Efficient processing of multiple continuous skyline queries over a data stream

2013 ◽  
Vol 221 ◽  
pp. 316-337 ◽  
Author(s):  
Yu Won Lee ◽  
Ki Yong Lee ◽  
Myoung Ho Kim
Keyword(s):  
Data Stream ◽  
Skyline Queries ◽  
Efficient Processing

Efficient processing of distance–time kth-order skyline queries in bicriteria networks

2019 ◽  
Vol 13 (5) ◽  
pp. 796-802
Author(s):  
Jiping Zheng ◽  
Shunqing Jiang ◽  
Jialiang Chen ◽  
Wei Yu
Keyword(s):  
Skyline Queries ◽  
Efficient Processing

scholarly journals Efficient Processing of ContinuousSkylineQuery over Smarter Traffic Data Stream for Cloud Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Wang Hanning ◽  
Xu Weixiang ◽  
Jiulin Yang ◽  
Lili Wei ◽  
Jia Chaolong
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Data Stream ◽  
Sliding Window ◽  
Continuous Query ◽  
Traffic Data ◽  
Stream Data ◽  
Smart Transportation ◽  
Efficient Processing ◽  
Query Algorithm

The analyzing and processing of multisource real-time transportation data stream lay a foundation for the smart transportation's sensibility, interconnection, integration, and real-time decision making. Strong computing ability and valid mass data management mode provided by the cloud computing, is feasible for handlingSkylinecontinuous query in the mass distributed uncertain transportation data stream. In this paper, we gave architecture of layered smart transportation about data processing, and we formalized the description about continuous query over smart transportation dataSkyline. Besides, we proposedmMR-SUDSalgorithm (Skylinequery algorithm of uncertain transportation stream data based onmicro-batchinMap Reduce) based on sliding window division and architecture.

scholarly journals A Review on Big Data Stream Processing Applications: Contributions, Benefits, and Limitations

2021 ◽  
Vol 5 (4) ◽  
pp. 456
Author(s):  
Shaimaa Safaa Ahmed Alwaisi ◽  
Maan Nawaf Abbood ◽  
Luma Fayeq Jalil ◽  
Shahreen Kasim ◽  
Mohd Farhan Mohd Fudzee ◽  
...  
Keyword(s):  
Big Data ◽  
Data Stream ◽  
Learning Algorithm ◽  
Stream Processing ◽  
Streaming Data ◽  
Data Streaming ◽  
Data Stream Processing ◽  
Efficient Processing ◽  
Suitable Framework ◽  
Online Streaming

The amount of data in our world has been rapidly keep growing from time to time.  In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.

Efficient processing of top k group skyline queries

2019 ◽  
Vol 182 ◽  
pp. 104795
Author(s):  
Zhibang Yang ◽  
Xu Zhou ◽  
Kenli Li ◽  
Guoqing Xiao ◽  
Yunjun Gao ◽  
...  
Keyword(s):  
Skyline Queries ◽  
Efficient Processing
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