The Age of Intelligent Data Systems: An Introduction with Application Examples. Paulo Cortez (ALGORITMI R&D Centre, University of Minho)

Transcription

1 The Age of Intelligent Data Systems: An Introduction with Application Examples Paulo Cortez (ALGORITMI R&D Centre, University of Minho)

2 Intelligent Data Systems: Introduction

3 The Rise of Artificial Intelligence (AI) AI has achieved many remarkable milestones, including: 1997: IBM s Deep Blue beats Garry Kasparov in Chess. 2011: IBM s Watson beats two best human players on Jeopardy. 2016: Google s AlphaGo wins Korea s Lee Sedol Go player.

4 Artificial Intelligence (AI) Includes several subfields: Machine Learning, Metaheuristics, Artificial Intelligence Metaheuristics (optimization) Machine Learning (prediction) ABI Business Intelligence (decision support)

5 The Rise of Data How much new information is created each year?

6 The Rise of Data zettabytes of data in 2013, and that s projected to grow to 44 zettabytes by 2020, and IBM estimates that 90 percent of the data in existence today was produced in the last two years. Many sources of data, including Web & Social Networks. Internet of Things (IoT). Industry 4.0. Smart Cities.

7 Data Related Terms Metaheuristics Modern Optimization Decision Support Systems Business Intelligence Big Data Adaptive Business Intelligence Machine Learning Analytics/ Data Mining Data Science 1960s 1990s 2000s Time

8 Business Intelligence (BI) Overlaps with Analytics, Decision Support Systems, Data Science Umbrella term that includes architectures, tools, methods and databases, to analyze raw data in order to support decisions. Can include: Data Warehouse (DW), Data Mining (DM) and Dashboards.

9 Tools: Gartner Magic Quadrant for Business Intelligence

10 Rexer 2015 Data Science Survey 1,220 analytic professionals from 72 countries. Highlights: Regression, Decision Trees, and Cluster analysis remain the most commonly used algorithms in the field. The rise of the R tool: 76% of respondents report using R.

11 Open Source Tools: R IEEE 2016 computer language ranking:

12 Adaptive Business Intelligence (ABI) Adds intelligent adaptive modules to standard BI systems: Data-driven Prediction and Modern Optimization.

13 Data-driven Prediction The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has the most direct business applications. Machine Learning: decision trees, neural networks, ensembles, random forests, support vector machines, deep learning,

14 Modern Optimization Also known as Metaheuristics "Related with general purpose solvers that use few domain knowledge, iteratively improving a solution (or population of solutions) to minimize or maximize a goal. Metaheuristics: simulated annealing, tabu search, genetic algorithms, genetic programming, multi-objective optimization (e.g. NSGAII), particle swarm optimization,

15 Intelligent Data Systems: Application Examples

16 Intensive Care Medicine Prediction: Multiclass, Neural Networks and Multinomial Logistic Regression Based on adverse events, measured from four biometrics. Dataset with 4425 patients from 42 European ICUs.

17 Intensive Care Medicine Prediction: Multiclass, Neural Networks and Multinomial Logistic Regression Rating organ failure via adverse events using data mining in the intensive care unit, AIIM 2008.

18 Wine Quality Prediction: Regression, Support Vector Machines Modeling wine preferences by data mining from physicochemical properties, DSS 2009.

19 Bank Telemarketing Prediction: Feature Engineering, Neural Networks Predict the success of telemarketing calls for selling bank long-term deposits. A total of 150 features (known prior to the call) were analyzed. Semi-automatic feature selection used to set a reduced set of 22 features. 52,944 phone contacts.

20 Bank Telemarketing Prediction: Feature Engineering, Neural Networks A data-driven approach to predict the success of bank telemarketing, DSS 2014.

21 Stock Market Prediction: Text mining, Sentiment Analysis Stock market sentiment lexicon acquisition using microblogging data and statistical measures, DSS New microblog financial lexicon:

22 Stock Market Prediction: Kalman Filter, Regression The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices, ESWA 2017.

23 Earthworks Prediction and Optimization: Regression, NSGAII An evolutionary multi-objective optimization system for earthworks, ESWA 2015.

24 Online News Prediction and Optimization: Text Mining, Random Forests, Stochastic Hill Climbing A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News, EPIA 2015.

25 Mobile Marketing: PROMOS Prediction and optimization of advertising campaigns for mobile devices Prediction and Optimization: Big Data, Classification, Modern Optimization R&D Project Reference: NORTH FEDER Funded by Portugal 2020/Adi Innovation Agency Promoter Entity: OLAMOBILE Co-promoter Entity: University of Minho Investment: 730 KEUR

26 Mobile Marketing: PROMOS Prediction and optimization of advertising campaigns for mobile devices Prediction and Optimization: Big Data, Classification, Modern Optimization

27 My contacts: URL: Linkedin: Thank you!