SCRAMBL 2015 - 2nd International Workshop on Scalable Computing for Real-Time Big Data Applications

The continuous growth of our society has lead to complex systems of behavior and also to the need to optimize certain aspects of our day to day activities. Time sensitive applications such as real time power management for smart grids, traffic control or network monitoring require on demand large scale information processing and real time responses. The data these applications gather on a regular basis from monitoring sensors exceeds the normal storage and capacity power of normal machines or even clusters. In addition, the complexities arising from handling large relational datasets include but are not limited to data heterogeneity (i.e. variability), data quality (missing/approximate), data temporality (i.e. high-velocity), data volume or data streaming. Traditional computing platforms and even storage/processing models cannot simultaneously address these efficiently. Advances in the state of art in scalable computing platforms such as clouds, offer an ideal environment for showcasing and advancing methods for modeling, management, mining and analysis of real-time big data. At the same time novel models that take into account all these complexities need to be designed on top of these scalable systems.
Following the success of last years first SCRAMBL held in Chicago, this workshop aims at providing a venue for designers, practitioners, researchers, developers, and industrial/governmental partners to come together, present and discuss leading research results, use cases, innovative ideas, challenges, and opportunities that arise from real-time big data applications. SCRAMBL'15 is co-located with the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2015) and will take place on May 4, 2015.
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Topics of Interest
Topics of interests include but are not limited to novel techniques for addressing modeling, storage, processing, mining and analysis of real-time big data in secure scalable computing environments:
Scalable Models
Data representation and data structures
Dynamic/temporal data
Heterogeneous data
Relational and linked Data
Semantic integration
Complex event processing
Messaging and data flow
Wokflows/processes
Scalable Algorithms & Architectures
Data extraction, cleaning, processing
Machine learning
Reasoning
Scalable architectures: Grids, Clouds, HPC, Clusters
Programming paradigms (e.g. MapReduce)
Big Data placement, scheduling and optimization
(Real-time) Big Data Applications and Usecases
Big Data in-use and challenges
Big Data mining and analytics
Big Data visualization and interactive mining
Security & Privacy Aspects
Trust and risk in Big Data
Security in scalable architectures
Access control to computing resources and data
Energy Efficiency
Energy efficient scalable architectures
Energy aware data storage and real-time computation