Sunnie Chung. Cleveland State University

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1 Sunnie Chung Cleveland State University

2 Data Scientist Big Data Processing Data Mining 2

3 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills: to Handle Big Data to Collect, Process and Extract value from Big Data (giant and diverse data sets) to Understand, Visualize and Present their findings to non-data scientists Ability to Create Data-driven Solutions that boost profits, reduce costs and even help save the world 3

4 And tackle big data projects on every level Big Data and Cloud Projects are in Every CEO s To Do List The Defense Department NASA : Predict Earthquake (specially after Nepal s Earthquake) NSA, Homeland Security : Predict and Prevent Terrorists Acts Internet start-ups Financial institutions 4

5 Volume : Unprecedentedly Huge Volume of Data fueled by web based business, social networking, micro blogs (e.g., click streams captured in web server logs) e.g.) Ebay processes 8 Peta Bytes data per night Various Structures of Data (No Structure) : Structured (Database, Data Warehouse) Semi-structured (Web pages) and Unstructured (Web Server Log, Sensor Data) most of time!! Velocity : Unprecedentedly generate new data at a high rate e.g.) Streaming Twitter Messages Machine-generated data streaming in from smart devices, sensors, monitors and meters needs big data analytics 5

6 Numerous new analytic and business intelligence opportunities like: Fraud detection Customer profiling Customer loyalty analysis All of which directly affect revenue of business and critical business decisions. 6

7 Identifying Field Specific Motive/Purposes Identify Nature of Big Data Source and Data Specific Processes Decisions on Building IT Infrastructure of Big Data Processing Systems Public Cloud/Private Cloud Which MPP Big Data Systems should be built for our specific Big Data Source and Volume Execution of Data Analytics Data Source Modeling Apply Data Mining Strategies Research solutions Implement Big Data Processing Steps for Solutions/Strategies Analyze Results/Interpretation -- Feedback 7

8 Massively Parallel Processing (MPP) Systems Parallel Data Warehouse (PDW) System Oracle, IBM, Teradata, Microsoft Hadoop System with Map Reduce Hive, Hbase, MongoDB, Cassandra, and more by Google, Yahoo, Facebook, Twitter, LinkedIn Hybrid of Both MPP System on Cloud Amazon, Google, Microsoft, Oracle 8

9 Massively Parallel Processing (MPP) Systems Virtual Machine (VM) Cloud Type Cloud as Service Cloud as Platform Cloud as Service Amazon Elastic Cloud Computing (EC2) Google Cloud Microsoft Cloud: Azure 9

10 Anomaly detection The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (Dependency modelling) Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering The task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification The task of generalizing known structure to apply to new data. For example, an program might attempt to classify an as "legitimate" or as "spam". Regression attempts to find a function which models the data with the least error. Summarization Providing a more compact representation of the data set, including visualization and report generation. Results validation 10

11 Statistics Naive Bayes, Clustering Machine Learning Classification Algorithms: Decision Tree, Neural Network, Support Vector Machine New Algorithm: Convolutional Neural Network - still evolving in fast rate Database Association Rule Mining, Data Warehouse OLAP Big Data Processing Most Recent - still evolving in fast rate Information Retrieval Google Search Engine -> Artificial Intelligence - still evolving in fast rate 11

12 Databases Advanced Modern Databases and Data Processing Strategies Big Data Processing with: Parallel Data Warehouse and OLAP (Online Analytic Processing) Map Reduce Hadoop Based MPP Systems Statistics Data Mining - Database: Association Rule Mining, Data Warehouse OLAP - Statistics: Bayesian, Clustering - Machine Learning: SVM, Neural Network: CNN, RNN, LSTM And More on recent developments 12

13 Massively Paralle Processing (MPP) Systems Parallel Data Warehouse Based Systems : Oracle, Tera Data, Microsoft PDW, IBM In Memeory NEW SQL Systems Hadoop/MapReduce Based Systems: No SQL systems Mongo DB Pig Latin Hbase Hive Stream Processing: Spark Cloud: Big Data Processing Systems on Cloud Google Cloud, Amazon Cloud, Microsoft Azure, Oracle, IBM 13

14 14

15 Popular Free Open Source R/ Map R: A programming language and software environment for statistical computing, data mining, and graphics. GNU Project. Sparks: Streaming Data Processing Google Tensorflow: Python, C++ based Image Processing Library, Natural Language Processing Libraray Weka: A suite of machine learning software applications written in the Java programming language UIMA:(Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video originally developed by IBM Major Commercial: SAS Enterprise Miner Microsoft Business Intelligence Data Analytic Tool using Databases 15

16 On Databases CIS 530 : Intro to Database Systems and Processing CIS 611 : Enterprise Database Systems and Data Warehouse - Advanced Data Processing Techniques - Parallel Data Warehouse and OLAP - Big Data Processing and Management Systems CIS 612 : Big Data and Parallel Data Processing Systems - Hadoop and MapReduce - NoSQL Systems on VM(Virtual Machine), Cloud - Stream Data Processing: Spark CIS 695: Practicum in Data Analytics and Big Data Processing (Scheduled to be created) 16

17 Data Analytics CIS 660: Data Mining Techniques from Database, Statistics and Machin Learning, Text/Web Mining Techniques EEC 525 Data Mining 17

18 Math and Statistics Graduate Certificate in Applied Predictive Modeling MTH 521 : Time Series Analysis MTH 531 : Categorical Data Analysis MTH 537 : Operation Research MTH 567 : Applied Linear Models I MTH 638 : Operation Research II MTH 668 : Applied Linear Models II MTH 675 : Applied Multivariate Statistics 18

19 Business Analytic Certificates Focus on SAS Certificate with SAS Enterprise Miner Tool BUS 575 : Introduction to Business Analytics BUS 600 : Applied Business Analytics BUS 601 : Managing Databases for Business Analytics BUS 602 : Strategy for Business Analytics BUS 603 : SAS for Data and Statistical Analysis BUS 604: Advanced Business Analytics I BUS 606: Practicum in Business Analytics 19

20 Explorys by IBM website: Data Analytic/ Big Data Processing on Health and Wellness Data Data Analytic for Cleveland Clinic (Tera Data PDW), Metro Health Progressive Big Data Processing on Auto Insurance : Hadoop Based MPP Systems PNC (Tera Data MPP PDW) Big Data Processing Systems on Financial Data 20

21 Hadoop Big Data Processing Workshop/Meetup EECS Dept of CSU Planning to host the meeting annually to connect our students to the local Big Data Companies Data Scientist Group Regular webinar on Advanced Data Analytic Topics 21

22 Current Research/Publications at CSU (by Sunnie Chung) Research on Big Data Analytics on Real Time Sentiment Analysis Research on Natural Language Processing with Machine Learning Research on Cyber Security with Data Analytics Methods Research on Question Answering Systems - Data Analytics Applications Research on Data Mining for Machine Fault Detection Research on Optimizations in MPP Systems Research on Integrating Big Data Management Systems 22

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