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BIDMA 2019 : International Symposium on Big Data Management and Analytics 2019

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Link: https://bidma.cpsc.ucalgary.ca/2019/index.php
 
When Apr 25, 2019 - Apr 26, 2019
Where Calgary, AB, Canada
Submission Deadline Feb 10, 2019
Notification Due Apr 3, 2019
Categories    big data   data analytics   machine learning   software engineering
 

Call For Papers

Call for Extended Abstracts

BIDMA organizing committee invites you to participate in the third International Symposium on Big Data Management and Analytics (BIDMA 2019), to be held on April 25-26, in Calgary, Canada. The theme topics of the symposium include the technology aspect of Big Data management, as well as business and application aspects of different services (web-based services, cloud-based services, business services, mobile services, and Big Data services).

Tracks

Papers can be submitted to one of the following tracks:

a) Academic Track

The objective of the Academic Track at BIDMA 2019 is to provide a strong basis for collaboration among researchers to conduct research in the field of data management and data analytics. BIDMA 2019 strives to offer papers which describe various tools, techniques, and methodologies which explore the contemporary challenges with data management, processing, and analytics.

b) Industry Track

The objective of the Industry Track at BIDMA 2019 is to establish dialogue and encourage collaboration among practitioners and researchers in the data analytics community. The industry track papers will address the application of data management and data analytics to a specific problem domain.

c) Education Track

This track explores research questions surrounding the role of data in education. Topics such as data-driven education as well as Big Data in curricula are addressed.

d) Thesis Track

This track is intended for students at any stage of their Ph.D. work. The selected students will be requested to present their work in a special session of the BIDMA 2019 Symposium. These Students will be given an opportunity to get feedback and advice on how to improve their Ph.D. thesis research.

Topics

Major topics include but not limited to the following:

• Big Data Models, Algorithms, and Architecture
• Foundational Models for Big Data
• Algorithms and Programming Techniques for Big Data Processing
• Big Data Analytics and Metrics
• Representation Formats for Multimedia Big Data
• Cloud Computing Techniques for Big Data
• Big Data as a Service
• Big Data Open Platforms
• Algorithms and Systems for Big Data Search
• Machine learning based on Big Data
• Visualization Analytics for Big Data
• Big Data Management and Analytics
• Big Data Persistence and Preservation
• Big Data Quality and Provenance Control
• Management Issues of Social Network Big Data
• Privacy-Preserving Big Data Analytics
• Security Applications of Big Data
• Anomaly Detection in Very Large Scale Systems
• Collaborative Threat Detection using Big Data Analytics
• Big Data Encryption
• Big Data for Enterprise, Government, and Society
• Big Data Economics
• Real-life Case Studies of Value Creation through Big Data Analytics
• Big Data for Business Model Innovation
• Big Data Toolkits
• Big Data in Business Performance Management
• Scientific Applications of Big Data
• Large-scale Social Media and Recommendation Systems
• Experiences with Big Data Project Deployments
• Big Data in Enterprise Management Models and Practices
• Big Data in Government Management Models and Practices
• Big Data in Smart Planet Solutions
• Big Data for Enterprise Transformation
• Big Data in Education
• Curriculum Development in Big Data
• Pedagogical Methods in Big Data Analysis
• Data Science Curricula
• Development and Challenges in Data-Oriented Education
• Building Repositories to Support Education

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