Original contributions from researchers describing their original, unpublished, research contribution which is not currently under review by another conference or journal and addressing state-of-the-art research are invited to share their work in all areas of Smart and Innovative trends.
Accepted papers will be published in the proceedings and submitted to the IEEE-Digital Library and EI Index. At least one author of each accepted paper is required to register and present their work at the conference; otherwise, the paper will not be included in the proceedings. Best Paper/Demo Awards will be presented to high-quality papers/demos.
The ICDSCA 2018 Organizing Committee also invites proposals for workshops associated with the conference, addressing research areas related to the conference. Accepted workshop papers will be included in the proceedings. Please send workshop proposals to firstname.lastname@example.org.
Submission of paper has to be double-blind : the name/s of the author/s are omitted when the paper is submitted to EasyChair paper submission link.
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, 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.
Learning for streaming, 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.
Data warehouses, cloud architectures, Large-scale databases, Information and knowledge retrieval, Web/social/databases query and search, Personalized search and recommendation, Human-machine interaction and interfaces, Crowdsourcing and collective intelligence.
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.
Best practices and lessons, Data-intensive organizations, business and economy, Domain-specific applications, Business/government analytics, Online/social/living/environment data analysis, Mobile analytics for hand-held devices, Quality assessment and interestingness metrics, Complexity, efficiency and scalability, Anomaly/fraud/exception/change/event/crisis analysis, Large-scale recommender and search systems, Big data representation and visualization, Large scale application case studies.