Paper Info
Title
Anomaly Detection over Streaming Data: Indy500 Case Study
Abstract
Sports racing is attracting billions of audiences each year. It is powered and transformed by the latest data analysis technologies, from race car design, driving skill improvements to audience engagement on social media. However, most of the data processing are off-line and retrospective analysis. The emerging real-time data analysis from the Internet of Things (IoT) result in fast data streams generated from distributed sensors. Applying advanced Machine Learning/Artificial Intelligence over such data streams to discover new information, predict future insights and make control decision is a crucial process. In this paper, we start by articulating racing car big data characteristics and present time-critical anomaly detection of the racing cars with the real-time sensors of cars and the tracks from actual racing events. We build a scalable system infrastructure based on neuro-morphic Hierarchical Temporal Memory Algorithm (HTM) algorithm and Storm stream processing engine. By courtesy of historical Indy500 racing logs, evaluation experiments on this prototype system demonstrate good performance in terms of anomaly detection accuracy and service level objective (SLO) of latency for a real-world streaming application.
Year
DOI
Venue
2019
10.1109/CLOUD.2019.00015
2019 IEEE 12th International Conference on Cloud Computing (CLOUD)
Keywords
Field
DocType
big data, stream processing, anomaly detection, neuro morphic computing, edge computing
Edge computing,Anomaly detection,Service level objective,Data processing,Data stream mining,Hierarchical temporal memory,Computer science,Real-time computing,Stream processing,Big data
Conference
ISSN
ISBN
Citations 
2159-6182
978-1-7281-2706-4
0
PageRank 
References 
Authors
0.34
10
10
Name
Order
Citations
PageRank
Chathura Widanage103.38
Jiayu Li200.34
Sahil Tyagi300.34
Ravi Teja400.34
Bo Peng592.91
Supun Kamburugamuve600.34
Dan Baum700.34
Dayle Smith800.34
Judy Qiu932.07
Jon Koskey1000.34