Query Workload-Aware Index Structures for Range Searches in 1D, 2D, and High-Dimensional Spaces

abstract: Most current database management systems are optimized for single query execution. Yet, often, queries come as part of a query workload. Therefore, there is a need for index structures that can take into consideration existence of multiple queries in a query workload and efficiently produce accurate results for the entire query workload. These index structures should be scalable to handle large amounts of data as well as large query workloads. The main objective of this dissertation is to create and design scalable index structures that are optimized for range query workloads. Range queries are an important type of queries with wide-ranging applications. There are no existing index structures that are optimized for efficient execution of range query workloads. There are also unique challenges that need to be addressed for range queries in 1D, 2D, and high-dimensional spaces. In this work, I introduce novel cost models, index selection algorithms, and storage mechanisms that can tackle these challenges and efficiently process a given range query workload in 1D, 2D, and high-dimensional spaces. In particular, I introduce the index structures, HCS (for 1D spaces), cSHB (for 2D spaces), and PSLSH (for high-dimensional spaces) that are designed specifically to efficiently handle range query workload and the unique challenges arising from their respective spaces. I experimentally show the effectiveness of the above proposed index structures by comparing with state-of-the-art techniques.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Efficient Execution of Multiple Query Workloads in Data Analysis Applications

Applications that analyze, mine, and visualize large datasets is considered an important class of applications in many areas of science, engineering and business. Queries commonly executed in data analysis applications often involve user-defined processing of data and application-specific data structures. If data analysis is employed in a collaborative environment, the data server should execute multiple such queries simultaneously to minimize the response time to the clients of the data analysis application. In a multi-client environment, there may be a large number of overlapping regions of interest and common processing requirements among the clients. Thus, better performance can be achieved if commonalities among multiple queries can be exploited. In this paper we present the design of a runtime system for executing multiple query workloads on a shared-memory machine. We describe initial experimental results using an application for browsing digitized microscopy images. (Cross-referenced as UMIACS-TR-2001-35

ABSTRACT Efficient Execution of Multiple Query Workloads in Data Analysis Applications ∗

Applications that analyze, mine, and visualize large datasets are considered an important class of applications in many areas of science, engineering, and business. Queries commonly executed in data analysis applications often involve user-defined processing of data and application-specific data structures. If data analysis is employed in a collaborative environment, the data server should execute multiple such queries simultaneously to minimize the response time to clients. In this paper we present the design of a runtime system for executing multiple query workloads on a sharedmemory machine. We describe experimental results using an application for browsing digitized microscopy images. 1