As an increasing number of sensor devices (Internet of Things) is used, more and more spatio-temporal data becomes available. Being able to process and analyze large quantities of such datasets is therefore critical. Spatial joins in classical geo-information systems do not scale well. Nevertheless, distributed implementations are promising to solve this. Various implementation variants for distributed spatial joins are documented in literature, with some being only suitable for specific use cases. We compared broadcast and multiple variants of a distributed spatially partitioned join. We anticipate that this comparison will give guidance to when to use which implementation strategy.

Telecommunication providers not only offer services but increasingly finance consumer devices. Credit scoring and the detection of fraud for new account applications gained importance as standard credit approval processes showed to fall short for new customers as there is only scarce information available in internal systems. Modern machine learning algorithms, however, can still infer intricate patterns from the data and thus can efficiently classify customers. Cost-sensitive methodologies can even enhance the savings. In this thesis, we develop a cost matrix which allows evaluating the individual risk of accepting a new customer and therefore helps to prevent new account subscription fraud optimally.