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Cited by 50Advanced Database Systems and QueriesData Mining Algorithms and ApplicationsTime Series Analysis and Forecasting

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Sequence Clustering-based Automated Rule Generation for Adaptive Complex Event Processing

Lee, O-Joun, Jung, Jai E.·2017·Future Generation Computer Systems
DOI 10.1016/j.future.2016.02.011
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Crossref · 10.1016/j.future.2016.02.011

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1 of 1 references verified0 citation errors · 0 not checkable

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Citations

Cites

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Research works printed but not linked: 15

  1. Patroumpas, 2012, Event processing and real-time monitoring over streaming traffic data, vol. 7236, 116
  2. Zang, 2007, Complex event processing in enterprise information systems based on RFID, Enterp. Inf. Syst., 1, 3, 10.1080/17517570601092127 · DOI
  3. Cugola, 2012, Processing flows of information: From data stream to complex event processing, ACM Comput. Surv. (CSUR), 44, 15, 10.1145/2187671.2187677 · DOI
  4. Wasserkrug, 2012, Efficient processing of uncertain events in rule-based systems, IEEE Trans. Knowl. Data Eng., 24, 45, 10.1109/TKDE.2010.204 · DOI
  5. Wasserkrug, 2008, Complex event processing over uncertain data, vol. 332, 253
  6. Garofalakis, 2006, Probabilistic data management for pervasive computing: The Data Furnace project, IEEE Data Eng. Bull., 29, 57
  7. Cugola, 2015, Introducing uncertainty in complex event processing: model, implementation, and validation, Computing, 97, 103, 10.1007/s00607-014-0404-y · DOI · Paper page
  8. A. Paschke, A. Kozlenkov, H. Boley, A homogeneous reaction rule language for complex event processing, in: Proceedings of 2nd International Workshop on Event Driven Architecture and Event Processing Systems, EDA-PS, 2007.
  9. Hasan, 2015, Probabilistic event pattern discovery, vol. 9202, 241
  10. George, 2015, Event pattern mining for smart environments, vol. 9369, 42
  11. Moraru, 2015, Supporting rule generation and validation on environmental data in EnStreaM, vol. 7540, 441
  12. Xu, 2005, Survey of clustering algorithms, IEEE Trans. Neural Netw., 16, 645, 10.1109/TNN.2005.845141 · DOI
  13. Fraley, 1998, How many clusters? which clustering method? answers via model-based cluster analysis, Comput. J., 41, 578, 10.1093/comjnl/41.8.578 · DOI
  14. Jones, 2011, Bayesian information criterion for longitudinal and clustered data, Stat. Med., 30, 3050, 10.1002/sim.4323 · DOI
  15. Y. Loewenstein, E. Portugaly, M. Fromer, M. Linial, Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space, in: Proceedings of the 16th International Conference on Intelligent Systems for Molecular Biology, ISMB 2008, Toronto, Canada, 2008, pp. 41–49. · DOI

Cited by

Sorted by citations

  • Prescriptive analytics: Literature review and research challenges

    Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.003 · Cited by 501

    “…attempts for coupling probabilistic logic and uncertainty into CEP engines in order to react to predictions about future events before they occur (Cugola, Margara, Matteucci, & Tamburrelli, 2015; Engel et al., 2012; Lee & Jung, 2017; Mousheimish, Taher, & Zeitouni, 2016; Wang, Cao, & Zhang, 2013).”

See all 50

Metadata

Authors
Lee, O.-J., & Jung, J. E.
Affiliations
Chung-Ang University (KR)
Corresponding author
Jung, Jai E.
ORCID
Venue
Future Generation Computer Systems
Publisher
Elsevier BV
Published
2017-01
Volume
66
Pages
100-109
ISSN
0167-739X (ISSN-L)
DOI
10.1016/j.future.2016.02.011
OpenAlex
W2292926386
Cited by
50
References
1
Citations by year
2025: 2 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 6 · 2019: 8 · 2018: 13 · 2017: 6
FWCI
9.75
Type
journal article
Language
en
Open access
closed
Licence
Locations
Future Generation Computer Systems (publishedVersion)
Funders
  • National Research Foundation of Korea
  • Institute for Information and Communications Technology Promotion
  • Ministry of Science, ICT and Future Planning (IITP-2015-H8501-15-1018, NRF-2014R1A2A2A05007154)
Topics
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords
complex event processing, automatic rule generation, sequence clustering, probabilistic graphical models, self-adaptive systems, two-way learning, automated stock trading

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CoChat. (2026). Citation check: Sequence Clustering-based Automated Rule Generation for Adaptive Complex Event Processing. https://app.cochat.ai/papers/10.1016/j.future.2016.02.011
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Lee, O.-J., & Jung, J. E. (2017). Sequence Clustering-based Automated Rule Generation for Adaptive Complex Event Processing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2016.02.011