Data management

About: Data management is a research topic. Over the lifetime, 31574 publications have been published within this topic receiving 424326 citations.

STREAM: The Stanford Data Stream Management System

Abstract: Traditional database management systems are best equipped to run one-time queries over finite stored data sets. However, many modern applications such as network monitoring, financial analysis, manufacturing, and sensor networks require long-running, or continuous, queries over continuous unbounded streams of data. In the STREAM project at Stanford, we are investigating data management and query processing for this class of applications. As part of the project we are building a general-purpose prototype Data Stream Management System (DSMS), also called STREAM, that supports a large class of declarative continuous queries over continuous streams and traditional stored data sets. The STREAM prototype targets environments where streams may be rapid, stream characteristics and query loads may vary over time, and system resources may be limited.

Retrieving and Integrating Data from Multiple Information Sources

Abstract: With the current explosion of data, retrieving and integrating information from various sources is a critical problem. Work in multidatabase systems has begun to address this problem, but it has primarily focused on methods for communicating between databases and requires significant effort for each new database added to the system. This paper describes a more general approach that exploits a semantic model of a problem domain to integrate the information from various information sources. The information sources handled include both databases and knowledge bases, and other information sources (e.g. programs) could potentially be incorporated into the system. This paper describes how both the domain and the information sources are modeled, shows how a query at the domain level is mapped into a set of queries to individual information sources, and presents algorithms for automatically improving the efficiency of queries using knowledge about both the domain and the information sources. This work is implemented in a system called SIMS and has been tested in a transportation planning domain using nine Oracle databases and a Loom knowledge base.

Building Successful Knowledge Management Projects

Abstract: It is widely acknowledged that developed economies have gradually been transformed over the past fifty years. Scholars and observers from disciplines as disparate as sociology, economics, and management science generally agree that knowledge has been at the center of this change. 1 Knowledge could be defined as information that has been combined with experience, context, interpretation, and reflection. Given the value of this asset to organizations, it is not surprising that greater attention is being paid to the subject of knowledge: what it is, how it differs from the related concepts of information and data, and how to begin to create, transfer, and use it more effectively. The subject of knowledge management, in particular, has had a recent flowering. 2

Where did knowledge management come from

Abstract: In this essay I look at the history of knowledge management and offer insights into what knowledge management means today and where it may be headed in the future. This is an updated version of an article first published in Knowledge Directions, the journal of the Institute for Knowledge Management, fall 1999.

The HiPAC project: combining active databases and timing constraints

Abstract: The HiPAC (High Performance ACtive database system) project addresses two critical problems in time-constrained data management: the handling of timing constraints in databases, and the avoidance of wasteful polling through the use of situation-action rules that are an integral part of the database and are monitored by DBMS's condition monitor. A rich knowledge model provides the necessary primitives for definition of timing constraints, situation-action rules, and precipitating events. The execution model allows various coupling modes between transactions, situation evaluations and actions, and provides the framework for correct concurrent execution of transactions and triggered actions. Different approaches to scheduling of time-constrained tasks and transactions are explored and an architecture is being designed with special emphasis on the interaction of the time-constrained, active DBMS and the operating system. Performance models are developed to evaluate the various design alternatives.