Complex event detection at wire speed with FPGAs

Woods, Louis; Teubner, Jens; Alonso, Gustavo

doi:10.14778/1920841.1920926

Cited by 70 publications

(49 citation statements)

References 10 publications

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“…In [14]- [16], data streams with special features, such as being disordered or distributed, are treated. Reference [17] considers occurrence frequencies of events and [18] uses field-programmable gate arrays to increase throughput. Reference [19] considers the decidability of pattern matching when ambiguous patterns and infinite data streams are given.…”

Section: Non-probabilistic Data Streamsmentioning

confidence: 99%

Grouping Methods for Pattern Matching over Probabilistic Data Streams

Sugiura

Ishikawa

Sasaki

2017

IEICE Trans. Inf. & Syst.

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SUMMARYAs the development of sensor and machine learning technologies has progressed, it has become increasingly important to detect patterns from probabilistic data streams. In this paper, we focus on complex event processing based on pattern matching. When we apply pattern matching to probabilistic data streams, numerous matches may be detected at the same time interval because of the uncertainty of data. Although existing methods distinguish between such matches, they may derive inappropriate results when some of the matches correspond to the real-world event that has occurred during the time interval. Thus, we propose two grouping methods for matches. Our methods output groups that indicate the occurrence of complex events during the given time intervals. In this paper, first we describe the definition of groups based on temporal overlap, and propose two grouping algorithms, introducing the notions of complete overlap and single overlap. Then, we propose an efficient approach for calculating the occurrence probabilities of groups by using deterministic finite automata that are generated from the query patterns. Finally, we empirically evaluate the effectiveness of our methods by applying them to real and synthetic datasets.

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Section: Non-probabilistic Data Streamsmentioning

confidence: 99%

Grouping Methods for Pattern Matching over Probabilistic Data Streams

Sugiura

Ishikawa

Sasaki

2017

IEICE Trans. Inf. & Syst.

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“…Finally, complex event processing on general data streams has been studied in research and industry [21,2,30]. However, there is no element of graph structure in their patterns, which is inadequate for many modern applications.…”

Section: Related Workmentioning

confidence: 99%

Event pattern matching over graph streams

Song

Chen

et al. 2014

Proc. VLDB Endow.

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A graph is a fundamental and general data structure underlying all data applications. Many applications today call for the management and query capabilities directly on graphs. Real time graph streams, as seen in road networks, social and communication networks, and web requests, are such applications. Event pattern matching requires the awareness of graph structures, which is different from traditional complex event processing. It also requires a focus on the dynamicity of the graph, time order constraints in patterns, and online query processing, which deviates significantly from previous work on subgraph matching as well. We study the semantics and efficient online algorithms for this important and intriguing problem, and evaluate our approaches with extensive experiments over real world datasets in four different domains.

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“…Other works investigated the benefits of parallel processing to reduce response time using not only multi-core CPUs, but also co-processors like GPUs [151] or FPGAs [152,153]. Investigating how these processing technologies could be adopted in the domain of stream reasoning certainly represents an important research direction.…”

Section: Efficient Query Evaluationmentioning

confidence: 99%

Streaming the Web: Reasoning over dynamic data

Margara

Urbani

Harmelen

et al. 2014

Journal of Web Semantics

111

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a b s t r a c tIn the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation.The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning.

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Complex event detection at wire speed with FPGAs

Cited by 70 publications

References 10 publications

Grouping Methods for Pattern Matching over Probabilistic Data Streams

Grouping Methods for Pattern Matching over Probabilistic Data Streams

Event pattern matching over graph streams

Streaming the Web: Reasoning over dynamic data

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