EDBT 2015 Workshop
March 27, 2015
The Big Data era has posed a number of challenges in applications related to event processing. In particular, the data volume, velocity and distribution necessitate the design on new scalable approaches for the efficient and timely processing of the produced data. The lack of veracity in the handled data/events further complicates the problem. Moreover, key challenges concern the use of the voluminous data in order to forecast future events and perform proactive event-driven decision-making.
Event forecasting is important because eliminating or mitigating an anticipated problem, or capitalizing on a forecast opportunity, can substantially improve our quality of life, and prevent environmental and economic damage. For example, changing traffic-light priority and speed limits to avoid traffic congestions will reduce carbon emissions, optimize transportation and increase the productivity of commuters. At the business level, making smart decisions ahead of time can become a differentiator leading to significant competitive advantage. In a wide range of applications, prevention is more effective than the cure. To prevent problems and to capitalize on opportunities before they even occur, a proactive event-driven decision-making paradigm is necessary. Decisions are triggered by forecasting events instead of reacting to them once they happen. Moreover, decisions are made in real-time and require on-the-fly processing of Big Data, that is, extremely large amounts of noisy data flooding in from various locations, as well as historical data.
The aim of the EPForDM workshop is to bring together computer scientists with interests in the fields of event processing, event forecasting and event-driven decision-making to present recent innovations, find topics of common interest and stimulate further development of new approaches to make sense of Big Data.
Topics of interest include (but are not limited to):
Scalable event processing under uncertainty
Distributed event processing
Multi-scale temporal aggregation of events
Machine learning for event processing and forecasting
Distributed machine learning
Visual analytics for proactive decision-making and Big Data
Human Factors evaluation of proactive event-driven systems
Novel architectures for Big Data processing
Engineering proactive event-driven systems
Position papers on proactive event-driven systems
Privacy issues in Big Data processing
Energy efficiency and reliability in Big Data processing
Scheduling and provisioning issues in Big Data processing