Michael D. Morse

An efficient and accurate method for evaluating time series similarity

A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria is bounded by a specified ε threshold value, such as the LCSS and the EDR techniques, have been shown to be robust in the presence of noise, time shifts, and data scaling. Our work proposes a new algorithm, called the Fast Time Series Evaluation (FTSE) method, which can be used to evaluate such threshold value techniques, including LCSS and EDR. Using FTSE, we show that these techniques can be evaluated faster than using either traditional dynamic programming or even warp-restricting methods such as the Sakoe-Chiba band and the Itakura Parallelogram. We also show that FTSE can be used in a framework that can evaluate a richer range of ε threshold-based scoring techniques, of which EDR and LCSS are just two examples. This framework, called Swale, extends the ε threshold-based scoring techniques to include arbitrary match rewards and gap penalties. Through extensive empirical evaluation, we show that Swale can obtain greater accuracy than existing methods.

Efficient continuous skyline computation

In a number of emerging streaming applications, the data values that are produced have an associated time interval for which they are valid. A useful computation over such streaming data sets is to produce a continuous and valid skyline summary. To the best of our knowledge, this problem has not been addressed before. In this paper we introduce an operator called the continuous time-interval skyline operator for evaluating this computation. We also present a new algorithm called LookOut for evaluating the continuous time-interval skyline efficiently, and empirically demonstrate the scalability of this algorithm.

Efficient Continuous Skyline Computation

In a number of emerging streaming applications, the data values that are produced have an associated time interval for which they are valid. A useful computation over such streaming data sets is to produce a continuous and valid skyline summary. To the best of our knowledge, this problem has not been addressed before. In this paper we introduce an operator called the continuous time-interval skyline operator for evaluating this computation. We also present a new algorithm called LookOut for evaluating the continuous time-interval skyline efficiently, and empirically demonstrate the scalability of this algorithm.

Evaluating skylines in the presence of equijoins

When a database system is extended with the skyline operator, it is important to determine the most efficient way to execute a skyline query across tables with join operations. This paper describes a framework for evaluating skylines in the presence of equijoins, including: (1) the development of algorithms to answer such queries over large input tables in a non-blocking, pipeline fashion, which significantly speeds up the entire query evaluation time. These algorithms are built on top of the traditional relational Nested-Loop and the Sort-Merge join algorithms, which allows easy implementation of these methods in existing relational systems; (2) a novel method for estimating the skyline selectivity of the joined table; (3) evaluation of skyline computation based on the estimation method and the proposed evaluation techniques; and (4) a systematic experimental evaluation to validate our skyline evaluation framework.

Efficient evaluation of radial queries using the target tree

In this paper, we propose a novel indexing structure, called the target tree, which is designed to efficiently answer a new type of spatial query, called a radial query. A radial query seeks to find all objects in the spatial data set that intersect with line segments emanating from a single, designated target point. Many existing and emerging biomedical applications use radial queries, including surgical planning in neurosurgery. Traditional spatial indexing structures such as the R*-tree and quadtree perform poorly on such radial queries. A target tree uses a regular hierarchical decomposition of space using wedge shapes that emanate from the target point, resulting in an index structure that is very efficient for evaluating radial queries. We present a detailed performance evaluation of the target tree, comparing with the R*-tree and quadtree indexing methods, and show that the target tree method outperforms these existing methods by at least a factor of 2-10.

Efficient Evaluation of Radial Queries using the Target Tree

In this paper, we propose a novel indexing structure, called the target tree, which is designed to efficiently answer a new type of spatial query, called a radial query. A radial query seeks to find all objects in the spatial data set that intersect with line segments emanating from a single, designated target point. Many existing and emerging biomedical applications use radial queries, including surgical planning in neurosurgery. Traditional spatial indexing structures such as the R*-tree and quadtree perform poorly on such radial queries. A target tree uses a regular hierarchical decomposition of space using wedge shapes that emanate from the target point, resulting in an index structure that is very efficient for evaluating radial queries. We present a detailed performance evaluation of the target tree, comparing with the R*-tree and quadtree indexing methods, and show that the target tree method outperforms these existing methods by at least a factor of 2-10.