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Freitag, 19. Februar 2021, 11:30 Uhr
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| Vortragende(r) | Mohamed Amine Chalghoum |
|---|---|
| Titel | A comparative study of subgroup discovery methods |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Vadim Arzamasov |
| Vortragsmodus | |
| Kurzfassung | Subgroup discovery is a data mining technique that is used to extract interesting relationships in a dataset related to to a target variable. These relationships are described in the form of rules. Multiple SD techniques have been developed over the years. This thesis establishes a comparative study between a number of these techniques in order to identify the state-of-the-art methods. It also analyses the effects discretization has on them as a preprocessing step . Furthermore, it investigates the effect of hyperparameter optimization on these methods.
Our analysis showed that PRIM, DSSD, Best Interval and FSSD outperformed the other subgroup discovery methods evaluated in this study and are to be considered state-of-the-art . It also shows that discretization offers an efficiency improvement on methods that do not employ internal discretization. It has a negative impact on the quality of subgroups generated by methods that perform it internally. The results finally demonstrates that Apriori-SD and SD-Algorithm were the most positively affected by the hyperparameter optimization. |
Freitag, 19. Februar 2021, 14:00 Uhr
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| Vortragende(r) | Nico Peter |
|---|---|
| Titel | Model-Based Rule Engine for the Reconstruction of Component-Based Software Architectures for Quality Prediction |
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Yves Kirschner |
| Vortragsmodus | |
| Kurzfassung | With architecture models, software developers and architects are able to enhance their documentation and communication, perform architecture analysis, design decisions and finally with PCM, can start quality predictions. However, the manual creation of component architecture models for complex systems is difficult and time consuming. Instead, the automatic generation of architecture models out of existing projects saves time and effort. For this purpose, a new approach is proposed which uses technology specific rule artifacts and a rule engine that transforms the source code of software projects into a model representation, applies the given rules and then automatically generates a static software architecture model. The resulting architecture model is then usable for quality prediction purposes inside the PCM context. The concepts for this approach are presented and a software system is developed, which can be easily extended with new rule artifacts to be useful for a broader range of technologies used in different projects. With the implementation of a prototype, the collection of technology specific rule sets and an evaluation including different reference systems the proposed functionality is proven and a solid foundation for future improvements is given. |
Freitag, 26. Februar 2021, 11:30 Uhr
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| Vortragende(r) | Aleksandr Eismont |
|---|---|
| Titel | Predicting System Dependencies from Tracing Data Instead of Computing Them |
| Vortragstyp | Proposal |
| Betreuer(in) | Pawel Bielski |
| Vortragsmodus | |
| Kurzfassung | The concept of Artificial Intelligence for IT Operations combines big data and machine learning methods to replace a broad range of IT operations including availability and performance monitoring of services. In large-scale distributed cloud infrastructures a service is deployed on different separate nodes. As the size of the infrastructure increases in production, the analysis of metrics parameters becomes computationally expensive. We address the problem by proposing a method to predict dependencies between metrics parameters of system components instead of computing them. To predict the dependencies we use time windowing with different aggregation methods and distributed tracing data that contain detailed information for the system execution workflow. In this bachelor thesis, we inspect the different representations of distributed traces from simple counting of events to more complex graph representations. We compare them with each other and evaluate the performance of such methods. |
Freitag, 26. Februar 2021, 14:00 Uhr
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(Keine Vorträge)
Freitag, 12. März 2021, 14:00 Uhr
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(Keine Vorträge)