predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering by recording their reactions to documents they read. The reactions are called annotations; they can be accessed by other people’s filters. Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser. Tapestry’s client/server architecture, its various components, and the Tapestry query language are described.

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

Continuous queries are persistent queries that allow users to receive new results when they become available. While continuous query systems can transform a passive web into an active environment, they need to be able to support millions of queries due to the scale of the Internet. No existing systems have achieved this level of scalability. NiagaraCQ addresses this problem by grouping continuous queries based on the observation that many web queries share similar structures. Grouped queries can share the common computation, tend to fit in memory and can reduce the I/O cost significantly. Furthermore, grouping on selection predicates can eliminate a large number of unnecessary query invocations. Our grouping technique is distinguished from previous group optimization approaches in the following ways. First, we use an incremental group optimization strategy with dynamic re-grouping. New queries are added to existing query groups, without having to regroup already installed queries. Second, we use a query-split scheme that requires minimal changes to a general-purpose query engine. Third, NiagaraCQ groups both change-based and timer-based queries in a uniform way. To insure that NiagaraCQ is scalable, we have also employed other techniques including incremental evaluation of continuous queries, use of both pull and push models for detecting heterogeneous data source changes, and memory caching. This paper presents the design of NiagaraCQ system and gives some experimental results on the system’s performance and scalability. 1.

Increasingly pervasive networks are leading towards a world where data is constantly in motion. In such a world, conventional techniques for query processing, which were developed under the assumption of a far more static and predictable computational environment, will not be sufficient. Instead, query processors based on adaptive dataflow will be necessary. The Telegraph project has developed a suite of novel technologies for continuously adaptive query processing. The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams. In this paper, we describe the system architecture and its underlying technology, and report on our ongoing implementation effort, which leverages the PostgreSQL open source code base. We also discuss open issues and our research agenda.

Information Dissemination applications are gaining increasing popularity due to dramatic improvements in communications bandwidth and ubiquity. The sheer volume of data available necessitates the use of selective approaches to dissemination in order to avoid overwhelming users with unnecessaryinformation. Existing mechanisms for selective dissemination typically rely on simple keyword matching or "bag of words" information retrieval techniques. The advent of XML as a standard for information exchangeand the development of query languages for XML data enables the development of more sophisticated filtering mechanisms that take structure information into account. We have developed several index organizations and search algorithms for performing efficient filtering of XML documents for large-scale information dissemination systems. In this paper we describe these techniques and examine their performance across a range of document, workload, and scale scenarios. 1

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.

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.

We consider wireless broadcasting of data as a way of disseminating information to a massive number of users. Organizing and accessing information on wireless communication channels is different from the problem of organizing and accessing data on the disk. We describe two methods, (1; m) Indexing and Distributed Indexing, for organizing and accessing broadcast data. We demonstrate that the proposed algorithms lead to significant improvement of battery life, while retaining a low access time. 1 Introduction The physical requirements of wireless communication channels, make the problem of organizing wireless broadcast data different from data organization on the disk. Index based organization of data transmitted over wireless channels, is very important from the power conservation point of view and can result in significant improvement in battery utilization. New technology can utilize and build upon some well known techniques for file organization and access. These traditional solutio...

In this paper we introduce the concept of continual queries, describe the design of a distributed event-driven continual query system -- OpenCQ, and outline the initial implementation of OpenCQ on top of the distributed interoperable information mediation system DIOM [21, 19]. Continual queries are standing queries that monitor update of interest and return results whenever the update reaches specified thresholds. In OpenCQ, users may specify to the system the information they would like to monitor (such as the events or the update thresholds they are interested in). Whenever the information of interest becomes available, the system immediately delivers it to the relevant users; otherwise, the system continually monitors the arrival of the desired information and pushes it to the relevant users as it meets the specified update thresholds. In contrast to conventional pull-based data management systems such as DBMSs and Web search engines, OpenCQ exhibits two important featu...

Mobile computing is a new emerging computing paradigm posing many challenging data management problems. We identify these new challenges and investigate their technical significance. New research problems include management of location dependent data, information services to mobile users, frequent disconnections, wireless data broadcasting, and energy efficient data access. 1 Introduction The rapidly expanding technology of cellular communications, wireless LAN, and satellite services will make it possible for mobile users to access information anywhere and at anytime. In the near future, tens of millions of users will be carrying a portable computer, often called a personal digital assistant or a personal communicator. Smaller units will run on AA batteries and may be diskless; larger units will run on Ni-Cd packs. These larger units will be powerful laptop computers with large memories and powerful processors. Regardless of size, all mobile computers will be equipped with a wireless...