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EnDM 2014 : 3rd International Workshop on Energy Data Management


When Mar 28, 2014 - Mar 28, 2014
Where Athens, Greece
Submission Deadline Dec 7, 2013
Categories    databases

Call For Papers

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" (

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.

The workshop solicits contributions on data management issues related to data processing/analytics within the energy domain ranging from conceptual modeling over theoretical foundations, system architecture and implementation to real world case studies. Relevant topics include, but are not limited to, the following:

Energy-specific data modeling
Meta-data management, provenance management, life cycle management of energy data
Large-scale data integration considering energy-specific formats and protocols
Novel data processing architectures for coping with very large energy data sets
Novel partitioning, caching, and replication schemes within energy IT infrastructures
Query language design, query processing, and query optimization techniques
Robustness aspects with regard to performance, scalability, reliability, and dependability
Data security and data privacy when managing energy data
Novel and domain specific data mining algorithms
Domain-specific forecasting and prediction methods
Systems support to manage and query forecasted data
Support for advanced energy analytics within database systems
Case studies outlining real world applications and application scenarios with a strong focus on production, distribution, and consumption of energy
Challenges and vision of mid-/long-term solutions for an efficient and robust energy data management system

We welcome submissions of high-quality, original research and are particularly interested in submissions that demonstrate industrial-strength solutions from systems as well as application perspectives. We also explicitly ask for contributions describing work-in-progress with a clear vision of the overall work. Submissions should not extend 8 pages in the ACM Proceedings style.

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