Jennifer Widom

Models and issues in data stream systems

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.

The Lorel Query Language for Semistructured Data

Lorel language, designed for querying semistructured data. Semistructured data is becoming more and more prevalent, e.g., in structured documents such as HTML and when performing simple integration of data from multiple sources. Traditional data models and query languages are inappropriate, since semistructured data often is irregular: some data is missing, similar concepts are represented using different types, heterogeneous sets are present, or object structure is not fully known. Lorel is a user-friendly language in the SQL/OQL style for querying such data effectively. For wide applicability, the simple object model underlying Lorel can be viewed as an extension of the ODMG data model and the Lorel language as an extension of OQL. The main novelties of the Lorel language are: (i) the extensive use of coercion to relieve the user from the strict typing of OQL, which is inappropriate for semistructured data; and (ii) powerful path expressions, which permit a flexible form of declarative navigational access and are particularly suitable when the details of the structure are not known to the user. Lorel also includes a declarative update language. Lorel is implemented as the query language of the Lore prototype database management system at Stanford. Information about Lore can be found at http://www-db.stanford.edu/lore. In addition to presenting the Lorel language in full, this paper briefly describes the Lore system and query processor. We also briefly discuss a second implementation of Lorel on top of a conventional object-oriented database management system, the O2 system.

The CQL continuous query language: semantic foundations and query execution

CQL, a continuous query language, is supported by the STREAM prototype data stream management system (DSMS) at Stanford. CQL is an expressive SQL-based declarative language for registering continuous queries against streams and stored relations. We begin by presenting an abstract semantics that relies only on “black-box” mappings among streams and relations. From these mappings we define a precise and general interpretation for continuous queries. CQL is an instantiation of our abstract semantics using SQL to map from relations to relations, window specifications derived from SQL-99 to map from streams to relations, and three new operators to map from relations to streams. Most of the CQL language is operational in the STREAM system. We present the structure of CQLs query execution plans as well as details of the most important components: operators, interoperator queues, synopses, and sharing of components among multiple operators and queries. Examples throughout the paper are drawn from the Linear Road benchmark recently proposed for DSMSs. We also curate a public repository of data stream applications that includes a wide variety of queries expressed in CQL. The relative ease of capturing these applications in CQL is one indicator that the language contains an appropriate set of constructs for data stream processing.

The TSIMMIS Approach to Mediation: Data Models and Languages

TSIMMIS—The Stanford-IBM Manager of Multiple InformationSources—is a system for integrating information. It offers a datamodel and a common query language that are designed to support thecombining of information from many different sources. It also offerstools for generating automatically the components that are needed tobuild systems for integrating information. In this paper we shalldiscuss the principal architectural features and their rationale.

Object exchange across heterogeneous information sources

We address the problem of providing integrated access to diverse and dynamic information sources. We explain how this problem differs from the traditional database integration problem and we focus on one aspect of the information integration problem, namely information exchange. We define an object-based information exchange model and a corresponding query language that we believe are well suited for integration of diverse information sources. We describe how, the model and language have been used to integrate heterogeneous bibliographic information sources. We also describe two general-purpose libraries we have implemented for object exchange between clients and servers.<<ETX>>

Continuous queries over data streams

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.

Research problems in data warehousing

The topic of data warehousing encompasses architectures, algorithms, and tools for bringing together selected data from multiple databases or other information sources into a single repository, called a data warehouse, suitable for direct querying or analysis. In recent years data warehousing has become a prominent buzzword in the database industry, but attention from the database research community has been limited. In this paper we motivate the concept of a data warehouse, we outline a general data warehousing architecture, and we propose a number of technical issues arising from the architecture that we believe are suitable topics for exploratory research.

Lore: a database management system for semistructured data

Lore (for Lightweight Object Repository) is a DBMS designed specifically for managing semistructured information. Implementing Lore has required rethinking all aspects of a DBMS, including storage management, indexing, query processing and optimization, and user interfaces. This paper provides an overview of these aspects of the Lore system, as well as other novel features such as dynamic structural summaries and seamless access to data from external sources.

Adaptive filters for continuous queries over distributed data streams

We consider an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data. Significant communication overhead is incurred in the presence of rapid update streams, and we propose a new technique for reducing the overhead. Users register continuous queries with precision requirements at the central stream processor, which installs filters at remote data sources. The filters adapt to changing conditions to minimize stream rates while guaranteeing that all continuous queries still receive the updates necessary to provide answers of adequate precision at all times. Our approach enables applications to trade precision for communication overhead at a fine granularity by individually adjusting the precision constraints of continuous queries over streams in a multi-query workload. Through experiments performed on synthetic data simulations and a real network monitoring implementation, we demonstrate the effectiveness of our approach in achieving low communication overhead compared with alternate approaches.

Clustering association rules

The authors consider the problem of clustering two-dimensional association rules in large databases. They present a geometric-based algorithm, BitOp, for performing the clustering, embedded within an association rule clustering system, ARCS. Association rule clustering is useful when the user desires to segment the data. They measure the quality of the segmentation generated by ARCS using the minimum description length (MDL) principle of encoding the clusters on several databases including noise and errors. Scale-up experiments show that ARCS, using the BitOp algorithm, scales linearly with the amount of data.