Efficient Algorithms for Similarity and Skyline Summary on Multidimensional Datasets

Efficient management of large multidimensional datasets has attracted much attention in the database research community. Such large multidimensional datasets are common and efficient algorithms are needed for analyzing these data sets for a variety of applications. In this thesis, we focus our study on two very common classes of analysis: similarity and skyline summarization. We first focus on similarity when one of the dimensions in the multidimensional dataset is temporal. We then develop algorithms for evaluating skyline summaries effectively for both temporal and low-cardinality attribute domain datasets and propose different methods for improving the effectiveness of the skyline summary operation. This thesis begins by studying similarity measures for time-series datasets and efficient algorithms for time-series similarity evaluation. The first contribution of this thesis is a new algorithm, called the Fast Time Series Evaluation (FTSE) method, which can be used to evaluate similarity methods whose matching criteria is bounded by a specified ǫ threshold value. We then show that FTSE can be used in a framework that can evaluate a rich range of ǫ threshold-based scoring techniques which we call the Sequence Weighted Alignment (Swale) method. The second contribution of this thesis is the development of a new time-interval skyline operator, which continuously computes the current skyline over a data stream. We present a new algorithm called LookOut for evaluating such queries efficiently, and empirically demonstrate the scalability of this algorithm. In addition, we also examine the effect of