Symposium on Big Data and Data Science (CSCI-ISBD)

by admin last modified Oct 21, 2018 10:57 AM
All accepted papers of this symposium will be published by Conference Publishing Services, CPS:

https://www.computer.org/web/cs-cps/ as part of the proceedings of The 2018 International Conference on Computational Science and Computational Intelligence (CSCI'18).

 Past conference proceedings can be accessed via IEEE Xplore Digital Library at:

  Volume (2017):    IEEE Xplore indexation/inclusion is APPROVED by IEEE Quality Assurance on October 17, 2018 (IEEE Record #: 43386); CSCI 2017 proceedings will appear in Xplore within days.

 

You are invited to Submit a Paper for Consideration. Submissions are to be uploaded to the Evaluation Web site portal at: https://american-cse.org/

Scope & Topics

   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.
 
   Data Science:
   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, ...
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