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On Load Shedding in Complex Event Processing

TLDR
This paper formalizes broad classes of CEP load-shedding scenarios as different optimization problems and demonstrates an array of complexity results that reveal the hardness of these problems and construct shedding algorithms with performance guarantees.
Abstract
Complex Event Processing (CEP) is a stream processing model that focuses on detecting event patterns in continuous event streams. While the CEP model has gained popularity in the research communities and commercial technologies, the problem of gracefully degrading performance under heavy load in the presence of resource constraints, or load shedding, has been largely overlooked. CEP is similar to “classical” stream data management, but addresses a substantially different class of queries. This unfortunately renders the load shedding algorithms developed for stream data processing inapplicable. In this paper we study CEP load shedding under various resource constraints. We formalize broad classes of CEP load-shedding scenarios as different optimization problems. We demonstrate an array of complexity results that reveal the hardness of these problems and construct shedding algorithms with performance guarantees. Our results shed some light on the difficulty of developing load-shedding algorithms that maximize utility.

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Load Shedding for Complex Event Processing: Input-based and State-based Techniques

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References
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On the densest k-subgraph problems

U. Feige, +1 more
TL;DR: In this article, the applicability of semidefinite programming for approximating the dense k-subgraph problem was studied and it was shown that the problem remains NP-hard even when the maximum degree in G is three.
Journal ArticleDOI

High-performance dynamic pattern matching over disordered streams

TL;DR: This work proposes clean order-agnostic pattern-detection semantics for AFAs, with new algorithms that allow a very efficient implementation, while retaining significant expressiveness and supporting native handling of out-of-order input, stream revisions, dynamic patterns, and several optimizations.
Proceedings ArticleDOI

Sampling algorithms in a stream operator

TL;DR: This paper abstracts the stream sampling process and designs a new stream sample operator that can be used to implement a wide variety of algorithms that perform sampling and sampling-based aggregations within a data stream management system.

Load Shedding Techniques for Data Stream Systems

TL;DR: A systematic approach to load shedding with the objective of maximizing query accuracy has been lacking for data stream monitoring systems processing continuous monitoring queries over data streams.
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