses, as well as aiding in the systematic exploration of the entire space of approximations. / text

Earth Mover's Distance (EMD), as a similarity measure, has received a lot of attention in the fields of multimedia and probabilistic databases, computer vision, image retrieval, machine learning, etc. EMD on multidimensional histograms provides better distinguishability between the objects approximated by the histograms (e.g., images), compared to classic measures like Euclidean distance. Despite its usefulness, EMD has a high computational cost; therefore, a number of effective filtering methods have been proposed, to reduce the pairs of histograms for which the exact EMD has to be computed, during similarity search. Still, EMD calculations in the refinement step remain the bottleneck of the whole similarity search process. In this thesis, we focus on optimizing the refinement phase of EMD-based similarity search by (i) adapting an efficient min-cost flow algorithm (SIA) for the EMD computation, (ii) proposing a dynamic distance bound, which is progressively updated and tightened during the refinement process and can be used to terminate an EMD refinement early, and (iii) proposing a dynamic refinement order for the candidates which, paired with a concurrent EMD refinement strategy, reduces the amount of needless computations. Our proposed techniques are orthogonal to and can be easily integrated with the state-of-the-art filtering techniques, reducing the cost of EMD-based similarity queries by orders of magnitude. / published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / abstract / toc / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy