Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs; and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision making (Veracity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce.
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<br>Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, t...
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Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs; and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision making (Veracity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce.
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<br>Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make business more agile, and to answer questions that were previously considered beyond our reach. Distributed systems is a classical research discipline investigating various distributed computing technologies and applications such as cloud computing and MapReduce. With new paradigms and technologies, distributed systems research keeps going with new innovative outcomes from both industry and academia. For example, wide deployment of MapReduce is a distributed programming paradigm and an associated implementation to support distributed computing over large datasets on cloud. BigDataSE (Big Data Science and Engineering) is created to provide a prime international forum for researchers, industry practitioners and environment experts to exchange the latest fundamental advances in the state of the art and practice of Big Data and broadly related areas.
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<br>BigDataSE 2015 is the next event in a series of highly successful International Conferences, previously held as BigDataSE2014 (Beijing, China, September 2014), BigDataSE2013 (Sydney, Australia, December 2013), BigDataMR-12 (Xiangtan, China, November 2012), AHPCN-12 (Bradford, UK, June 2012), AHPCN-11 (Banff, Canada, September 2011), AHPCN-10 (Melbourne, Australia, September 2010), AHPCN-09 (Seoul, Korea, June 2009), AHPCN-08 (Dalian, China, September 2008).
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<br>Topics of interest include, but not limited to
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<br> - Big Data novel theory, algorithm and applications
<br> - Big Data standards
<br> - Big Data mining and analytics
<br> - Big Data Infrastructure, MapReduce and Cloud Computing
<br> - Big Data visualization
<br> - Big Data curation and management
<br> - Big Data semantics, scientific discovery and intelligence
<br> - Big Data performance analysis and large-scale deployment
<br> - Security, privacy, trust, and legal issues to big data
<br> - Big Data vs Big Business and Big Industry
<br> - Large data stream processing on cloud
<br> - Large incremental datasets on cloud
<br> - Distributed and federated datasets
<br> - NoSQL data stores and DB scalability
<br> - Big Data placement, scheduling, and optimization
<br> - Distributed file systems for Big Data
<br> - MapReduce for Big Data processing, resource scheduling and SLA
<br> - Performance characterization, evaluation and optimization
<br> - Simulation and debugging systems and tools for MapReduce and Big Data
<br> - Volume, Velocity, Variety, Value and Veracity of Big Data
<br> - Multiple source data processing and integration with MapReduce
<br> - Storage and computation management of Big Data
<br> - Large-scale big data workflow management
<br> - Mobility and big data
<br> - Sensor network, social network and big data
<br> - Big data applications
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<br>Important Dates
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<br>Workshop Proposal: February 1, 2015
<br>Submission Deadline: 11:59PM (UTC/GMT+3 hours) March 31, 2015
<br>Authors Notification: May 31, 2015
<br>Final Manuscript Due: July 1, 2015
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<br>Submission Guidelines
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<br>Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Papers must be clearly presented in English, must not exceed 8 pages, including tables, figures, references and appendixes, in IEEE Computer Society proceedings format with Portable Document Format (.pdf). Please submit your paper at https://comnet.aalto.fi/BDSE2015/sub/.
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<br>Papers will be selected based on their originality, timeliness, significance, relevance, and clarity of presentation. Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work. Accepted and presented papers will be included in the IEEE CPS Proceedings. Distinguished papers presented at the conference, after further revision, will be recommended to high quality international journals.
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