NDLI: Complex Event Detection in Probabilistic Stream

Content Provider	IEEE Xplore Digital Library
Author	Xu Chuanfei Lin Shukuan Wang Lei Qiao Jianzhong
Copyright Year	2010
Abstract	Complex event detection in stream is an important problem in event stream processing field. In this paper, we propose a new complex event detection algorithm in probabilistic stream, Instance Pruning and Filter-Detection Algorithm (IPF-DA). This algorithm is based on a kind of data structure called Chain Instance Queues (CIQ), to detect complex events satisfying query requirements with single-scanning probabilistic stream. In the process of complex event detection, IPF-DA prunes unnecessary event instances with query requirements and achieves filter for complex events with the given threshold. And it further improves the efficiency by setting proper tolerance, while insuring high recall. In addition, we construct Bayesian network to express and infer the probability distribution of uncertain events. Conditional Probability Indexing-Tree (CPI-Tree) is defined to store conditional probabilities of Bayesian network, saving query time compared with traditional Conditional Probability Table (CPT). Experimental results show that a series of strategies proposed by this paper are effective for complex event detection in probabilistic stream.
Starting Page	361
Ending Page	363
File Size	316278
Page Count	3
File Format	PDF
ISBN	9781769540122
e-ISBN	9781424466009
DOI	10.1109/APWeb.2010.56
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-06
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Atomic measurements Supply chain management Filters Event detection Bayesian methods Hidden Markov models Object detection Data structures Wind forecasting Radiofrequency identification
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

RFID data filtering algorithm in supply chain

Large-scale event detection using semi-hidden Markov models

Bayesian Supply Chain Tracking Using Serial-Level Information

Complex event processing over multi-granularity RFID data streams

Recognition of group activities using dynamic probabilistic networks

A Complex Event Processing Model Based on RFID Network

Probabilistic Modeling of Streaming RFID Data by Using Correlated Variable-duration HMMs

An improved Markov chain model for hour-ahead wind speed prediction

Towards risk based prevention of grooming attacks

Complex Event Detection in Probabilistic Stream

Similar Documents

RFID data filtering algorithm in supply chain

Large-scale event detection using semi-hidden Markov models

Bayesian Supply Chain Tracking Using Serial-Level Information

Complex event processing over multi-granularity RFID data streams

Recognition of group activities using dynamic probabilistic networks

A Complex Event Processing Model Based on RFID Network

Probabilistic Modeling of Streaming RFID Data by Using Correlated Variable-duration HMMs

An improved Markov chain model for hour-ahead wind speed prediction

Towards risk based prevention of grooming attacks

Complex Event Detection in Probabilistic Stream