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ICCMIT 2019 : Special Session on Advances in Data Science Theory and Interdisciplinary Applications - 5th International Conference on Communication, Management and Information Technology


When Mar 26, 2019 - Mar 29, 2019
Where Vienna, Austria
Submission Deadline Dec 31, 2018
Notification Due Jan 15, 2019
Final Version Due Jan 22, 2019
Categories    data science   machine learning   data management   big data

Call For Papers

In recent years, we testify an increasing amount of data in world history. More and
more data is becoming available at scale on the internet, smartphones, corporations,
or on the giant platforms such as Amazon. Data is transforming healthcare,
transportation, smart cities, business, and interpersonal social communication. The
need of data discovery constitutes new opportunities for building intelligent
systems. It is a scientific challenge to develop powerful methods and algorithms,
which improves the data collection, management, and extraction of relevant
knowledge from a large volume of data coming from heterogeneous sources and in
various formats. Machine learning techniques and data driven analytics could offer
significant improvements in the discovery and exploitation of data in different areas
and interdisciplinary applications.
This session aims to solicit high quality submissions on various aspects of Data
Acquisition (eg. IOT, sensors), Access to the information (eg. Databases, security
and privacy), Data Analytical techniques (eg. Machine learning, Natural language
processing), Data Visualization (eg. geospatial representations, social networks, data
mapping) and innovative Data Science Applications (eg. Smart communities,
healthcare, e-commerce). We invite submissions that describe novel methods to
address the challenges inherent to the following topics.
Topics of the Session:
 Data Science and Foundations
 Novel Theoretical / Computational Models for Data science
 Data and Information Quality
 New Data Standards
 Big Data Infrastructure
 Cloud/Grid/Stream Computing for Data Science
 High Performance/Parallel Computing Platforms
 Data Acquisition, Integration, Cleaning, and Best Practices
 Data Management
 Geospatial data Management
 Search and Mining of variety of data including scientific and engineering,
social, sensor/IoT/wearables, and multimedia data
 Data Search Architectures, Scalability and Efficiency
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