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.

It is in conjunction with the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2014), held on 13-16 May 2014, at Tainan, Taiwan.

==== 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, 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 Dates ====
* January 15, 2013: Due date for workshop papers submission
* February 15, 2013: Notification of paper decision to authors
* March 1, 2014: Camera-ready of accepted papers
* May 13, 2014: Workshop

==== Submission Information ====

Paper submission system is at https://www.easychair.org/conferences/?conf=sdaworkshop2014.

We call for original and unpublished research contributions of manuscripts to the workshop following the Springer LNCS/LNAI manuscript submission guidelines (available at http://www.springer.de/comp/lncs/authors.html).

Each submitted paper should include an abstract up to 200 words and be not longer than 12 single-spaced pages with 10pt font size. All papers must be submitted electronically through the paper submission system in PDF format only. The submitted paper should NOT adhere to the double-blind review policy.

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.