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3rd International Conference on Computational Intelligence and Data Engineering (ICCIDE-25)

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DATA SCIENCE AND ENGINEERING

Data Integration, Interoperability, and Metadata for Cross-Media Data Analytics

This involves combining and harmonizing data from diverse sources, ensuring seamless compatibility and enriched metadata to enable effective cross-media analysis. It supports insights from varied formats like text, images, and video.

Data mining extracts patterns and knowledge from large datasets using techniques like clustering, classification, and association rules. Applications include market analysis, fraud detection, and recommendation systems.

Optimization techniques improve processes and decision-making by identifying the best solutions within constraints. These methods are crucial in resource allocation, logistics, and machine learning.

This field ensures efficient storage, retrieval, and processing of data in mobile and ubiquitous environments. It supports applications like cloud-connected apps and IoT devices.

These computing models process large-scale data by distributing tasks across multiple nodes, enhancing speed and scalability for applications like big data analysis and blockchain.

Web data management focuses on organizing and retrieving web-based data, including content hidden in the “deep web,” enabling comprehensive analysis for applications like search engines.

Scientific databases store structured research data, while biological databases support genomic, proteomic, and evolutionary studies, enhancing knowledge in fields like medicine and biotechnology.

Data grids and warehouses store and manage large datasets for analysis. OLAP (Online Analytical Processing) facilitates multidimensional querying for business intelligence.

These databases manage time-sensitive, location-based, or multimedia data, supporting applications in GIS, multimedia systems, and real-time monitoring.

These models underpin data analysis by providing frameworks for predictions, correlations, and pattern identification in diverse datasets.

This encompasses the frameworks, methodologies, and tools for collecting, analyzing, and managing data, ensuring scalability and efficiency in data-driven projects.

Big data techniques transform massive datasets into actionable insights through visualization, predictive modeling, and analytics, enabling informed decision-making.

This field analyzes online data, social media, and networked interactions to extract patterns and trends, supporting applications in marketing, influence analysis, and recommendation systems.