Data stream management
著者
書誌事項
Data stream management
（Synthesis lectures on data management, #5）
Morgan & Claypool, c2010
- : pbk
大学図書館所蔵 件 / 全2件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Bibliography: p. 49-64
内容説明・目次
内容説明
Many applications process high volumes of streaming data, among them Internet traffic analysis, financial tickers, and transaction log mining. In general, a data stream is an unbounded data set that is produced incrementally over time, rather than being available in full before its processing begins.
In this lecture, we give an overview of recent research in stream processing, ranging from answering simple queries on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis.
We will discuss two types of systems for end-to-end stream processing: Data Stream Management Systems (DSMSs) and Streaming Data Warehouses (SDWs). A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the real-time response of a DSMS by loading new data as soon as they arrive with a data warehouse's ability to manage Terabytes of historical data on secondary storage.
目次
Introduction
Data Stream Management Systems
Streaming Data Warehouses
Conclusions
「Nielsen BookData」 より