Aims and Scope

The energy sector is one of the most active application domains being forced to re-think the current practice and apply data-management based IT solutions to provide a scalable and sustainable supply and distribution of energy. Challenges range from energy production by seamlessly incorporating renewable energy resources over energy distribution and monitoring to controlling energy consumption. Decisions are based on huge amounts of empirically collected data from smart meters, new energy sources (increasingly RES - renewable energy sources such as wind, solar, hydro, thermal, etc), new distributions mechanisms (Smart Grid), and new types of consumers and devices, e.g., electric cars.

Energy is at the top of the worldwide political agenda, e.g., due to global warming concerns and recent nuclear accidents. Ambitious goals for reductions of energy consumption and CO2 emissions have been formulated, e.g., the EU 20-20-20 goals (20% renewable energy, 20% better energy efficiency, and 20% CO2 reduction by 2020), with much more ambitious goals set for 2030 and 2050. This situation is reflected by increasing attention in research funding schemes such as the EU 7th Framework program as well as national programs. A recent trend in these programs is joint calls involving both energy and IT partners. Data management is at the heart of this development, as witnessed by the following story headlines from key players: “The Smart Grid Data Deluge” (O’Reilly Radar); “Big data for the Smart Grid” (theenergycollective); “The Coming Smart Grid Data Surge” (SmartGridNews.com).

This workshop focuses on conceptual and system architecture issues related to the management of very large-scale data sets specifically in the context of the energy domain. The overall goal is to bridge the gap between domain experts and data management scientists on the one hand. On the other hand, the workshop’s goal is to create awareness of this upcoming and very challenging application area. For the workshop’s research program, we are seeking contributions that push the envelope towards novel schemes for large-scale data processing with special focus on energy data management.