In conjunction with the IEEE International Conference on Data Mining (IEEE ICDM 2014), Shenzhen China, Dec. 14, 2014.
Description
With the fast evolving technology for data collection, data transmission, and data analysis, the scientific, biomedical, and engineering research communities are undergoing a profound transformation where discoveries and innovations increasingly rely on massive amounts of data. New prediction techniques, including novel statistical, mathematical, and modeling techniques are enabling a paradigm shift in scientific and biomedical investigation.  Data become the fourth pillar of science and engineering, offering complementary insights in addition to theory, experiments, and computer simulation.  Advances in machine learning, data mining, and visualization are enabling new ways of extracting useful information from massive data sets.  The characteristics of volume, velocity, variety and veracity bring challenges to current data analytics techniques.  It is desirable to scale up data analytics techniques for modeling and analyzing big data from various domains.

The workshop aims to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art theories and applications of scalable data analytics technologies.
Topics of Interest
  • Distributed data analytics architectures
    • Data analytics algorithms for GPUs
    • Data analytics algorithms for clouds
    • Data analytics algorithms for clusters
  • Theory and algorithms for scalable descriptive statistical modeling
    • Structured, semi-structured, unstructured data preprocessing
    • Effective data sampling and feature engineering
    • Data calibration and transformation
    • Data qualitative quantitative measurement and validation
  • Theory and algorithms of scalable predictive statistical modeling
    • Association analysis
    • Data approximation, dimensional reduction, clustering
    • Linear/non-linear models for classification, regression, and ranking
    • Multiview learning, multitask learning, transfer learning, semi-supervised learning, active learning techniques for multimodal data
  • Scalable analytics techniques for temporal and spatial data
    • Real time analysis for data stream
    • Trend prediction in financial data
    • Topic detection in instant message systems
    • Real time modeling of events in dynamic networks
    • Spatial modeling on maps
  • Scalable data analytics algorithms in large graphs
    • Communities discovery and analysis in social networks
    • Link prediction in networks
    • Anomaly detection in social networks
    • Authority identification and influence measurement in social networks
    • Fusion of information from multiple blogs, rating systems, and social networks
    • Integration of text, videos, images, sounds in social media
    • Recommender systems
  • Novel applications of scalable data analytics in
    • Healthcare
    • Cybersecurity
    • Mobile computing
    • Smart cities
    • Astronomy
    • Biological data analysis
Important Date
  • August 1st, 2014: Due date for workshop papers submission
  • September 26, 2014: Notification of paper decision to authors
  • October 26, 2014: Camera-ready of accepted papers
  • December 14, 2014: Workshop
Submission Information
  • We call for original and unpublished research contributions limited to a maximum of eight (8) pages in the IEEE 2-column format.
  • The authors can pay extra charges for 2 additional pages.
  • Details about the submission instruction are here.
  • Papers should be submitted via the online submission system.
Proceedings
  • All accepted workshop papers will be published in the IEEE workshops proceedings.
  • The accepted workshop papers are forwarded by the IEEE for EI indexing.
  • Each accepted paper is required at least a workshop registration regardless of the status of the registered author.
  • One of the authors (or a qualified substitute) must give a presentation of the paper at the workshop.