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. 1

CQL, a Continuous Query Language, is supported by the STREAM prototype Data Stream Management System at Stanford. CQL is an expressive SQL-based declarative language for registering continuous queries against streams and updatable relations. We begin by presenting an abstract semantics that relies only on "black box" mappings among streams and relations.

Traditional databases store sets of relatively static records with no pre-defined notion of time, unless timestamp attributes are explicitly added. While this model adequately represents commercial catalogues or repositories of personal information, many current and emerging applications require support for online analysis of rapidly changing data streams. Limitations of traditional DBMSs in supporting streaming applications have been recognized, prompting research to augment existing technologies and build new systems to manage streaming data. The purpose of this paper is to review recent work in data stream management systems, with an emphasis on application requirements, data models, continuous query languages, and query evaluation.

Relational query optimizers have traditionally relied upon table cardinalities when estimating the cost of the query plans they consider. While this approach has been and continues to be successful, the advent of the Internet and the need to execute queries over streaming sources requires a different approach, since for streaming inputs the cardinality may not be known or may not even be knowable (as is the case for an unbounded stream.) In view of this, we propose shifting from a cardinality-based approach to a rate-based approach, and give an optimization framework that aims at maximizing the output rate of query evaluation plans. This approach can be applied to cases where the cardinality-based approach cannot be used. It may also be useful for cases where cardinalities are known, because by focusing on rates we are able not only to optimize the time at which the last result tuple appears, but also to optimize for the number of answers computed at any specified time after the query evaluation commences. We present a preliminary validation of our rate-based optimization framework on a prototype XML query engine, though it is generic enough to be used in other database contexts. The results show that rate-based optimization is feasible and can indeed yield correct decisions.

This paper describes our ongoing work developing the Stanford Stream Data Manager (STREAM), a system for executing continuous queries over multiple continuous data streams. The STREAM system supports a declarative query language, and it copes with high data rates and query workloads by providing approximate answers when resources are limited. This paper describes specific contributions made so far and enumerates our next steps in developing a general-purpose Data Stream Management System.

We consider the problem of approximating sliding window joins over data streams in a data stream processing system with limited resources. In our model, we deal with resource constraints by shedding load in the form of dropping tuples from the data streams. We first discuss alternate architectural models for data stream join processing, and we survey suitable measures for the quality of an approximation of a set-valued query result. We then consider the number of generated result tuples as the quality measure, and we give optimal offline and fast online algorithms for it. In a thorough experimental study with synthetic and real data we show the efficacy of our solutions. For applications with demand for exact results we introduce a new Archive-metric which captures the amount of work needed to complete the join in case the streams are archived for later processing.

We study sliding window multi-join processing in continuous queries over data streams. Several algorithms are reported for performing continuous, incremental joins, under the assumption that all the sliding windows fit in main memory. The algorithms include multiway incremental nested loop joins (NLJs) and multi-way incremental hash joins. We also propose join ordering heuristics to minimize the processing cost per unit time. We test a possible implementation of these algorithms and show that, as expected, hash joins are faster than NLJs for performing equi-joins, and that the overall processing cost is influenced by the strategies used to remove expired tuples from the sliding windows. 1

This paper describes our ongoing work developing the Stanford Stream Data Manager (STREAM), a system for executing continuous queries over multiple continuous data streams. The STREAM system supports a declarative query language, and it copes with high data rates and query workloads by providing approximate answers when resources are limited. This paper describes specific contributions made so far and enumerates our next steps in developing a general-purpose Data Stream Management System.

The XML Stream Machine (XSM) system is a novel XQuery processing paradigm that is tuned to the efficient processing of sequentially accessed XML data (streams). The system compiles a given XQuery into an XSM, which is an XML stream transducer, i.e., an abstract device that takes as input one or more XML data streams and produces one or more output streams, potentially using internal buffers. We present a systematic way to translate XQueries into efficient XSMs: First the XQuery is translated into a network of XSMs that correspond to the basic operators of the XQuery language and exchange streams. The network is reduced to a single XSM by repeated application of an XSM composition operation that is optimized to reduce the number of tests and actions that the XSM performs as well as the number of intermediate buffers that it uses. Finally, the optimized XSM is compiled into a C program. First empirical results illustrate the performance benefits of the XSM-based processor.

Abstract—As most current query processing architectures are already pipelined, it seems logical to apply them to data streams. However, two classes of query operators are impractical for processing long or infinite data streams. Unbounded stateful operators maintain state with no upper bound in size and, so, run out of memory. Blocking operators read an entire input before emitting a single output and, so, might never produce a result. We believe that a priori knowledge of a data stream can permit the use of such operators in some cases. We discuss a kind of stream semantics called punctuated streams. Punctuations in a stream mark the end of substreams allowing us to view an infinite stream as a mixture of finite streams. We introduce three kinds of invariants to specify the proper behavior of operators in the presence of punctuation. Pass invariants define when results can be passed on. Keep invariants define what must be kept in local state to continue successful operation. Propagation invariants define when punctuation can be passed on. We report on our initial implementation and show a strategy for proving implementations of these invariants are faithful to their relational counterparts. Index Terms—Continuous queries, stream semantics, continuous data streams, query operators, stream iterators. 1