Affiliation:
1. Humboldt-Universität zu Berlin, Berlin, Germany
Abstract
Complex event processing (CEP) detects situations of interest by evaluating queries over event streams. Once CEP is used in networked applications, the distribution of query evaluation among the event sources enables performance optimization. Instead of collecting all events at one location for query evaluation, sub-queries are placed at network nodes to reduce the data transmission overhead. Yet, existing techniques either place such sub-queries at exactly one node in the network, which neglects the benefits of truly distributed evaluation, or are agnostic to the network structure, which ignores transmission costs due to the absence of direct network links.
To overcome the above limitations, we propose INEV graphs for in-network evaluation of CEP queries with rich semantics, including Kleene closure and negation. Our idea is to introduce fine-granular routing of partial results of sub-queries as an additional degree of freedom in query evaluation: We exploit events already disseminated in the network as part of one sub-query, when evaluating another one. We show how to instantiate INEv graphs by splitting a query workload into sub-queries, placing them at network nodes, and forwarding of their results to other nodes. Also, we characterize INEv graphs that guarantee correct and complete query evaluation, and discuss their construction based on a cost model that unifies transmission and processing latency. Our experimental results indicate that INEv graphs can reduce transmission costs for distributed CEP by up to eight orders of magnitude compared to baseline strategies.
Publisher
Association for Computing Machinery (ACM)
Reference50 articles.
1. Efficient pattern matching over event streams
2. Network-Aware Query Processing for Stream-based Applications
3. Plan-based complex event detection across distributed sources
4. Akili et al. 2022. INEv: In-Network Evaluation for Event Stream Processing -- Extended Version. https://github.com/samieze/INEv/blob/main/INEv_TR.pdf. Akili et al. 2022. INEv: In-Network Evaluation for Event Stream Processing -- Extended Version. https://github.com/samieze/INEv/blob/main/INEv_TR.pdf.
5. Samira Akili and Matthias Weidlich . 2021 a. MuSE Graphs for Flexible Distribution of Event Stream Processing in Networks. In SIGMOD '21: International Conference on Management of Data , Virtual Event, China, June 20--25 , 2021, Guoliang Li, Zhanhuai Li, Stratos Idreos, and Divesh Srivastava (Eds.). ACM, 10--22. https://doi.org/10.1145/3448016.3457318 10.1145/3448016.3457318 Samira Akili and Matthias Weidlich. 2021a. MuSE Graphs for Flexible Distribution of Event Stream Processing in Networks. In SIGMOD '21: International Conference on Management of Data, Virtual Event, China, June 20--25, 2021, Guoliang Li, Zhanhuai Li, Stratos Idreos, and Divesh Srivastava (Eds.). ACM, 10--22. https://doi.org/10.1145/3448016.3457318
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. ACER: Accelerating Complex Event Recognition via Two-Phase Filtering under Range Bitmap-Based Indexes;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24
2. Local Self-Adaptation for Distributed Complex Event Processing;Proceedings of the 18th ACM International Conference on Distributed and Event-based Systems;2024-06-24
3. On-Demand Pattern Aggregation in Event Networks;International Workshop on Big Data in Emergent Distributed Environments;2024-06-09
4. DecoPa: Query Decomposition for Parallel Complex Event Processing;Proceedings of the ACM on Management of Data;2024-05-29
5. Stream Data Model and Architecture;Studies in Big Data;2024