In Conjunction with WSDM 2015 [Learn about WSDM 2015]
Feb. 6th, Shanghai Municipality, P. R. China
Introduction
The SDA workshop at WSDM 2015 is the 5th International Workshop on Scalable Data Analytics, following the previous 4 workshops of SDA respectively held at IEEE Big Data 2013, PAKDD 2014, IEEE Big Data 2014, and IEEE ICDM 2014.
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.
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
Topics of interest include, but not limited to, the following aspects:
★ Distributed data analytics architechtures
- 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
★ Scabable 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
★ Distributed data analytics architechtures
- 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
★ Scabable 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.
It is the authors’ responsibility to ensure that their submissions adhere strictly to the required format as described in this call for papers. Submissions that do not conform to these guidelines may be rejected without review. [Submit your papers here]
It is the authors’ responsibility to ensure that their submissions adhere strictly to the required format as described in this call for papers. Submissions that do not conform to these guidelines may be rejected without review. [Submit your papers here]
Important Dates
★ November 29, 2014: Due date for full workshop papers submission
★ December 12, 2014: Notification of paper acceptance to authors
★ December 19, 2014: Camera-ready & registration of accepted papers
★ February 6, 2015: Workshops
★ December 12, 2014: Notification of paper acceptance to authors
★ December 19, 2014: Camera-ready & registration of accepted papers
★ February 6, 2015: Workshops