SDATA 2015 - 1st International Workshop On Scalable Data Analytics: Theory & Applications (SDATA)

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.
Topic of Interest
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Topics of interest include, but not limited to, the following aspects:
*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
- Liner / 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 machine learning in big data
- Decision making with big data
- Counterfactual reasoning with big data
- Medical / health informatics big data analysis
- Security big data analysis
- Astronomy big data analysis
- Biological big data analysis
- Urban / smart city big data analysis
- Education big data analysis
Paper Submission
===
Submissions must represent new and original work. Concurrent submissions are not allowed. Submissions
that have been previously presented in venues with no formal proceedings or as posters are allowed, but
must be so indicated on the first page of the submission. Papers must be formatted for US Letter size
according to ACM guidelines and style files, must fit within 10 pages (with a font size no smaller than
9pt), including references, diagrams, and appendices if any. A submitted paper must be self-contained
and in English. Submit papers via the link https://easychair.org/conferences/?conf=sdata2015.
Important Dates
===
* November 14, 2014: Due date for full workshop papers submission
* December 5, 2014: Notification of paper acceptance to authors
* December 19, 2014: Camera-ready & registration of accepted papers
* February 6, 2015: Workshops
Organizers
===
Kaizhu HUANG, Xi'an Jiaotong-Liverpool University
Haiqin YANG, The Chinese University of Hong Kong
Irwin KING, The Chinese University of Hong Kong
Michael LYU, The Chinese University of Hong Kong