Institutsseminar/2017-11-24 Zusatztermin

Aus SDQ-Institutsseminar
Termin (Alle Termine)
Datum Freitag, 24. November 2017
Uhrzeit 11:30 – 13:00 Uhr (Dauer: 90 min)
Ort Raum 010 (Gebäude 50.34)
Vorheriger Termin Fr 17. November 2017
Nächster Termin Fr 1. Dezember 2017

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Vortragende(r) Milena Nedelcheva
Titel Data-Flow Correctness and Compliance Verification for Data-Aware Workflows in Energy Markets
Vortragstyp Diplomarbeit
Betreuer(in) Jutta Mülle
Kurzfassung Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in workflows is often considered as second-class object and is not sufficiently supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance verification has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-flow of data

objects for a scenario in the energy market domain. For this purpose we use a scientific workflow management system, namely the Apache Taverna. We will then insure the correctness of the data flow of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna workflows to BPMN 2.0. We then ensure the correctness of the data-flow of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.

Vortragende(r) Jakob Bach
Titel Impact of Aggregation Methods on Clustering of High-Resolution Energy Data
Vortragstyp Masterarbeit
Betreuer(in) Holger Trittenbach
Kurzfassung Energy data can be used to gain insights into production processes. In the industrial domain, sensors have high sampling rates, resulting in large time series. Therefore, aggregation techniques are used to reduce computation times and memory requirements of data mining techniques like clustering. However, it is unclear what effects the aggregation has on clustering results and how these effects could be described.

In our work, we propose measures to analyse the impact of aggregation on clustering and evaluate them experimentally. In particular, we aggregate with standard summary statistics and assess the impact using clustering structure measures, internal validity indices, external validity indices and instance-based forecasting. We adapt these evaluation measures and other data mining techniques to our use case. Furthermore, we propose a decision framework which allows to choose an aggregation level and other experimental settings, considering the trade-off between clustering quality and computational cost.

Our extensive experiments comprise the cross-product of 6 physical attributes, 7 clustering algorithms, 7 aggregation techniques, 9 aggregation levels and 13 time series dissimilarities. We use real-world data from different machines and sensors of a production site at the KIT Campus North, extracting time series of fixed and variable length. Overall, we find that clustering results become less similar the more the data is aggregated. However, the exact effect and value of evaluation measures depends on the type of aggregate, clusteringalgorithm, dataset and dissimilarity measure.

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