@@ -1,24 +1,39 @@
-  - id: affenzeller2022prescriptive
+  # - id: affenzeller2023prescriptive
+  #   year: 2023
+  #   title: "Prescriptive Analytics: When Data- and Simulation-based Models Interact in a Cooperative Way"
+  #   authors: Affenzeller, M., Boegl, M., Fischer, L., Sbieczky, F., Yang, K. & Zenisek, J.
+  #   publisher: In proceedings of the 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (to be published)
+  #   abstract: Business analytics is an extensive use of data acquired from diverse sources, statistical and quantitative analysis, explainable and predictive models, and fact-based management to make better strategic decisions for different stakeholders. To be able to model complex systems holistically in such a way that they can be fed into an efficient simulation-based optimization in the sense of prescriptive analytics, approaches and solutions that go beyond state-of-the-art are required. This paper introduces the basic technologies used in prescriptive analytics and proposes secure prescriptive analytics (SPA) that is based on component-based hierarchical modeling and dynamic optimization. Each element under the SPA framework is defined and illustrated by an example of production plan optimization.
+  #   link: /  
+
+  - id: zenisek2023shapley
+    year: 2023
+    title: Shapley Value based Variable Interaction Networks for Data Stream Analysis
+    authors: Zenisek, J., Dorl, S., Falkner, D., Gaisberger, L., Winkler, S. & Affenzeller, M.
+    publisher: Computer Aided Systems Theory - EUROCAST 2022 18th International Conference, Las Palmas de Gran Canaria, Spain, February 20-25, 2022, Revised Selected Papers
+    abstract: The current development of today's production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance. By this means, the condition of plants and products in future production lines will be continuously analyzed with the objective to predict any kind of breakdown and trigger preventing actions proactively. Such ambitious predictions are commonly performed with support of machine learning algorithms. In this work, we utilize these algorithms to model complex systems, such as production plants, by focussing on their variable interactions. The core of this contribution is a sliding window based algorithm, designed to detect changes of the identified interactions, which might indicate beginning malfunctions in the context of a monitored production plant. Besides a detailed description of the algorithm, we present results from experiments with a synthetic dynamical system, simulating stable and drifting system behavior.
+    link: https://link.springer.com/chapter/10.1007/978-3-031-25312-6_15
+
+  - id: knospe2022atabu
     year: 2022
-    title: "Prescriptive Analytics: When Data- and Simulation-based Models Interact in a Cooperative Way"
-    authors: Affenzeller, M., Boegl, M., Fischer, L., Sbieczky, F., Yang, K. & Zenisek, J.
-    publisher: In proceedings of the 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (to be published)
-    abstract: Business analytics is an extensive use of data acquired from diverse sources, statistical and quantitative analysis, explainable and predictive models, and fact-based management to make better strategic decisions for different stakeholders. To be able to model complex systems holistically in such a way that they can be fed into an efficient simulation-based optimization in the sense of prescriptive analytics, approaches and solutions that go beyond state-of-the-art are required. This paper introduces the basic technologies used in prescriptive analytics and proposes secure prescriptive analytics (SPA) that is based on component-based hierarchical modeling and dynamic optimization. Each element under the SPA framework is defined and illustrated by an example of production plan optimization.
-    link: /  
+    title: A Tabu Search Approach to the Shot-Term Operational Planning of Power Systems
+    authors: Knospe, I., Stainko, R., Gattinger, A., Boegl, M., Rafetseder, K., Falkner, D.
+    publisher: Operations Research 2022, Karlsruhe
+    abstract:
+    link: https://www.or2022.de/img/Conference_Booklet_OR2022.pdf
 
   - id: zenisek2022utilizing
     year: 2022
     title: Utilizing Interpretable Machine Learning to detect Dynamics in Energy Communities
     authors: Zenisek, J., Bachinger, F., Pitzer, E., Wagner, S. & Affenzeller, M.
-    publisher: Proceedings of the 34th European Modeling and Simulation Symposium, 2022, Italy, Rome (to be published)
+    publisher: Proceedings of the 34th European Modeling and Simulation Symposium, 2022, Italy, Rome
     abstract: With the growing use of machine learning models in many critical domains, research regarding making these models, as well as their predictions, more explainable has intensified in the last few years. In this paper, we present extensions to the machine learning based data mining technique Variable Interaction Networks (VIN), to integrate existing domain knowledge and thus, enable more meaningful analysis. Several tests on data from a case study concerned with long-term monitored photovoltaic systems, verify the feasibility of our approach to provide valuable, human-interpretable insights. In particular, we show the successful application of root-cause detection in scenarios with changing system conditions.
-    link: /
+    link: https://www.researchgate.net/publication/368477809_Utilizing_Interpretable_Machine_Learning_to_detect_Dynamics_in_Energy_Communities
 
-  - id: zenisek2022shapley
+  - id: khan2022systematic
     year: 2022
-    title: Shapley Value based Variable Interaction Networks for Data Stream Analysis
-    authors: Zenisek, J., Dorl, S., Falkner, D., Gaisberger, L., Winkler, S. & Affenzeller, M.
-    publisher: Lecture Notes in Computer Science 12013, Springer Cham, 2022, pp. xxx-xxx (to be published)
-    abstract: The current development of today's production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance. By this means, the condition of plants and products in future production lines will be continuously analyzed with the objective to predict any kind of breakdown and trigger preventing actions proactively. Such ambitious predictions are commonly performed with support of machine learning algorithms. In this work, we utilize these algorithms to model complex systems, such as production plants, by focussing on their variable interactions. The core of this contribution is a sliding window based algorithm, designed to detect changes of the identified interactions, which might indicate beginning malfunctions in the context of a monitored production plant. Besides a detailed description of the algorithm, we present results from experiments with a synthetic dynamical system, simulating stable and drifting system behavior.
-    link: /
-
+    title: A Systematic Mapping Study of Predictive Maintenance in SMEs
+    authors: Khan, M., Ahmad, A., Sobieczky, F., Pichler, M., Moser, B. & Bukovsky, I.
+    publisher: IEEE Access, 10, 88738-88749
+    abstract:
+    link: https://ieeexplore.ieee.org/abstract/document/9864153