Continuous queries over data streams

TLDR

Continuous data streams are increasingly common, requiring a rethinking of data management and prompting new research directions that encompass prior work on continuous queries, triggers, and materialized views. The study aims to define and evaluate continuous queries over data streams, addressing semantic and efficiency challenges while outlining future research directions. We propose a general, flexible architecture for stream query processing that serves as a framework to clarify alternative semantics and processing techniques. The paper maps research topics in stream query processing, indicating relevant prior work and identifying remaining challenges.

Abstract

In many recent applications, data may take the form of continuous data streams, rather than finite stored data sets. Several aspects of data management need to be reconsidered in the presence of data streams, offering a new research direction for the database community. In this paper we focus primarily on the problem of query processing, specifically on how to define and evaluate continuous queries over data streams. We address semantic issues as well as efficiency concerns. Our main contributions are threefold. First, we specify a general and flexible architecture for query processing in the presence of data streams. Second, we use our basic architecture as a tool to clarify alternative semantics and processing techniques for continuous queries. The architecture also captures most previous work on continuous queries and data streams, as well as related concepts such as triggers and materialized views. Finally, we map out research topics in the area of query processing over data streams, showing where previous work is relevant and describing problems yet to be addressed.