A Query Processing Approach Based on Window Semantics in Data Stream System

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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Topological operators: a relaxed query processing approach

GeoInformatica ◽

10.1007/s10707-011-0124-9 ◽

2011 ◽

Vol 16 (1) ◽

pp. 67-110 ◽

Cited By ~ 2

Author(s):

Alberto Belussi ◽

Barbara Catania ◽

Paola Podestà

Keyword(s):

Query Processing ◽

Processing Approach

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Correction to: Semantic annotation of summarized sensor data stream for effective query processing

The Journal of Supercomputing ◽

10.1007/s11227-017-2212-6 ◽

2018 ◽

Vol 76 (6) ◽

pp. 4040-4040

Author(s):

Shobharani Pacha ◽

Suresh Ramalingam Murugan ◽

R. Sethukarasi

Keyword(s):

Query Processing ◽

Data Stream ◽

Semantic Annotation ◽

Sensor Data

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Distributed Publish/Subscribe Query Processing on the Spatio-Textual Data Stream

2017 IEEE 33rd International Conference on Data Engineering (ICDE) ◽

10.1109/icde.2017.154 ◽

2017 ◽

Cited By ~ 21

Author(s):

Zhida Chen ◽

Gao Cong ◽

Zhenjie Zhang ◽

Tom Z.J. Fuz ◽

Lisi Chen

Keyword(s):

Query Processing ◽

Data Stream ◽

Textual Data

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A Method for Processing Top-k Continuous Query on Uncertain Data Stream in Sliding Window Model

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL ◽

10.37394/23203.2021.16.22 ◽

2021 ◽

Vol 16 ◽

pp. 261-269

Author(s):

Raja Azhan Syah Raja Wahab ◽

Siti Nurulain Mohd Rum ◽

Hamidah Ibrahim ◽

Fatimah Sidi ◽

Iskandar Ishak

Keyword(s):

Query Processing ◽

Data Streams ◽

Data Stream ◽

Uncertain Data ◽

Research Work ◽

Computational Cost ◽

Sliding Window ◽

Possible World ◽

Processing Methods ◽

Uncertain Data Streams

The data stream is a series of data generated at sequential time from different sources. Processing such data is very important in many contemporary applications such as sensor networks, RFID technology, mobile computing and many more. The huge amount data generated and frequent changes in a short time makes the conventional processing methods insufficient. The Sliding Window Model (SWM) was introduced by Datar et. al to handle this problem. Avoiding multiple scans of the whole data sets, optimizing memory usage, and processing only the most recent tuple are the main challenges. The number of possible world instances grows exponentially in uncertain data and it is highly difficult to comprehend what it takes to meet Top-k query processing in the shortest amount of time. Following the generation of rules and the probability theory of this model, a framework was anticipated to sustain top-k processing algorithm over the SWM approach until the candidates expired. Based on the literature review study, none of the existing work have been made to tackle the issue arises from the top-k query processing of the possible world instance of the uncertain data streams within the SWM. The major issue resulted from these scenarios need to be addressed especially in the computation redundancy area that contributed to the increases of computational cost within the SWM. Therefore, the main objective of this research work is to propose the top-k query processing methods over uncertain data streams in SWM utilizing the score and the Possible World (PW) setting. In this study, a novel expiration and object indexing method is introduced to address the computational redundancy issues. We believed the proposed method can reduce computational costs and by managing insertion and exit policy on the right tuple candidates within a specified window frame. This research work will contribute to the area of computational query processing.

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