MATLAB for Data Analytics The MathWorks, Inc. 1

Transcription

1 MATLAB for Analytics 2016 The MathWorks, Inc. 1

2 Railway Automotive Aeronautics Retail Finance Off-highway vehicles Prognostics Fleet Analytics Condition Monitoring Retail Analytics Operational Analytics Internet Industrial Automation Process Analytics Risk Analysis Oil & Gas Health Monitoring Clean Energy Asset Analytics Medical Devices Supply Chain Mfg Process Analytics Healthcare Analytics Healthcare Management Logistics 2

3 What is Analytics? Turn large volumes of complex data into actionable information Descriptive What happened? Diagnostics Why did it happen? Predictive What will happen? Decisions Prescriptive What should be done? 3

4 Access and Explore Preprocess Develop Predictive Models Integrate Analytics with Files Working with Messy Model Creation e.g. Machine Learning Desktop Apps bases Reduction/ Transformation Parameter Optimization Enterprise Scale Sensors Feature Extraction Model Validation Embedded Devices and Hardware 4

5 Access and Explore Preprocess Develop Predictive Models Integrate Analytics with Files Working with Messy Model Creation e.g. Machine Learning Desktop Apps bases 1 Reduction/ Transformation Parameter Optimization Enterprise Scale Sensors Feature Extraction Model Validation Embedded Devices and Hardware 5

6 Access and Explore Preprocess Files bases Sensors Business and Transactional Repositories SQL, NoSQL, etc. File I/O Text, Spreadsheet, etc. Web Sources RESTful, JSON, etc. Engineering, Scientific and Field Real-Time Sources Sensors, GPS, etc. File I/O Image, Audio, etc. Communication Protocols OPC (OLE for Process Control), CAN (Controller Area Network), etc. Working with Messy Reduction/ Transformation Feature Extraction 6

7 Access and Explore Preprocess Files Challenges aggregation Different sources (files, web, etc.) Different types (images, text, audio, etc.) Working with Messy bases clean up Poorly formatted files Irregularly sampled data Redundant data, outliers, missing data etc. Reduction/ Transformation Sensors Domain specific processing Signals: Smoothing, resampling, denoising, Wavelet transforms, etc. Images: Image registration, morphological filtering, deblurring, etc. Feature Extraction Dealing with out of memory data (big data) 7

8 Access and Explore MATLAB Analytics work with business and engineering data 1 Preprocess Files Working with Messy bases Files bases Reduction/ Transformation Signals Images Sensors Point and click tools to access variety of data sources High-performance environment for big data Built-in algorithms for data preprocessing including sensor, image, audio, video and other real-time data Feature Extraction 8

9 Access and Explore Preprocess Develop Predictive Models Integrate Analytics with Files Working with Messy Model Creation e.g. Machine Learning Desktop Apps bases Reduction/ Transformation 1 2 Parameter Optimization Enterprise Scale Sensors Feature Extraction Model Validation Embedded Devices and Hardware 9

10 Preprocess Develop Predictive Models Working with Messy Reduction/ Transformation Feature Extraction Challenges Lack of data science expertise Feature Extraction How to transform data to best represent the system? Requires subject matter expertise No right way of designing features Feature Selection What attributes or subset of data to use? Entails a lot of iteration Trial and error Difficult to evaluate features Model Development Many different models Model Validation and Tuning Model Creation e.g. Machine Learning Parameter Optimization Model Validation Time required to conduct the analysis 10

11 Preprocess MATLAB enables domain experts to do Science 2 Develop Predictive Models Working with Messy Apps Language Model Creation e.g. Machine Learning Reduction/ Transformation Parameter Optimization Feature Extraction Easy to use apps Wide breadth of tools to facilitate Automatic MATLAB code generation Model Validation domain specific analysis High speed processing of large Examples/videos to get started data sets 11

12 Access and Explore Preprocess Develop Predictive Models Integrate Analytics with Files Working with Messy Model Creation e.g. Machine Learning Desktop Apps bases Reduction/ Transformation Parameter Optimization Enterprise Scale Sensors Feature Extraction Model Validation Embedded Devices and Hardware 12

13 Develop Predictive Models Integrate Analytics with Model Creation e.g. Machine Learning Challenges End user: Operators, Analysts, Administrative Staff, customers etc. Desktop Apps Parameter Optimization Different target platforms: Cluster or Cloud environment Standalone desktop applications Server based Web and enterprise systems Embedded hardware Enterprise Scale Model Validation Different Interfaces: C++, Java, Python,.NET etc. Need to translate analytics to production environment Embedded Devices and Hardware 13

14 Integrate analytics with systems MATLAB Analytics run anywhere 3 Embedded Hardware Enterprise C, C++ HDL PLC Standalone Application Excel Add-in Hadoop/ Spark C/C++ Java ++ Python.NET MATLAB Production Server MATLAB Runtime 14

15 Request Broker Deployed Analytics MATLAB Production Server Web Application Server Apache Tomcat MATLAB Production Server MATLAB Production Server MATLAB Desktop Train in MATLAB Web Server/ Webservice Predictive Models CTF Weather Energy 15

16 Key Takeaways MATLAB Analytics work with business and engineering data 1 MATLAB enables domain experts to do Science 2 3 MATLAB Analytics run anywhere 16

17 MathWorks Services Consulting Integration analysis/visualization Unify workflows, models, data Training Classroom, online, on-site Processing, Visualization, Deployment, Parallel Computing 17

18 2016 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders The MathWorks, Inc. 18