Prof. Dr. h.c. Hasso Plattner

Christian Schwarz, M. Sc.

Research Projects

Predictive Analytics on In-Memory Databases

For manufacturers it is important to have an accurate demand forecast for their products in order to avoid over or under capacity in their stores. In case of Vendor-Managed-Inventory the manufacturer is solely responsible for filling the shelfs inside the retail stores. Point-of-Sale (POS) data is one of the most important basis for forecasting. However, for different reasons, many shops cannot provide this kind of data. Instead of using imprecise shipment forecasting, new approaches have to be evaluated.

Intermodal Mobility using In-Memory Databases

The usage of electric vehicles is especially attractive for people living urban areas. Those people often only have to drive short distances and are able to charge their electric vehicles at home. Thus, the limited travel distance does not negatively affect the overall comfort of owning an electric vehicle vs. using a normal car. Nevertheless, in larger cities like Berlin, the range provided by one charging cycle might not be enough for one day. For drivers of electrical vehicles it got complicated if they need to recharge their vehicle during a trip, requiring up to multiple hours for recharging their vehicle. This project has build a prototype to make it more comfortable to drive an EV, even when recharging is required.

In-Memory Real-Time Energy Management

The project focuses on the real-time evaluation and processing of huge amounts of data that arise from smart grids, both for enterprises as well as customers since smart homes and smart industries leverage great possibilities for the existing challenges in the energy business. In-memory column store technology allows us to process the huge amount of data in real time, including energy demand forecasting and consumption pattern analysis.

The Rock Project

For traditional data warehouses, mostly large and expensive server and storage systems are used. In particular, for small- and medium size companies, it is often too expensive to run or rent such systems. This problem stems from the use of a) complex cube structures containing pre-aggregated values for reporting and b) materialized views to pre-compute joins between fact and dimensions tables. The inherent design principles of memory-based column databases allow for the computation of aggregations and joins on-the-fly without relying on materialized views, making them the technology of choice for SME analytics. SMEs might, however, need analytical services only from time to time, for example at the end of a billing period. A solution to overcome these problems is to use Cloud Computing. In the Rock project, we are building an OLAP cluster of analytics databases on the Amazon EC2 cloud. For this purpose we build infrastructure around SAP's in-memory column database TREX to support multi-tenancy, replication, and failover. This project is joint work with SAP and the University of California in Berkeley.


  • Zimmermann, T., Djürken, T., Mayer, A., Janke, M., Boissier, M., Schwarz, C., Schlosser, R., Uflacker, M.: Detecting Fraudulent Advertisements on a Large E-Commerce Platform. Proceedings of the Nineteenth International Workshop on Data Warehousing and OLAP, DOLAP, Venice, Italy, March 21, 2017 (2017).
  • Schwarz, C., Schmidt, C., Hopstock, M., Sinzig, W., Plattner, H.: Efficient Calculation and Simulation of Product Cost Leveraging In-Memory Technology and Coprocessors. The Sixth International Conference on Business Intelligence and Technology (BUSTECH 2016) (2016).
  • Richly, K., Bothe, M., Rohloff, T., Schwarz, C.: Recognizing Compound Events in Spatio-Temporal Football Data. International Conference on Internet of Things and Big Data (IoTBD) (2016).
  • Januschowski, T., Kolassa, S., Lorenz, M., Schwarz, C.: Forecasting With In-Memory Technology. Foresight, The International Journal of Applied Forecasting. (2013).
  • Schwarz, C., Leupold, F., Schubotz, T.: Short-Term Energy Pattern Detection of Manufacturing Machines with In-Memory Databases - A Case Study. Proceedings of the Second International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (2012).
  • Folkerts, E., Heimburger, R., Simchi-Levi, D., Youssef, N., Schwarz, C., Lorenz, M., Januschowski, T., Akkas, A.: Demand Forecasting with partial POS Data using In-Memory Technology. 32nd International Symposium on Forecasting in Boston (2012).
  • Schwarz, C., Leupold, F., Schubotz, T., Januschowski, T., Plattner, H.: Rapid Energy Consumption Pattern Detection with In-Memory Technology. International Journal On Advances in Intelligent Systems, v5 n 3&4. (2012).
  • Borovskiy, V., Schwarz, C., Koch, W., Zeier, A.: Semantically Rich API for In-database Data Manipulation in Main-Memory ERP Systems. 13th International Conference on Enterprise Information Systems (2011).
  • Schaffner, J., Eckart, B., Schwarz, C., Brunnert, J., Jacobs, D., Zeier, A., Plattner, H.: Simulating Multi-Tenant OLAP Database Clusters. Datenbanksysteme in Business, Technologie und Web (BTW 2011), 14. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), Proceedings, Kaiserslautern, Germany (2011).
  • Schwarz, C., Borovskiy, V., Zeier, A.: Optimizing Operation Scheduling for In-Memory Databases. The 2011 International Conference on Modeling, Simulation and Visualization Methods (2011).
  • Zeier, A., Jacobs, D., Schwarz, C., Eckart, B., Schaffner, J.: Towards Analytics-as-a-Service Using an In-Memory Column Database. Divyakant Agrawal, K. Selçuk Candan, Wen-Syan Li (Eds.): New Frontiers in Information and Software as Services Service and Application Design Challenges in the Cloud, LNBIP Volume 74, pp. 257–282, Springer-Verlag, Berlin Heidelberg (2011).
  • Schaffner, J., Eckart, B., Jacobs, D., Plattner, H., Zeier, A., Schwarz, C.: Predicting In-Memory Database Performance for Automating Cluster Management Tasks. 27th IEEE International Conference on Data Engineering (ICDE) (2011).
  • Borovskiy, V., Schwarz, C., Wust, J., Koch, W., Zeier, A.: A Linear Programming Approach for Optimizing Workload Distribution in a Cloud. CLOUD COMPUTING (2011).