Institutsseminar/2021-05-21

Termin (Alle Termine)
	Datum	Freitag, 21. Mai 2021
	Uhrzeit	11:30 – 12:45 Uhr (Dauer: 75 min)
	Ort
	Webkonferenz	https://conf.dfn.de/webapp/conference/979160755
	Vorheriger Termin	Fr 14. Mai 2021
	Nächster Termin	Fr 21. Mai 2021

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Vorträge

Vortragende(r)	Haiko Thiessen
Titel	Detecting Outlying Time-Series with Global Alignment Kernels (Defense)
Vortragstyp	Masterarbeit
Betreuer(in)	Florian Kalinke
Vortragssprache
Vortragsmodus
Kurzfassung	Detecting outlying time-series poses two challenges: First, labeled training data is rare, as it is costly and error-prone to obtain. Second, algorithms usually rely on distance metrics, which are not readily applicable to time-series data. To address the first challenge, one usually employs unsupervised algorithms. To address the second challenge, existing algorithms employ a feature-extraction step and apply the distance metrics to the extracted features instead. However, feature extraction requires expert knowledge, rendering this approach also costly and time-consuming. In this thesis, we propose GAK-SVDD. We combine the well-known SVDD algorithm to detect outliers in an unsupervised fashion with Global Alignment Kernels (GAK), bypassing the feature-extraction step. We evaluate GAK-SVDD's performance on 28 standard benchmark data sets and show that it is on par with its closest competitors. Comparing GAK with a DTW-based kernel, GAK improves the median Balanced Accuracy by 4%. Additionally, we extend our method to the active learning setting and examine the combination of GAK and domain-independent attributes.

Vortragende(r)	Kuan Yang
Titel	Efficient Verification of Data-Value-Aware Process Models
Vortragstyp	Bachelorarbeit
Betreuer(in)	Elaheh Ordoni
Vortragssprache
Vortragsmodus
Kurzfassung	Verification methods detect unexpected behavior of business process models before their execution. In many process models, verification depends on data values. A data value is a value in the domain of a data object, e.g., $1000 as the price of a product. However, verification of process models with data values often leads to state-space explosion. This problem is more serious when the domain of data objects is large. The existing works to tackle this problem often abstract the domain of data objects. However, the abstraction may lead to a wrong diagnosis when process elements modify the value of data objects. In this thesis, we provide a novel approach to enable verification of process models with data values, so-called data-value-aware process models. A distinctive of our approach is to support modification of data values while preserving the verification results. We show the functionality of our approach by conducting the verification of a real-world application: the German 4G spectrum auction model.