Scope and Topics

Introduction

In parallel with Petrol as a driving resource in this world, Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Gradually and steadily, it is being world-wide recognised that data and talents are playing key roles in modern businesses.

As an interdisciplinary area, Data Science draws scientific inquiry from a broad range of subject areas such as statistics, mathematics, computer science, machine learning, optimization, signal processing, information retrieval, databases, cloud computing, computer vision, natural language processing and etc. Data Science is on the essence of deriving valuable insights from data. It is emerging to meet the challenges of processing very large datasets, i.e. Big Data, with the explosion of new data continuously generated from various channels such as smart devices, web, mobile and social media.

Data intensive systems are posing many challenges in exploiting parallelism of current and upcoming computer architectures. Data volumes of applications in the fields of sciences and engineering, finance, media, online information resources, etc. are expected to double every two years over the next decade and further. With this continuing data explosion, it is necessary to store and process data efficiently by utilizing enormous computing power. The importance of data intensive systems has been raising and will continue to be the foremost fields of research. This raise brings up many research issues, in forms of capturing and accessing data effectively and fast, processing it while still achieving high performance and high throughput, and storing it efficiently for future use. Innovative programming models, high performance scalable computing platforms, efficient storage systems and expression of data requirements are at immediate need.
     
DSDIS (Data Science and Data Intensive Systems) was created to provide a prime international forum for researchers, industry practitioners and domain experts to exchange the latest advances in Data Science and Data Intensive Systems as well as their synergy.

Scope and Topics

A. Data Science

Topics of particular interest include, but are not limited to:
• Data sensing, fusion and mining
• Data representation, dimensionality reduction, processing and proactive service layers
• Stream data processing and integration
• Data analytics and new machine learning theories and models
• Knowledge discovery from multiple information sources
• Statistical, mathematical and probabilistic modeling and theories
• Information visualization and visual data analytics
• Information retrieval and personalized recommendation
• Data provenance and graph analytics
• Parallel and distributed data storage and processing infrastructure
• MapReduce, Hadoop, Spark, scalable computing and storage platforms
• Security, privacy and data integrity in data sharing, publishing and analysis
• Big Data, data science and cloud computing
• Innovative applications in business, finance, industry and government cases
   
B. Data Intensive Systems

Topics of particular interest include, but are not limited to:
• Data-intensive applications and their challenges
• Scalable computing platform such as Hadoop and Spark
• Storage and file systems
• High performance data access toolkits
• Fault tolerance, reliability, and availability
• Meta-data management
• Remote data access
• Programming models, abstractions for data intensive computing
• Compiler and runtime support
• Data capturing, management, and scheduling techniques
• Future research challenges of data intensive systems
• Performance optimization techniques
• Replication, archiving, preservation strategies
• Real-time data intensive systems
• Network support for data intensive systems
• Challenges and solutions in the era of multi/many-core platforms
• Stream data computing
• Green (Power efficient) data intensive systems
• Security and protection of sensitive data in collaborative environments
• Data intensive computing on accelerators and GPUs
• HPC system architecture, programming models and run-time systems for data intensive applications
• Productivity tools, performance measuring and benchmark for data intensive systems
• Big Data, cloud computing and data intensive systems
• Innovative data intensive applications such as big sensing/surveillance/transport data, big document/accounting data, big online transaction data analysis and etc.

Submission Guidelines

Submissions must include an abstract, keywords, the e-mail address of the corresponding author and should not exceed 8 pages for main conference, including tables and figures in IEEE CS format. The template files for LATEX or WORD can be downloaded here. All paper submissions must represent original and unpublished work. Each submission will be peer reviewed by at least three program committee members. Submission of a paper should be regarded as an undertaking that, should the paper be accepted, at least one of the authors will register for the conference and present the work. Submit your paper(s) in PDF file at the submission site: http://www.swinflow.org/confs/dsdis2015/submission.htm.

Publications

Accepted and presented papers will be included into the IEEE Conference Proceedings published by IEEE CS Press. Authors of accepted papers, or at least one of them, are requested to register and present their work at the conference, otherwise their papers may be removed from the digital libraries of IEEE CS after the conference.

Distinguished papers presented at the conference, after further revision, will be published in special issues of Concurrency and Computation: Practice and Experience, Journal of Network and Computer Applications, Journal of Computer and System Sciences and IEEE Transactions on Big Data.