Efficient Execution of Multiple Query Workloads in Data Analysis Applications

Abstract

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)