THE 2016 INTERNATIONAL SYMPOSIUM ON BIG DATA AND DATA SCIENCE
All accepted papers of all symposiums will be published by Conference Publishing Services, CPS:
as part of the proceedings of The 2016 International Conference on Computational Science and Computational Intelligence (CSCI'16). Past year's CSCI proceedings are indexed into IEEE Xplore Digital Library and can be accessed via:
- Volume I (2014): http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6822065
- Volume II (2014): http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6822285
- Volume (2015): http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7420813
and via ACM Digital Library at: http://dl.acm.org/
Topics of interest include, but are not limited to the following
Algorithms for Big Data:
Data and Information Fusion; Algorithms (including Scalable methods); Natural Language Processing; Signal Processing; Simulation and Modeling; Data-Intensive Computing; Parallel Algorithms; Testing Methods; Multidimensional Big Data; Multi-linear Subspace Learning; Sampling Methodologies; and Streaming.
Big Data Fundamentals:
Novel Computational Methodologies; Algorithms for Enhancing Data Quality; Models and Frameworks for Big Data; Graph Algorithms and Big Data; Computational Science; and Computational Intelligence.
Conversion of Data to Information; Conversion of Data to Knowledge; Data Mining; Information Mining; Predictive Analytics; Knowledge Discovery; Cloud Computing; Databases and Information Integration; Signal Processing; Natural Language Processing; Information Retrieval Methods; and Information Visualization.
Infrastructures for Big Data & Data Science:
Cloud Based Infrastructures (applications, storage & computing resources); Grid and Stream Computing for Big Data; High Performance Computing, Including Parallel & Distributed Processing; Autonomic Computing; Cyber-infrastructures and System Architectures; Programming Models and Environments to Support Big Data; Software and Tools for Big Data; Big Data Open Platforms; Emerging Architectural Frameworks for Big Data; Paradigms and Models for Big Data beyond Hadoop/MapReduce, ...
Big Data & Data Science Management and Frameworks:
Database and Web Applications; Federated Database Systems; Distributed Database Systems; Distributed File Systems; Distributed Storage Systems; Knowledge Management and Engineering; Massively Parallel Processing (MPP) Databases; Novel Data Models; Data Preservation and Provenance; Data Protection Methods; Data Integrity and Privacy Standards and Policies; Novel Data Management Methods; Crowdsourcing; Stream Data Management; and Scientific Data Management.
Big Data Search:
Multimedia and Big Data; Social Networks; Web Search and Information Extraction; Scalable Search Architectures; Cleaning Big Data (noise reduction), Acquisition & Integration; Visualization Methods for Search; Time Series Analysis; Recommendation Systems; and Graph Based Search and Similar Technologies.
Security & Privacy in the Era of Data Science & Big Data:
Cryptography; Threat Detection Using Big Data Analytics; Privacy Threats of Big Data; Privacy Preserving Big Data Collection; Intrusion Detection; Socio-economical Aspect of Big Data in the Context of Privacy and Security.
Applications of Big Data:
Big Data as a Service; Big Data Analytics in e-Government and Society; Applications in Science, Engineering, Healthcare, Visualization, Business, Education, Security, Humanities, Bioinformatics, Health Informatics, Medicine, Finance, Law, Transportation, Retailing, Telecommunication, all Search-based applications, ...