Expressions as Data in Relational Data Base Management Systems

Numerous applications, such as publish/subscribe, website personalization, applications involving continuous queries, etc., require that user.s interest be persistently maintained and matched with the expected data. Conditional Expressions can be used to maintain user interests. This thesis focuses on the support for expression data type in relational database system, allowing storing of conditional expressions as .data. in columns of database tables and evaluating those expressions using an EVALUATE operator. With this context, expressions can be interpreted as descriptions, queries, and filters, and this significantly broadens the use of a relational database system to support new types of applications. The thesis presents an overview of the expression data type, storing the expressions, evaluating the stored expressions and shows how these applications can be easily supported with improved functionality. A sample application is also explained in order to show the importance of expressions in application context, with a comparison of the application with and without expressions

A PROCRUSTEAN APPROACH TO STREAM PROCESSING

The increasing demand for real-time data processing and the constantly growing data volume have contributed to the rapid evolution of Stream Processing Engines (SPEs), which are designed to continuously process data as it arrives. Low operational cost and timely delivery of results are both objectives of paramount importance for SPEs. Given the volatile and uncharted nature of data streams, achieving the aforementioned goals under fixed resources is a challenge. This calls for adaptable SPEs, which can react to fluctuations in processing demands. In the past, three techniques have been developed for improving an SPE’s ability to adapt. Those techniques are classified based on applications’ requirements on exact or approximate results: stream partitioning, and re-partitioning target exact, and load shedding targets approximate processing. Stream partitioning strives to balance load among processors, and previous techniques neglected hidden costs of distributed execution. Load Shedding lowers the accuracy of results by dropping part of the input, and previous techniques did not cope with evolving streams. Stream re-partitioning is used to reconfigure execution while processing takes place, and previous techniques did not fully utilize window semantics. In this dissertation, we put stream processing in a procrustean bed, in terms of the manner and the degree that processing takes place. To this end, we present new approaches, for window-based aggregate operators, which are applicable to both exact and approximate stream processing in modern SPEs. Our stream partitioning, re-partitioning, and load shedding solutions offer improvements in performance and accuracy on real-world data by exploiting the semantics of both data and operations. In addition, we present SPEAr, the design of an SPE that accelerates processing by delivering approximate results with accuracy guarantees and avoiding unnecessary load. Finally, we contribute a hybrid technique, ShedPart, which can further improve load balance and performance of an SPE

Differential evaluation of continual queries

SIGLEAvailable from British Library Document Supply Centre-DSC:7769.555(2001/11) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Differential Evaluation of Continual Queries

Information Superhighway environments such as the Internet have brought us ready access to large amount of information. However, Internet data is notoriously unorganized and autonomously managed in a distributed fashion. Large scale information monitoring in the Internet environment requires support beyond traditional database techniques. Two of the key issues are the increasing reward in monitoring a fast growing information base and the similarly increasing processing cost. To improve the expressiveness of queries for information monitoring, we define continual queries as a useful tool for monitoring of updated information. Continual queries are standing queries that monitor the source data and notify the users whenever new data matches the query. In addition to periodic refresh, continual queries include Epsilon Transaction concepts to allow users to specify query refresh based on the magnitude of updates. To support efficient processing of continual queries, we propose a different..