The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Processing flows of information: From data stream to complex event processing
Abstract
A large number of distributed applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Examples include intrusion detection systems which analyze network traffic in real-time to identify possible attacks; environmental monitoring applications which process raw data coming from sensor networks to identify critical situations; or applications performing online analysis of stock prices to identify trends and forecast future values. Traditional DBMSs, which need to store and index data before processing it, can hardly fulfill the requirements of timeliness coming from such domains. Accordingly, during the last decade, different research communities developed a number of tools, which we collectively call Information flow processing (IFP) systems , to support these scenarios. They differ in their system architecture, data model, rule model, and rule language. In this article, we survey these systems to help researchers, who often come from different backgrounds, in understanding how the various approaches they adopt may complement each other. In particular, we propose a general, unifying model to capture the different aspects of an IFP system and use it to provide a complete and precise classification of the systems and mechanisms proposed so far.
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Cites
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Machine learning with big data: challenges and approaches.
L'Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2017) · IEEE Access · doi:10.1109/access.2017.2696365 · Cited by 1,023
A survey on the evolution of stream processing systems
Fragkoulis, M., Carbone, P., Kalavri, V., & Katsifodimos, A. (2024) · The VLDB Journal · doi:10.1007/s00778-023-00819-8 · Cited by 207
Survey of Real-time Processing Technologies of IoT Data Streams
Yasumoto, K., Yamaguchi, H., & Shigeno, H. (2016) · Journal of Information Processing · doi:10.2197/ipsjjip.24.195 · Cited by 139
Predictive Analytics for Complex IoT Data Streams
Akbar, A., Khan, A., Carrez, F., & Moessner, K. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2712672 · Cited by 136
Seven Paradoxes of Business Process Management in a Hyper-Connected World
Beverungen, D., Buijs, J. C. A. M., Becker, J., Di Ciccio, C., van der Aalst, W. M. P., Bartelheimer, C., vom Brocke, J., Comuzzi, M., Kraume, K., Leopold, H., et al. (2021) · Business & Information Systems Engineering · doi:10.1007/s12599-020-00646-z · Cited by 89
Metadata
- Authors
- Cugola, G., & Margara, A.
- Venue
- ACM Computing Surveys
- Published
- 2012-06
- Volume
- 44
- Issue
- 3
- Pages
- 1-62
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W1973026225
- Cited by
- 833
- References
- 85
- FWCI
- 88.63
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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