Range Aggregation for Multi-dimensional Data Streams
Project: Research
Researcher(s)
- Chung Keung POON (Principal Investigator / Project Coordinator)Department of Computer Science
Description
Data stream processing has received much attention recently due to its application in many areas such as IP network monitoring, aggregation in sensor networks, web-log mining, financial monitoring, database query optimization, and so on. Because of the rapid rate of data generation, algorithms for such applications can only examine the input elements once and use a small amount of memory compared with the volume of data. Moreover, the processing of each incoming element has to be fast. In many applications, the data stream consists of points in a multi-dimensional space. For example, the stream of IP packets passing through a network router can be modelled as a stream of two-dimensional points defined by their source and destination IP addresses. Various range queries on multi-dimensional data are useful for analyzing the data and have been well-studied in traditional computation models. In this project, the researchers focus on supporting such important queries on the data stream model. In particular, they propose to investigate the design of algorithms for the following range query problems on multi-dimensional data streams: (i) range frequency moments, (ii) range sum queries, and (iii) range max/min queries.Detail(s)
Project number | 9041252 |
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Grant type | GRF |
Status | Finished |
Effective start/end date | 1/01/08 → 2/09/11 |