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Cited by 22Advanced Database Systems and QueriesData Mining Algorithms and ApplicationsBusiness Process Modeling and Analysis

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

Integrating Predictive Analytics into Complex Event Processing by Using Conditional Density Estimations

Christ, Maximilian, Krumeich, Julian, Kempa-Liehr, Andreas W.·2016·2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW)
DOI 10.1109/edocw.2016.7584363
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Abstract

The monitoring of real-time objects such as steel billets during their casting process creates myriads of events. Complex Event Processing (CEP) is the technology to analyze resulting event streams as fast as possible. But classic CEP is not able to consider events that did not happen yet. It is not clear how to transform CEP from a technology, which reacts on past events, to one, which anticipates near future events. Conditional density estimation allows to combine both estimation and expected uncertainty about the next occurrence of a given event in one mathematical object. Moreover, it allows to calculate the probability of event patterns, which are the basis for CEP. Hence, we are introducing the concept of Conditional Event Occurrence Density Estimation (CEODE) to CEP. We present a reference architecture for combining CEP engines with predictive analytics using CEODEs. On basis of concrete guidelines for transforming classical event processing rules to proactive ones, we are demonstrating how CEP evolves from being reactive to becoming both predictive and prescriptive.

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Citations

Cites

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Research works printed but not linked: 24

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Cited by

Metadata

Authors
Christ, M., Krumeich, J., & Kempa-Liehr, A. W.
Affiliations
  • Saarland University (DE)
  • University of Freiburg (DE)
ORCID
Kempa-Liehr, Andreas W.
Venue
2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW)
Publisher
IEEE
Published
2016-09
Pages
1-8
DOI
10.1109/edocw.2016.7584363
OpenAlex
W2529611981
Cited by
22
References
1
Citations by year
2025: 2 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 5 · 2020: 2 · 2019: 4 · 2018: 1 · 2017: 1
FWCI
1.71
Type
conference paper
Language
en
Open access
closed
Locations
  • IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW) (publishedVersion)
  • ResearchSpace (University of Auckland) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
Keywords
complex event processing, predictive analytics, conditional density estimation, event streams, prescriptive analytics, real-time monitoring

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