We present different topics of our research every Tuesday at 11:15am in room 103, Obermarkt 17. For news about the EAD-Lunch talks and seminars please feel free to subscribe to EAD-Public@googlegroups.com. (Register here: https://groups.google.com/forum/?hl=de&fromgroups#!forum/ead-public)
contact: mullrich(at)hszg.de
In this work we evaluate the scalability and performance of our previously presented GenPAC method by applying it on larger datasets. The work is motivated by the necessity of meeting privacy constraints when focusing on the importance and broad application of data mining but also by the growing demand for privacy preservation in general. GenPAC, which can be used with any standard classification method, relies on clustering data to obfuscate information. The method is particularly useful in multi-party data mining scenarios where privacy is of interest. Its application has only minor impact on the classification performance of the used underlying data mining method whilst privacy preservation can be provided at the cost of a higher execution time. In this regard, empirical analysis and evaluation have been conducted. The corresponding results are presented, analyzed and discussed with respect to their classification performance and execution time showing a high scalability in regard to the size of the dataset and the number of participating parties.
contact: s3mamich(at)stud.hszg.de
Heutzutage werden die Kundenanforderungen in der Softwareentwicklung immer anspruchsvoller. Die Softwarebranche hat das erkannt und richtet ihre Dienstleistungen immer individueller auf die Kundenwünsche aus. Jedoch werden oft nicht-funktionale Anforderungen, wie die Performance eines Services außer Acht gelassen. In den meisten Fällen, weiß der Kunde gar nicht welche Bedeutung die Performance hat. Es muss erst zum Serviceausfall kommen ehe gehandelt wird. In den SLA-Bedingungen wird die Verfügbarkeit eines Services vereinbart, dazu zählt aber nicht die Performance. In Zusammenarbeit mit der Saxonia Systems AG wurde auf Basis der Demingkreis Methode ein Wartungsschema entwickelt, um Performance Monitoring durchzuführen. Das Wartungsschema ist eine Handlungsanleitung für das Wartungsteam, um die Kontrolle über die Performance zu erlangen. Im Anschluss wird das Wartungsschema in einem Fallbeispiel getestet, bei dem die Meldung eines Incident gelöst wird.
contact: a.bartusiak(at)hszg.de, Marcel.Kuehne(at)hszg.de
In this talk, the speakers will present the final results of the research project that was conducted between January 2015 and October 2016. The work addresses the problem of massive amounts of unstructured data that is generated on a daily basis in most business organisations. The goal is to improve their processes for extracting valuable business information from such disorganised data. As a product of this research project, a flexible and scalable data analysis framework is developed which is capable of transforming various types of documents into semantically annotated structures.