Data Intensive vs Sliding Window Outlier Detection in the Stream Data — An Experimental Approach

2016 ◽  
pp. 73-87 ◽  
Author(s):  
Mateusz Kalisch ◽  
Marcin Michalak ◽  
Marek Sikora ◽  
Łukasz Wróbel ◽  
Piotr Przystałka
Keyword(s):  
Outlier Detection ◽  
Experimental Approach ◽  
Sliding Window ◽  
Stream Data ◽  
Data Intensive

Outlier Detection and Elimination in Stream Data – An Experimental Approach

2016 ◽  
pp. 416-426 ◽  
Author(s):  
Mateusz Kalisch ◽  
Marcin Michalak ◽  
Piotr Przystałka ◽  
Marek Sikora ◽  
Łukasz Wróbel
Keyword(s):  
Outlier Detection ◽  
Experimental Approach ◽  
Stream Data

Clustering and Outlier Detection for Trajectory Stream Data

10.1142/11555 ◽  
2020 ◽  
Author(s):  
Jiali Mao ◽  
Cheqing Jin ◽  
Aoying Zhou
Keyword(s):  
Outlier Detection ◽  
Stream Data

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 Detecting Incremental Frequent Subgraph Patterns in IoT Environments

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4020 ◽  
Author(s):  
Kyoungsoo Bok ◽  
Jaeyun Jeong ◽  
Dojin Choi ◽  
Jaesoo Yoo
Keyword(s):  
Internet Of Things ◽  
Sliding Window ◽  
Performance Evaluations ◽  
Stream Data ◽  
Computation Cost ◽  
Frequent Subgraph ◽  
Subgraph Pattern ◽  
As Graph ◽  
Pattern Information

As graph stream data are continuously generated in Internet of Things (IoT) environments, many studies on the detection and analysis of changes in graphs have been conducted. In this paper, we propose a method that incrementally detects frequent subgraph patterns by using frequent subgraph pattern information generated in previous sliding window. To reduce the computation cost for subgraph patterns that occur consecutively in a graph stream, the proposed method determines whether subgraph patterns occur within a sliding window. In addition, subgraph patterns that are more meaningful can be detected by recognizing only the patterns that are connected to each other via edges as one pattern. In order to prove the superiority of the proposed method, various performance evaluations were conducted.

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

2020 ◽  
Vol 34 (1) ◽  
Author(s):  
Kumar G. Ranjan ◽  
Debesh S. Tripathy ◽  
B. Rajanarayan Prusty ◽  
Debashisha Jena
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Sliding Window
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