Lecture 11 - Big Data and Analytics_CTMod.pdf

Takes past financial data to produce a credit score

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Takes past financial data to produce a credit score Starts with decision trees, neural networks, and support vector machine (SVM) algorithms for initial model Next uses Predictive Modeling Markup Language (PMML) § Marketing Churn analysis: predicting which customers will leave using clustering via k-means algorithm Social media analysis using association rules Lee-Tsai-758Y-11 28
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Use of Prescriptive Analytics § Use of optimization and simulation tools for prescribing the best action to take § Example applications Making trading decisions in securities and stock market Making pricing decisions for airlines and hotels Making product recommendations (e.g. Amazon and Netflix) § Often requires predictive analytics and game theory Lee-Tsai-758Y-11 29
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Online Analytical Processing (OLAP) Tools § Online Analytical Processing (OLAP) the use of a set of graphical tools that provides users with multidimensional views of their data and allows to analyze the data using simple windowing techniques § Relational OLAP (ROLAP) view the database as a traditional relational database. § Multidimensional OLAP (MOLAP) load data into an intermediate structure using Cube structure. § Hybrid OLAP (HOLAP) Storage mode combines both MOLAP and ROLAP. Lee-Tsai-758Y-11 30
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Cube Operations § Slicing : select one dimension. § Dicing : select two or more dimensions. § Pivoting : rotate dimensional orientation of the cube. § Drill-down : navigates from less detailed data to more detailed data. § Roll-up : reduce dimension. Lee-Tsai-758Y-11 31
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Figure 11-12: Slicing a Data Cube Lee-Tsai-758Y-11 32
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Figure 11-13: Example of Drill-Down Lee-Tsai-758Y-11 33 Summary report Drill-down with color added Starting with summary data, users can obtain details for particular cells.
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Figure 11-14: Sample Pivot Table Lee-Tsai-758Y-11 34 four dimensions: Country (pages) Resort Name (rows) Travel Method, No. of Days (columns)
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SQL OLAP Querying § SQL is generally not an analytic language, but it can be used for analysis. § However, OLAP extensions to SQL make this easier. § OLAP queries should support: Categorization – e.g. group data by dimension characteristics Aggregation – e.g. create averages per category Ranking – e.g. find customer in some category with highest average monthly sales Lee-Tsai-758Y-11 35
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Regular SQL Query Lee-Tsai-758Y-11 36 TOP 1 can be removed
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OLAP SQL Query § SalesHistory (TerritoryID, Quarter, Sales) and the desire to show a three-quarter moving average of sales. Lee-Tsai-758Y-11 37 OVER (also called WINDOW) is a special clause that provide a “sliding view” of rows from a query. PARTITION BY is like a GROUP by for OVER.
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Data Visualization § Representation of data in graphical and multimedia formats for human analysis § “A picture tells a thousand words” § Without showing precise values, graphs and charts can depict relationships in the data § Often used in dashboards, as shown in next slide Lee-Tsai-758Y-11 38
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Figure 11-16: Sample Dashboard Lee-Tsai-758Y-11 39 Business Performance Management (BPM)
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