SCRAMBL 2014 - 1st 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.
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'14 is co-located with the 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2014) and will take place on May 26, 2014.
Topics of Interest
Topics of interests include but are not limited to novel techniques for addressing modeling, storage, processing, mining and analysis real-time big data in secure scalable computing environments:
Scalable Models
Data representation and data structures
Dynamic/temporal data
Heterogeneous data
Relational
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)
(Real-time) Big Data Applications and Usecases
Security & Privacy Aspects
Workshop Organization
Co-chairs
Charalampos Chelmis, University of Southern California, USA
Marc Frincu, University of Southern California, USA
Bogdan Nicolae, IBM Research, Ireland
Steering Committee
Viktor Prasanna, University of Southern California, USA
Yogesh Simmhan, Indian Institute of Sciences, India
Yinglong Xia, IBM T.J. Watson Research Center, USA
Program Committee
Albert Bifet, University of Waikato, New Zeeland
Charalampos Chelmis, University of Southern California, USA
Marc Frincu, University of Southern California, USA
Danny Lange, Microsoft Research, USA
Adrien Lèbre, Ecole des Mines, France
Bogdan Nicolae, IBM Research, Ireland
Anand Panangadan, University of Southern California, USA
Viktor Prasanna, University of Southern California, USA
Ioan Raicu, Illinois Institute of Technology, USA
Ibrahim Shadi, INRIA, France
Yogesh Simmhan, Indian Institute of Sciences, India
Yinglong Xia, IBM T.J. Watson Research Center, USA
Liang Zhang, LinkedIn, USA