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 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.
The workshop offers a variety of sponsorship opportunities suitable for all range of organizations. The opportunities include complimentary registrations, student travel scholarships and invited speaker travel support. Sponsors of the workshop will gain visibility for their companies, demonstrate support for real-time data analytics research, and contribute to the success of the workshop.
Potential sponsors should contact Dr. Marc Frincu (frincu_at_usc_dot_edu) or Dr. Charalampos Chelmis (chelmis_at_usc_dot_edu) with the potential sponsor's contact information including contact person, phone number, email address and level of sponsorship. Note: replace _at_ with @ and _dot_ with . in the contact emails.
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:
SCRAMBL'14 is a half day workshop conducted in a highly interactive manner. We envisage two presentations sessions followed by a general discussion. In addition to regular research papers, we accept real-world use cases and position papers to stimulate discussions including practitioners. Further, we will accept outrageous ideas statements, which may highlight controversial topics or visionary ideas, to further foster discussions.
Particularly, we welcome submissions in the form of:
Authors are invited to submit papers formatted according to the 2-column IEEE format (Latex), via EasyChair. The review process will not be blind, therefore it is not necessary for submissions to be anonymized. Submitted manuscripts should not exceed 10 letter-size (8.5 x 11) pages including figures, tables and references using the IEEE format for conference proceedings. Submissions not conforming to these guidelines or received after the due date will be returned without review. For the final camera-ready version, authors with accepted papers may purchase additional pages at the following rates: 100 USD for each of the first two additional pages; 200 USD for each of the third and fourth additional pages. Submitted papers must represent original and unpublished work, that is not currently under review. All manuscripts will be evaluated according to their significance, originality, technical content, style, clarity, quality of presentation, and relevance to the workshop. At least one author of each accepted paper is expected to attend the workshop.
Prof. Dr. Xian-He Sun Department of Computer Science, Illinois Institute of Technology, USA
Title: C-AMAT: A Concurrent Model for Scalable Data Access
Scalable computing for real-time big data applications is a challenging task. Big data applications put even more pressure on the lasting memory-wall problem, which makes data access the prominent performance bottleneck for real-time solutions. Scalable computing is known for its massively parallel architectures. A natural way to improve memory performance is to increase and utilize memory concurrency to a level commensurate with that of scalable computing. We argue that substantial memory concurrency exists at each layer of current memory systems, but it has not been fully utilized. In this talk we reevaluate memory systems and introduce the novel C-AMAT model for system design analysis of concurrent data accesses. C-AMAT is a paradigm shift to support sustained data accessing from a data-centric view. The power of C-AMAT is that it has opened new directions to reduce data access delay. In an ideal parallel memory system, the system will explicitly express and utilize parallel data accesses. This awareness is largely missing from current memory systems. We will review the concurrency available in modern memory systems, present the concept of C-AMAT, and discuss the considerations and possibility of optimizing parallel data access for big data applications. We will also present some of our recent results which quantize and utilize parallel I/O following the parallel memory concept.Dr. Xian-He Sun is a Distinguished Professor of Computer Science and the chairman of the Department of Computer Science at the Illinois Institute of Technology (IIT). He is the director of the Scalable Computing Software laboratory at IIT and a guest faculty in the Mathematics and Computer Science Division at the Argonne National Laboratory. Before joining IIT, he worked at DoE Ames National Laboratory, at ICASE, NASA Langley Research Center, at Louisiana State University, Baton Rouge, and was an ASEE fellow at Navy Research Laboratories. Dr. Sun is an IEEE fellow and is known for his memory-bounded speedup model, also called Sun-Ni’s Law, for scalable computing. His research interests include parallel and distributed processing, memory and I/O systems, software systems for big data applications, and performance evaluation. He has over 200 publications and 4 patents in these areas. He is a former IEEE CS distinguished speaker and former vice chair of the IEEE Technical Committee on Scalable Computing, and is serving and served on the editorial board of most of the leading professional journals in the field of parallel processing. More information about Dr. Sun can be found at his web site www.cs.iit.edu/~sun/.