Big Data Optimization
Data optimization is the practice of changing an organization's data strategy to improve the speed and efficiency with which data is extracted, analysed, and used. The goal of optimization is to find the best acceptable answer given some conditions of the problem. For a problem, there may be different solutions, in order to compare them and choose the optimal solution; a function called objective function is defined. The goal of optimization is to achieve the "best" design with respect to a set of priority criteria or constraints. These include factors such as maximizing productivity, strength, reliability, longevity, efficiency and utilization. Data optimization alleviates this problem by reorganizing datasets and filtering out inaccuracies and noise. The result is often a dramatic increase in the speed at which actionable information is extracted, analysed, and made available to decision makers.
Related Conference of Big Data Optimization
12th World Congress on Computer Science, Machine Learning and Big Data
6th International Conference on Renewable Energy and Resources
12th International Conference and Exhibition on Mechanical & Aerospace Engineering
25th International Conference on Big Data & Data Analytics
Big Data Optimization Conference Speakers
Recommended Sessions
- Big Data Algorithm
- Big Data Analytics
- Big Data Applications, Challenges and Opportunities
- Big Data in Nursing Research
- Big Data Management
- Big Data Optimization
- Big Data Technologies
- Big Data Tools and Systems
- Cloud computing
- Data Mining and Machine Learning
- Data Mining Tasks, Processes and Analysis
- Data Mining Tools and Software
- Data Privacy and Ethics
- Data Warehousing
- Forecasting from Big Data
Related Journals
Are you interested in
- Advanced Deep Learning Architectures - ARTIFICIAL INTELLIGENCE-2026 (France)
- AI Futures & Emerging Trends - ARTIFICIAL INTELLIGENCE-2026 (France)
- AI in Cybersecurity - ARTIFICIAL INTELLIGENCE-2026 (France)
- AI-Driven Autonomous Systems & Robotics - ARTIFICIAL INTELLIGENCE-2026 (France)
- Applied Machine Learning Across Industries - ARTIFICIAL INTELLIGENCE-2026 (France)
- Artificial Intelligence - ARTIFICIAL INTELLIGENCE-2026 (France)
- Artificial Neural Networks - ARTIFICIAL INTELLIGENCE-2026 (France)
- Big Data & Data Engineering - ARTIFICIAL INTELLIGENCE-2026 (France)
- Cloud Computing for AI - ARTIFICIAL INTELLIGENCE-2026 (France)
- Computer Vision - ARTIFICIAL INTELLIGENCE-2026 (France)
- Deep Learning - ARTIFICIAL INTELLIGENCE-2026 (France)
- Generative Adversarial Networks & Diffusion Models - ARTIFICIAL INTELLIGENCE-2026 (France)
- Internet of Things (IoT) & Edge AI - ARTIFICIAL INTELLIGENCE-2026 (France)
- Machine Learning - ARTIFICIAL INTELLIGENCE-2026 (France)
- Multi-Agent Systems - ARTIFICIAL INTELLIGENCE-2026 (France)
- Natural Language Processing - ARTIFICIAL INTELLIGENCE-2026 (France)
- Neural Network Optimization - ARTIFICIAL INTELLIGENCE-2026 (France)
- Neuromorphic Computing & Brain-Inspired AI - ARTIFICIAL INTELLIGENCE-2026 (France)
- Predictive Analytics - ARTIFICIAL INTELLIGENCE-2026 (France)
- Quantum Machine Learning - ARTIFICIAL INTELLIGENCE-2026 (France)
- Reinforcement Learning Applications - ARTIFICIAL INTELLIGENCE-2026 (France)
- Responsible & Ethical AI - ARTIFICIAL INTELLIGENCE-2026 (France)
- Robotics and Intelligent Automation - ARTIFICIAL INTELLIGENCE-2026 (France)
