Conference Information
DSS 2017 : IEEE International Conference on Data Science and Systems
Submission Date:
2017-07-15 Extended
Notification Date:
Conference Date:
Bangkok, Thailand
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Conference Location
Call For Papers
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 recognized 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 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. 

DSS (Data Science and 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 Systems as well as their synergy. 2017 is the 3rd event following the success in 2015 (DSDIS 2015) and 2016 (DSS 2016).

Scope and Topics

A. Data Science

• Foundational theories and models of data science
• Foundational algorithms and methods for big data
• Data classification and taxonomy
• Data metrics and metrology
• Machine learning and deep learning
• Data analytics 
• Data provenance
• Fault tolerance, reliability, and availability
• Security, privacy and trust in Data

B. Data Processing Technology

• Data sensing, fusion and mining
• Data representation, dimensionality reduction, processing and proactive service layers
• Data capturing, management, and scheduling techniques
• Stream data processing and integration
• Knowledge discovery from multiple information sources
• Statistical, mathematical and probabilistic modeling and theories
• Information visualization and visual data analytics
• Information retrieval and personalized recommendation
• 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
• Replication, archiving, preservation strategies
• Stream data computing
• Meta-data management
• Remote data access

C. Data Systems

• Storage and file systems
• High performance data access toolkits
• Programming models, abstractions for data intensive computing
• Compiler and runtime support
• Future research challenges of data intensive systems
• Real-time data intensive systems
• Network support for data intensive systems
• Challenges and solutions in the era of multi/many-core platforms
• Green (power efficient) data intensive systems
• Data intensive computing on accelerators and GPUs
• Productivity tools, performance measuring and benchmark for data intensive systems
• Big Data, cloud computing and data intensive systems

D. Data Applications

• HPC system architecture, programming models and run-time systems for data intensive applications
• Innovative applications in business, finance, industry and government cases
• Data-intensive applications and their challenges  
• Innovative data intensive applications such as health, energy, cybersecurity, transport, food, soil and water, resources, advanced manufacturing, environmental Change, and etc.
Last updated by Dou Sun in 2017-07-01
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