The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2017. Selected projects have presented their results on April 25th and November 15th 2017 at the Future SOC Lab Day events.

As pointed out by Zadeh, the mission of fuzzy logic in the era of big data is to create a relevant summary of huge amounts of data and facilitate decision-making. In this study, elements of fuzzy set theory are used to create a visual summary of telecom data, which gives a comprehensive idea concerning the desirability of boosting an operator’s presence in different neighborhoods and regions. The data used for validation cover historical mobility in a region of Sweden during a week. Fuzzy logic allows us to model inherently relative characteristics, such as “a tall man” or “a beautiful woman”, and importantly it also defines “anchors”, the situations (characterized with the value of the membership function for the characteristic) under which the relative notion receives a unique crisp interpretation. We propose color coding of the membership value for the relative notions such as “the desirability of boosting operator’s presence in the neighborhood” and “how well the operator is doing in the region”. The corresponding regions on the map (e.g., postcode zones or larger groupings) are colored in different shades passing from green (1) though yellow (0.5) to red (0). The color hues pass a clear intuitive message making the summary easy to grasp.

The main resource for any telecom operator is the physical radio cell network. We present two related methods for optimizing utilization in radio networks: Tetris optimization and selective cell expansion. Tetris optimization tries to find the mix of users from different market segments that provides the most even load in the network. Selective cell expansion identifies hotspot cells, expands the capacity of these radio cells, and calculates how many subscribers the radio network can handle after the expansions. Both methods are based on linear programming and use mobility data, i.e., data defining where different categories of subscribers tend to be during different times of the week. Based on real-world mobility data from a region in Sweden, we show that Tetris optimization based on six user segments made it possible to increase the number of subscribers by 58% without upgrading the physical infrastructure. The same data show that by selectively expanding less than 6% of the cells we are able to increase the number of subscribers by more than a factor of three without overloading the network. We also investigate the best way to combine Tetris optimization and selective cell expansion.

In this work, we apply cluster validation measures for analyzing email communications at an organizational level of a company. This analysis can be used to evaluate the company structure and to produce further recommendations for structural improvements. Our evaluations, based on data in the forms of email logs and organizational structure for a large European telecommunication company, show that cluster validation techniques can be useful tools for assessing the organizational structure using objective analysis of internal email communications, and for simulating and studying different reorganization scenarios.