Top-k Frequent Items and Item Frequency Tracking over Sliding Windows of Any Sizes

2017 ◽  
Author(s):  
Chunyao Song ◽  
Xuanming Liu ◽  
Tingjian Ge
Keyword(s):  
Frequency Tracking ◽  
Sliding Windows ◽  
Frequent Items

Top-k frequent items and item frequency tracking over sliding windows of any size

2019 ◽  
Vol 475 ◽  
pp. 100-120 ◽  
Author(s):  
Chunyao Song ◽  
Xuanming Liu ◽  
Tingjian Ge ◽  
Yao Ge
Keyword(s):  
Frequency Tracking ◽  
Sliding Windows ◽  
Frequent Items

Finding frequent items over sliding windows with constant update time

2010 ◽  
Vol 110 (7) ◽  
pp. 257-260 ◽  
Author(s):  
Regant Y.S. Hung ◽  
Lap-Kei Lee ◽  
H.F. Ting
Keyword(s):  
Sliding Windows ◽  
Frequent Items

Identifying frequent items in sliding windows over on-line packet streams

2003 ◽  
Author(s):  
Lukasz Golab ◽  
David DeHaan ◽  
Erik D. Demaine ◽  
Alejandro Lopez-Ortiz ◽  
J. Ian Munro
Keyword(s):  
Sliding Windows ◽  
On Line ◽  
Frequent Items

Anomaly Detection via Mining Numerical Workflow Relations from Logs

2020 ◽  
Author(s):  
Bo Zhang ◽  
Hongyu Zhang ◽  
Pablo Moscato
Keyword(s):  
Distributed Systems ◽  
Anomaly Detection ◽  
Text Messages ◽  
Distributed File System ◽  
Log Data ◽  
Sliding Windows ◽  
Novel Approach ◽  
Hadoop Distributed File System ◽  
Blue Gene ◽  
Online Anomaly Detection

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

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