SCOPE of DMBDA 2023

Contributed papers are solicited describing original works in Data Mining and Big Data Analytics. Topics and technical areas of interest include but are not limited to the following:

 

Data Mining Big Data Analytics Algorithms and Systems for Big Data Search
Data mining foundations Foundational Models for Big Data Distributed, and Peer-to-peer Search
Grand challenges of data mining Algorithms and Programming Techniques for Big Data Processing Machine learning based on Big Data
Parallel and distributed data mining algorithms Big Data Analytics and Metrics Visualization Analytics for Big Data
Mining on data streams Representation Formats for Multimedia Big Data Big Data Economics
Graph mining Cloud Computing Techniques for Big Data Real-life Case Studies of Value Creation through Big Data Analytics
Spatial data mining Big Data as a Service Big Data for Business Model Innovation
Text, video, multimedia data mining Big Data Open Platforms Big Data Toolkits
Web mining Big Data in Mobile and Pervasive Computing Big Data in Business Performance Management
High performance data mining algorithms Big Data Persistence and Preservation SME-centric Big Data Analytics
Interactive data mining Big Data Quality and Provenance Control Big Data for Vertical Industries (including Government, Healthcare, and Environment)
Data-mining-ready structures and pre-processing Management Issues of Social Network Big Data Scientific Applications of Big Data
Data mining visualization Models and Languages for Big Data Protection Large-scale Social Media and Recommendation Systems
Information hiding in data mining Privacy Preserving Big Data Analytics Experiences with Big Data Project Deployments
Competitive analysis of mining algorithms Big Data Encryption Big Data in Enterprise Management Models and Practices
Security and privacy issues Collaborative Threat Detection using Big Data Analytics Big Data for Enterprise Transformation