Research on Multi-Attribute Sequence Query Processing Techniques over Data Streams

Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this paper, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

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Research on Sequence Query Processing Techniques over Data Streams

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.3507 ◽

2013 ◽

Vol 284-287 ◽

pp. 3507-3511 ◽

Cited By ~ 5

Author(s):

Edgar Chia Han Lin

Keyword(s):

Query Processing ◽

Data Streams ◽

Management System ◽

Data Stream ◽

Sensor Data ◽

Data Management System ◽

Content Filtering ◽

Data Stream Management ◽

Stream Management ◽

Great Progress

Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this project will study the issues related to two types of data. First, we will focus on the content filtering on single-attribute streams, such as sensor data. Second, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

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Spatial Query Processing on AIS Data Streams in Data Stream Management Systems

Business Information Systems Workshops - Lecture Notes in Business Information Processing ◽

10.1007/978-3-030-36691-9_39 ◽

2019 ◽

pp. 461-472

Author(s):

Tobias Brandt ◽

Marco Grawunder

Keyword(s):

Query Processing ◽

Data Streams ◽

Data Stream ◽

Management Systems ◽

Spatial Query ◽

Data Stream Management ◽

Spatial Query Processing ◽

Stream Management ◽

Data Stream Management Systems

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Content-based load shedding in multimedia data stream management system

Foundations of Computing and Decision Sciences ◽

10.2478/v10209-011-0007-8 ◽

2012 ◽

Vol 37 (2) ◽

pp. 79-95

Author(s):

Rafal Maison ◽

Maciej Zakrzewicz

Keyword(s):

Data Streams ◽

Management System ◽

Data Stream ◽

Public Safety ◽

Load Shedding ◽

Multimedia Data ◽

Data Stream Management ◽

Stream Management ◽

Overload Management ◽

Data Stream Management System

Abstract.Overload management has become very important in public safety systems that analyse high performance multimedia data streams, especially in the case of detection of terrorist and criminal dangers. Efficient overload management improves the accuracy of automatic identification of persons suspected of terrorist or criminal activity without requiring interaction with them. We argue that in order to improve the quality of multimedia data stream processing in the public safety arena, the innovative concept of a Multimedia Data Stream Management System (MMDSMS) using load-shedding techniques should be introduced into the infrastructure to monitor and optimize the execution of multimedia data stream queries. In this paper, we present a novel content-centered load shedding framework, based on searching and matching algorithms, for analysing video tuples arriving within multimedia data streams. The framework tracks and registers all symptoms of overload, and either prevents overload before it occurs, or minimizes its effects. We have extended our Continuous Query Language (CQL) syntax to enable this load shedding technique. The effectiveness of the framework has been verified using both artificial and real data video streams collected from monitoring devices.

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