Title page for ETD etd-040410-202859

Document Type dissertation

Author Name Li, Ming

Email Address mingli3 at us.ibm.com

URN etd-040410-202859

Title Robust Complex Event Pattern Detection over Streams

Degree PhD

Department Computer Science

Advisors
Murali Mani, Advisor
Elke A. Rundensteiner, Committee Member
Daniel J. Dougherty, Committee Member
Tao Lin, Committee Member

Keywords
event
stream
constraint
database
CEP
interval
pattern detection
query processing

Date of Presentation/Defense 2010-03-12

Availability unrestricted

Abstract
Event stream processing (ESP) has become increasingly important in modern applications.
In this dissertation, I focus on providing a robust ESP solution by meeting three major research challenges regarding the robustness of ESP systems: (1) while event constraint of the input stream is available, applying such semantic information in the event processing; (2) handling event streams with out-of-order data arrival and (3) handling event streams with interval-based temporal semantics. The following are the three corresponding research tasks completed by the dissertation:

Task I - Constraint-Aware Complex Event Pattern Detection over Streams. In this task, a framework for constraint-aware pattern detection over event streams is designed, which on the fly checks the query satisfiability / unsatisfiability using a lightweight reasoning mechanism and adjusts the processing strategy dynamically by producing early feedback, releasing unnecessary system resources and terminating corresponding pattern monitor.

Task II - Complex Event Pattern Detection over Streams with Out-of-Order Data Arrival. In this task, a mechanism to address the problem of processing event queries specified over streams that may contain out-of-order data is studied, which provides new physical implementation strategies for the core stream algebra operators such as sequence scan, pattern construction and negation filtering.

Task III - Complex Event Pattern Detection over Streams with Interval-Based Temporal Semantics. In this task, an expressive language to represent the required temporal patterns among streaming interval events is introduced and the corresponding temporal operator ISEQ is designed.

Files
mli.pdf

Browse by Author | Browse by Department | Search all available ETDs

Questions? Email etd-questions@wpi.edu
Maintained by webmaster@wpi.edu

Document Type	dissertation
Author Name	Li, Ming
Email Address	mingli3 at us.ibm.com
URN	etd-040410-202859
Title	Robust Complex Event Pattern Detection over Streams
Degree	PhD
Department	Computer Science
Advisors	Murali Mani, Advisor Elke A. Rundensteiner, Committee Member Daniel J. Dougherty, Committee Member Tao Lin, Committee Member
Keywords	event stream constraint database CEP interval pattern detection query processing
Date of Presentation/Defense	2010-03-12
Availability	unrestricted
Abstract Event stream processing (ESP) has become increasingly important in modern applications. In this dissertation, I focus on providing a robust ESP solution by meeting three major research challenges regarding the robustness of ESP systems: (1) while event constraint of the input stream is available, applying such semantic information in the event processing; (2) handling event streams with out-of-order data arrival and (3) handling event streams with interval-based temporal semantics. The following are the three corresponding research tasks completed by the dissertation: Task I - Constraint-Aware Complex Event Pattern Detection over Streams. In this task, a framework for constraint-aware pattern detection over event streams is designed, which on the fly checks the query satisfiability / unsatisfiability using a lightweight reasoning mechanism and adjusts the processing strategy dynamically by producing early feedback, releasing unnecessary system resources and terminating corresponding pattern monitor. Task II - Complex Event Pattern Detection over Streams with Out-of-Order Data Arrival. In this task, a mechanism to address the problem of processing event queries specified over streams that may contain out-of-order data is studied, which provides new physical implementation strategies for the core stream algebra operators such as sequence scan, pattern construction and negation filtering. Task III - Complex Event Pattern Detection over Streams with Interval-Based Temporal Semantics. In this task, an expressive language to represent the required temporal patterns among streaming interval events is introduced and the corresponding temporal operator ISEQ is designed.
Files	mli.pdf