Paper Info
Title
PTAOD: A Novel Framework for Supporting Approximate Outlier Detection Over Streaming Data for Edge Computing.
Abstract
Outlier detection over sliding window is a fundamental problem in the domain of streaming data management, which has has been studied over 10 years. The key to supporting outlier detection is to construct a neighbour list for each object, which is used for predicting which objects may become outliers or are impossible to become outliers. However, existing work ignores the fact that, outliers amount is usually small, in which it is unnecessary to construct neighbour-list for all objects when they arrive in the window. It causes both high space and computational cost, which turns the solution infeasible for working under edge computation environment. In this paper, we propose a novel framework named PTAOD (Probabilistic Threshold-based Approximate Outlier Detection). Firstly, we propose an algorithm for evaluating the probability of a newly arrived object becoming an outlier before it expires from the window, using evaluating result for avoiding unnecessary candidate maintenance. In addition, we introduce a novel index namely ZHB-Tree (Z-order-based Hash B-Tree) to maintain streaming data. Last of all, we propose a novel algorithm to maintain candidate outliers. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.
Year
DOI
Venue
2020
10.1109/ACCESS.2019.2962066
IEEE ACCESS
Keywords
Field
DocType
Outlier detection,streaming data,probability guarantee,index
Edge computing,Data mining,Anomaly detection,Computer science,Streaming data,Distributed computing
Journal
Volume
ISSN
Citations 
8
2169-3536
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Rui Zhu1167.74
Tiantian Yu200.34
Zhiyuan Tan300.34
Wei Du400.34
Liang Zhao500.68
Jiajia Li631734.53
Xiufeng Xia700.34