In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

No abstract

In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

In many recent applications, data may take the form of continuous data soeams, 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.

We consider the problem of pipelined filters, where a continuous stream of tuples is processed by a set of commutative filters. Pipelined filters are common in stream applications and capture a large class of multiway stream joins. We focus on the problem of ordering the filters adaptively to minimize processing cost in an environment where stream and filter characteristics vary unpredictably over time. Our core algorithm, A-Greedy (for Adaptive Greedy), has strong theoretical guarantees: If stream and filter characteristics were to stabilize, A-Greedy would converge to an ordering within a small constant factor of optimal. (In experiments A-Greedy usually converges to the optimal ordering.) One very important feature of A-Greedy is that it monitors and responds to selectivities that are correlated across filters (i.e., that are nonindependent), which provides the strong quality guarantee but incurs run-time overhead. We identify a three-way tradeoff among provable convergence to good orderings, run-time overhead, and speed of adaptivity. We develop a suite of variants of A-Greedy that lie at different points on this tradeoff spectrum. We have implemented all our algorithms in the STREAM prototype Data Stream Management System and a thorough performance evaluation is presented.