DataBeers Malaga #20 especial datos y ciberseguridad- Plataforma de Análisis del Big Data -Jose F.Aldana

DataBeers Malaga #20 especial datos y ciberseguridad- Plataforma de Análisis del Big Data -Jose F.Aldana

• 1.
  José F. Aldana [email protected] KhaosResearch Plataforma de Análisis del Big Data Instituto de Tecnología e Ingeniería del Software Universidad de Málaga @DataBeersMLG 1-Diciembre-2022
• 2.
  ITIS overview • ITIS/ ITIS Software: research institute for Software Technologies and Software Engineering of University of Malaga • The structure – Formal unit at University of Malaga, with accreditation since June 2019 – 50 Doctors + 50 PhD students and Engineers in Computer Science and Telecommunications – 9 founding research teams organized in 4 transversal areas • The mission: research and collaboration with industry and society in the field of Software and its applications 2
• 3.
  ITIS Research areas& capabilities • Automated Software engineering: modelling, testing, verification, quality and reliability • Data Science and Engineering: (Data based) Artificial Intelligence, Big Data Analytics, Machine Learning and Optimization • Smart Networks and Services • Cybersecurity 3
• 4.
  ITIS Research areas& capabilities • Automated Software engineering: modelling, testing, verification, quality and reliability • Data Science and Engineering: (Data based) Artificial Intelligence, Big Data Analytics, Machine Learning and Optimization • Domain knowledge in Big Data analytic algorithms • Data integration and scalable reasoning on linked data • Multi-objective optimization with metaheuristics • Artificial intelligence for software engineering • Memetic computing • Intelligent systems for smart cities • Gamification, video games and AR/VRSmart Networks and Services • Smart Networks and Services • Cybersecurity 4
• 5.
  ITIS relevant researchinfrastructures • Cybersecurity analysis in industrial environments • Data analytics • Fog computing • Mobile networks (4G/5G/B5G) • Artificial intelligence and numeric algorithms 5 Shield box Power analyzer Radio access network emulator Measurements visualizer TAP engine Portal for the definiton of experiments
• 6.
  Research Areas • DataScience, Data Engineering and Artificial Intelligence –Big Data Analysis and Management –Data Engineering, Semantic Data Integration and Knowledge Graphs –Machine Learning and Deep Learning –Metaheuristics and Optimization • Society Challenges (Software Technologies Application and Software Engineering) –Precision Agriculture and Earth Observation –Smart Cities and 5/6G telecommunications –eHealth, Biomedicine and Bioinformatics
• 7.
  eHealth, Biomedicine andBioinformatics Health Applications using Deep Learning, Machine Learning, Transfer Learning and Metaheuristics in: - Cancer - Obesity - Heart diseases - Mental disorders - Cognitive decline
• 8.
  Smart Cities and5/6G telecommunications Smart Cities Real-time tracking of people from traffic cameras 15+ environmental indicators from satellite images
• 9.
  Precision Agriculture andEarth Observation Precision Agriculture Crop classification from satellite images
• 10.
  80% 20%
• 11.
  Lab to supportData Science and Engineering 11 Data Science and Engineering: (Data based) Artificial Intelligence, Big Data Analytics, Machine Learning and Optimization Domain knowledge in Big Data analytic algorithms Data integration and scalable reasoning on linked data Multi-objective optimization with metaheuristics Artificial intelligence for software engineering
• 12.
  Plataforma Big DataUMA Consultoría Tecnológica y TBI (Test Before Invest) Innovación Ejemplo Instanciación AI-BigData Lab: Artificial Intelligence, Big Data Analytics, Machine Learning and Optimization
• 13.
  Big Data AnalyticsWorkflows 13
• 14.
  Big Data AnalyticsWorkflows 14
• 15.
  Big Data AnalyticsWorkflows 15
• 16.
  Big Data AnalyticsWorkflows 16
• 17.
  Big Data AnalyticsWorkflows 17
• 18.
  Big Data AnalyticsWorkflows 18 Semantics
• 19.
  Big Data AnalyticsWorkflows 19 Semantics
• 20.
  Big Data AnalyticsWorkflows 20 TASK EXECUTION SCHEDULE (Deployment, Load Balancing, Resource Demand Priorities) Semantics
• 21.
  Big Data AnalyticsWorkflows 21 TASK EXECUTION SCHEDULE (Deployment, Load Balancing, Resource Demand Priorities) Semantics
• 22.
  Example Workflow: LandCover Classification and Monitoring Data Fusion (clean, wrangling, storing) Acquisition Meteo Processing Pre-processing, radiometric calibration Index calculation (NDVI, SWDI, LAI, etc) UAS Multispectral images Land cover mapping products Variables stack, dataset standardization Scene Classification (KNN, SVM, CNN, DT, RF, …) Analysis Dataset Slitting Validation (Ground-truth, Test areas, Confusion matrix) Decision Making Visualization (time series scene evolution, flags and recommendation) Crossvalidation Component 1 Component 2 Component 3 Component 4 Component 5 Component 7 Component 8 Component 6 Component 10 Component 11 Component 13 Component 14 Component 12 Task with high computational cost (automatic deploymend on on-demand resources of RES) Task requiring scalable storing and data processing (automatic deploymend on HDFS environments)
• 23.
  BIGOWL BIG DATA
• 25.
  ITIS/KHAOS (https://khaos.uma.es/) 10+ Workflowsdeveloped 90+ Components developed
• 26.
  TITAN Core TITAN platform’s architectureis composed of a Graphical user interface, a REST API and an orchestrator for executing the workflows. BIG DATA
• 27.
  BIG DATA
• 28.
  DESIGN TESTING DEPLOYMENT TITAN BIG DATA
• 29.
  BIG DATA
• 30.
  Covariance matrix BIG DATA
• 31.
  BIG DATA
• 32.
  Parameters Parameters BIG DATA
• 33.
  BIG DATA
• 34.
  BIG DATA
• 35.
  BIG DATA
• 36.
  BIG DATA From AeRobiology (R)
• 37.
  ●Open forests /Bare soil / shrubland / forests BIG DATA
• 38.
  Thanks!! José F. Aldana @Jose__Aldana [email protected]