KIT | KIT-Bibliothek | Impressum | Datenschutz

Towards predictive behavior analysis for smart environments

Koschmider, Agnes; Speidel, Stefanie

Abstract:
Predictive behavior analysis allows prediction of the (human) behavior based on the analysis of historical data. Efficient approaches for predictive behavior analysis are available for scenarios with structured processes (e.g., based on ERP systems). The prediction of behavior becomes an obstacle when unstructured (decision making) processes underlie the scenario. Scenarios with unstructured processes can be found in smart environments logging sensor (event) streams such as e.g., Smart Home or Connected Cars. No efficient solutions exist to identify abnormal behavior (anomalies) in such smart environments. To provide a solution for anomaly detection in unstructured processes we suggest crossing process engineering with deep learning. Methods from process engineering allow identifying deviations while deep learning improves the robustness of anomalie detection and prediction. This conjunction is a promising approach in order to find an efficient solution.

Open Access Logo


Verlagsausgabe §
DOI: 10.5445/IR/1000092970
Veröffentlicht am 18.04.2019
Coverbild
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Jahr 2016
Sprache Englisch
Identifikator ISSN: 1613-0073
KITopen-ID: 1000092970
Erschienen in 7th International Workshop on Enterprise Modeling and Information Systems Architectures: Fachgruppentreffen der GI-Fachgruppe Entwicklungsmethoden fur Informationssysteme und deren Anwendung, EMISA 2016 - 7th International Workshop on Enterprise Modeling and Information Systems Architectures: Professional Group Meeting of the GI Special Interest Group on Development Methods for Information Systems and their Application, EMISA 2016; Vienna; Austria; 3 October 2016 through 4 October 2016. Ed.: J. Mendling
Verlag RWTH, Aachen
Seiten 79-82
Serie CEUR Workshop Proceedings ; 1701
Schlagworte data, process mining, behavior analysis, deep learning
Nachgewiesen in Scopus
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page