Data driven scientific discovery is an important emerging paradigm for computing in areas including social, service, Internet of Things, sensor networks, telecommunications, biology, health-care and cloud. Under this paradigm, Data Science is the core that drives new researches in many areas, from environmental to social. There are many associated scientific challenges, ranging from data capture, creation, storage, search, sharing, modeling, analysis, and visualization. Among the complex aspects to be addressed we mention here the integration across heterogeneous, interdependent complex data resources for real-time decision making, streaming data, collaboration, and ultimately value co-creation. Data science encompasses the areas of data analytics, machine learning, statistics, optimization and managing big data, and has become essential to glean understanding from large data sets and convert data into actionable intelligence, be it data available to enterprises, Government or on the Web.
Following the first successful edition DSAA'2014 held in 2014 in Shanghai, then the second successful edition DSAA'2015 held in Paris, the 2016 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2016), sponsored by the IEEE Computational Intelligence Society, aims to provide a premier forum that brings together researchers, industry practitioners, as well as potential users of big data, for discussion and exchange of ideas on the latest theoretical developments in Data Science as well as on the best practices for a wide range of applications.
DSAA is also technically sponsored by ACM through SIGKDD.
IEEE DSAA'2016 will consist of two main Tracks: Research and Application; the Research Track is aimed at collecting contributions related to theoretical foundations of Data Science and Data Analytics. The Application Track is aimed at collecting contributions related to applications of Data Science and Data Analytics in real life scenarios. DSAA solicits then both theoretical and practical works on data science and advanced analytics.
Topics of Interest -- Research Track
General areas of interest to DSAA'2016 include but are not limited to:
Foundations
New mathematical, probabilistic and statistical models and theories
New machine learning theories, models and systems
New knowledge discovery theories, models and systems
Manifold and metric learning, deep learning
Scalable analysis and learning
Non-iidness learning
Heterogeneous data/information integration
Data pre-processing, sampling and reduction
High dimensional data, feature selection and feature transformation
Large scale optimization
High performance computing for data analytics
Architecture, management and process for data science
Data analytics, machine learning and knowledge discovery
Learning for streaming data
Learning for structured and relational data
Intent and insight learning
Mining multi-source and mixed-source information
Mixed-type and structure data analytics
Cross-media data analytics
Big data visualization, modeling and analytics
Multimedia/stream/text/visual analytics
Relation, coupling, link and graph mining
Personalization analytics and learning
Web/online/social/network mining and learning
Structure/group/community/network mining
Cloud computing and service data analysis
Storage, retrieval and search
Data warehouses, cloud architectures
Large-scale databases
Information and knowledge retrieval, and semantic search
Web/social/databases query and search
Personalized search and recommendation
Human-machine interaction and interfaces
Crowdsourcing and collective intelligence
Privacy and security
Security, trust and risk in big data
Data integrity, matching and sharing
Privacy and protection standards and policies
Privacy preserving big data access/analytics
Social impact
Topics of Interest -- Applications Track
Papers in this track should motivate, describe and analyse the use Data Analytics tools and/or techniques in practical application as well as illustrate their actual impact.
We seek contributions that address topics such as (but not limited to) the following:
Best practices and lessons
Data-intensive organizations, business and economy
Quality assessment and interestingness metrics
Complexity, efficiency and scalability
Big data representation and visualization
Business intelligence, data-lakes, big-data technologies
Large scale application case studies and domain-specific applications, such as but not limited to:
Online/social/living/environment data analysis
Mobile analytics for hand-held devices
Anomaly/fraud/exception/change/event/crisis analysis
Large-scale recommender and search systems
Data analytics applications in cognitive systems, planning and decision support
End-user analytics, data visualization, human-in-the-loop, prescriptive analytics
Business/government analytics, such as for financial services, manufacturing, retail, utilities, telecom, national security, cyber-security, e-governance, etc.
10月17日
2016
10月19日
2016
注册截止日期
2024年10月06日 美国 San Diego
The 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024)2022年10月13日 中国 Shenzhen
2022 IEEE 9th International Conference on Data Science and Advanced Analytics2022年10月13日 中国 Shenzhen
The 9th IEEE International Conference on Data Science and Advanced Analytics2021年10月06日
2021 IEEE 8th International Conference on Data Science and Advanced Analytics2018年10月01日 意大利
2018 IEEE 5th International Conference on Data Science and Advanced Analytics2017年10月19日 日本
2017 IEEE数据科学和先进分析方法国际会议2015年10月19日 法国
2015年IEEE数据科学先进分析国际会议
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