Big Data Analytics. What to Do with Big Data? V. CHRISTOPHIDES. Department of Computer Science University of Crete. Data contains value and knowledge

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1 Big Data Analytics V. CHRISTOPHIDES Department of Computer Science University of Crete 1 What to Do with Big Data? Data contains value and knowledge But to extract the knowledge data needs to be Stored Managed And ANALYZED Data Analysis include: Mine/summarize large datasets Extract knowledge from past data Predict trends in future data Data Mining Big Data Data Analytics Data Science 2 1 1

2 A Bit of Terminology Data mining is the old big data: an overused term including anything such as collecting, storing, curating and visualizing data machine learning / AI (which predates the term data mining) non-ml data mining (as in "knowledge discovery", where the focus is on new knowledge, not on learning of existing knowledge) "Business intelligence", "business analytics are marketing terms stressing that more data leads to better business decisions (periodic reporting as well as ad hoc queries, importance of tools and dashboards); Most "Big Data" today isn't ML: It's Extract, Transform, Load (ETL), so it is replacing data warehousing (except computational advertisement) Business Intelligence aims at descriptive statistics with data with high information density to measure things, detect trends etc. Big Data targets inductive statistics with data with low information density whose huge volume allow to infer laws (regressions ) and thus giving (with the limits of inference reasoning) to Big Data some predictive capabilities (called Deep Analytics) 3 Data Analysis: ERP & CRM Examples What is the most effective distribution channel? Who are our lowest/highest margin customers? Who are my customers and what products are they buying? What product prom- -otions have the biggest impact on revenue? Agrawal et al., VLDB 2010 Tutorial What impact will new products/services have on revenue and margins? Which customers are most likely to go to the competition? 4 2 2

3 Data Analysis Examples in Urban Computing What would the impacts be of a Fare change? Where are our lowest/highest margin passengers? What is the distribution of trip lengths? What is the quickest route from midtown To downtown at 4pm on Monday? Agrawal et al., VLDB 2010 Tutorial What impact will the introduction of additional medallions have? Where should drivers go to get passengers? 5 Data Analysis in Computational Advertizing&Marketing Computational advertising finds the best match between a given user in a given context and a suitable advertisement In 2011, over $100 billion was spent In online advertising (emarketer) A modern advertising analytic platform: Will build behavioral profiles on 100 million plus individuals Use full statistical models (not rules) for targeting Re-analyze all of the data each night Serve 10,000 s of ads per second using statistical models Respond <100ms (with analytics < 10 ms) Use real time geolocation data Do analytics at machine speed Be driven by an analyst with only modest training A. Broder, V. Josifovski, Introduction to computational advertising Autumn

4 Recommendation as Data Mining Estimate a utility function that automatically predicts how a user will like an item Based on: Past behavior Relations to other users Item similarity Context Web search context (SERP advertising) Web page content context (content match advertising and banners) Mobile, ambient context The Recommender Problem Revisited X. Amatriain B. Mobasher KDD 2014 Tutorial 7 Data Information Knowledge Wisdom Hierarchy Cognitive knowledge: "know-what" Advanced skills: "know-how" Systems understanding: "know-why" Self-motivated creativity: "care-why" 8 4 4

5 Interestingness of Patterns Interestingness criteria: Understandable: humans should be able to easily interpret the pattern Valid: hold on new or test data with some certainty Useful: should be possible to act on Unexpected: non-obvious to the system that validates some hypothesis that an analyst seeks to confirm Objective vs. subjective interestingness measures Objective: based on statistics and structures of patterns, e.g., support, confidence, etc. Subjective: based on user s beliefs in the data, e.g., unexpectedness, novelty, actionable, etc. 9 The Data Analysis Spectrum Source: Gartner Value Why did it happen? What happened? Descriptive Analytics Diagnostic Analytics How can we make it happen? Prescriptive Analytics What might happen? Predictive Analytics What is happening? Monitoring (Dashboards, Scorecards) Difficulty

6 Data Mining Methods Use some variables to predict unknown or future values of other variables Find human-interpretable patterns that describe the data 11 Large-Scale, Real-World Analytics Question How can I identify fraudulent activity? How do I segment my customers? Does this product appeal to some segments more than others? Which campaign is working better? How is product ownership distributed across customer segments? How do I target my marketing efforts towards customers most likely to churn? What new products should I offer my customers? What are my customers saying about the new product launch? Method Variable Selection, Logistic Regression K-means Clustering Log-likelihood Mann-Whitney U Test SQL, Cumulative Distribution Functions Logistic Regression Cosine similarity, k-nearest Neighbors MapReduce, NLP, sparse vectors Tools and Technologies for Big Data Steven HillionV.P. Analytics EMC Data Computing Division

7 Algorithmic vs. Statistical Perspectives Computer Scientists Data: are a record of everything that happened Goal: process the data to find interesting patterns and associations Methodology: Develop approximation algorithms under different models of data access since the goal is typically computationally hard Statisticians Data: are a particular random instantiation of an underlying process describing unobserved patterns in the world Goal: is to extract information about the world from noisy data Methodology: Make inferences (perhaps about unseen events) by positing a model that describes the random variability of the data around the deterministic model 13 The Two Perspectives are NOT Incompatible Statistical/probabilistic ideas are central to recent work on developing improved randomized algorithms for matrix problems Intractable optimization problems on graphs/networks yield to approximation when assumptions are made about network participants In boosting (a statistical technique that fits an additive model by minimizing an objective function with a method such as gradient descent), the computation parameter (i.e., the number of iterations) also serves as a regularization parameter Algorithmic and Statistical Perspectives on Large-Scale Data Analysis Michael W. Mahoney Stanford University Feb

8 Data Mining: Different Cultures Data mining overlaps with: Databases (DB): Large-scale data, simple queries Machine Learning (ML): Small data, Complex models Computer Science Theory: (Randomized) Algorithms Different cultures: To a DB person, data mining is an extreme form of analytic processing queries that examine large amounts of data Result is the query answer To a ML person, data-mining is the inference of models Result is the parameters of the model Machine Learning/ Big Data urges for a cross-culture curriculum stressing Statistics on Scalability /AI Algorithms Data Pattern Computing architectures Mining Recognition Automation for handling large data Database systems 15 Small Data are Good but Big - Data Mining The differences, gains and application areas Peter Cochrane

9 No Data Like More Data! Big - Data Mining The differences, gains and application areas Peter Cochrane 17 Underlying Motivation: The Law of Large Numbers! In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed. [Wikipedia]

10 10 10 A Note on the Meaningfulness of Mined Patterns A big data-mining risk is that you may discover patterns that are meaningless Bonferroni s principle: (roughly) if you look in more places for interesting patterns than your amount of data will support, you are bound to find crap Example: Rhine Paradox Joseph Rhine was a parapsychologist in the 1950 s who hypothesized that some people had Extra-Sensory Perception (ESP) He devised an experiment where subjects were asked to guess 10 hidden cards: red or blue He discovered that almost 1 in 1000 had ESP: they were able to get all 10 right! He told these people they had ESP and called them in for another test of the same type Alas, he discovered that almost all of them had lost their ESP What did he conclude? 19 Extracting Knowledge From (Big) Data Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns and make predictions KDD : Knowledge Discovery from Databases Iterative and Interactive Process Need to manage the data exploration process 20

11 11 11 We ve Moved into a New Era of Data Analytics 12+ terabytes of Tweets create daily. 5+ million trade events per second. Volume Velocity 100 s of different types of data. Variety Veracity Only1 in 3 decision makers trust their information. 21 Big Data: Small Analysis vs. Big Analysis If you want to analyze the whole set by accessing data several times, it can be much harder Many trial-and-error steps, easy to get lost Most existing data mining/ml methods were designed without considering data access and communication of intermediate results They iteratively use data by assuming they are readily available How to integrate these two worlds together? Efficient in analyzing/mining data Do not scale Efficient in managing big data Does not analyze or mine the data 22

12 12 12 Big Data: Small Analysis vs. Big Analysis Lots of intensive computations for complex math operations need new support in parallel/distributed settings Matrix multiplication QR decomposition (QR factorization) Singular Value Decomposition (SVD) Linear regression So we are facing many challenges methods not ready tools are not convenient platforms rapidly change, 23 Big Data Analytics This is an on-going research topic Roughly there are two types of approaches Parallelize existing (single-machine) algorithms Design new algorithms particularly for distributed settings of course there are things in between To have technical breakthroughs for bigdata analytics, we should know both algorithms and systems well, and consider them together Focused Services (Deep Insights) Deep Analytics Big Data Platform 24

13 13 13 The Evolution of Business Intelligence Speed BI Reporting OLAP & Dataware house Interactive Business Intelligence & In-memory RDBMS Scale QliqView, Tableau, HANA Big Data: Batch Processing & Distributed Data Store Business Objects, Scale SAS, Informatica, Cognos other SQL Reporting Tools Hadoop/Spark; HBase/Cassandra 1990 s 2000 s 2010 s Big Data: Real Time & Single View Graph Databases Speed 25 Example: Matrix-Matrix Product on One Machine Have you ever worried about calculating a math operation in one computer? Probably not: You can use Excel, statistical software (e.g., R, SAS), and many things else and we seldom care internally how these tools work Consider a simple operation like matrix-matrix products: where A segment of C code (assume n = m here) for (i=0;i<n;i++) for (j=0;j<n;j++) { c[i][j]=0; for (k=0;k<n;k++) c[i][j] += a[i][k]*b[k][j]; } Big-data Analytics: Challenges and Opportunities Chih-Jen Lin Department of Computer Science National Taiwan University August 30,

14 14 14 Example: Matrix-matrix Product (Cont'd) For 3000 x 3000 matrices $ gcc -O3 mat.c $ time./a.out 3m24.843s But on Matlab (single-thread mode) $matlab -singlecompthread >> tic; c = a*b; toc Elapsed time is seconds How can Matlab be much faster than ours? The fast implementation comes from some deep R&D Matlab calls optimized BLAS (Basic Linear Algebra Subroutines) that was developed in 80's-90's Our implementation is slow because data are not available for computation Big-data Analytics: Challenges and Opportunities Chih-Jen Lin Department of Computer Science National Taiwan University August 30, Example: Matrix-Matrix Product (Cont'd) increasing in capacity decreasing in speed Optimized BLAS: try to make data available in a higher level of memory You don't waste time to frequently move data Big-data Analytics: Challenges and Opportunities Chih-Jen Lin Department of Computer Science National Taiwan University August 30,

15 15 15 Example: Matrix-Matrix Product (Cont'd) Optimized BLAS uses block algorithms If we compare the number of page faults (cache misses) Ours: much larger Block: much smaller For big-data analytics, we want to run mathematical algorithms (classification and clustering) in a complicated architecture (distributed system) But we are like at the time point before optimized BLAS was developed Big-data Analytics: Challenges and Opportunities Chih-Jen Lin Department of Computer Science National Taiwan University August 30, Big Data Mining Challenges Analytics Architecture combine historical with real-time data at the same time Statistical Significance achieve significant statistical results, and not be fooled by randomness Distributed Mining paralyze data mining techniques with practical & theoretical guarantees Stream Mining learn from evolving data streams Hidden Big Data currently only 3% of the potentially useful data is tagged, and even less is analyzed Sampling and Compression subsampling is easy on one machine, but may not be in a distributed 30 setting

16 16 16 Big Data Mining Projects Apache Mahout Open-source package on Hadoop for data mining and machine learning Revolution R (R-Hadoop) Extensions to R package to run on Hadoop 31 Other Aspects of BIG Data Bigger Data are not always Better Data Big will evolve/change Not all Data are equivalent Just because it is accessible doesn t make it ethical 32

17 17 17 References CS246: Mining Massive Datasets Jure Leskovec, Stanford University, 1014 CS525: Special Topics in DBs Large-Scale Data Management Advanced Analytics on Hadoop Mohamed Eltabakh Spring 2013 Big-data Analytics: Challenges and Opportunities Chih-Jen Lin Department of Computer Science National Taiwan University August 30, 2014 Knowledge Discovery and Data Mining Evgueni Smirnov Maastricht School on Data Mining Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands August 27 - August 30,

18 18 18 Big Data Processing and Analysis Framework

19 What Matters when Mining Data J. Leskovec, Stanford CS246: Mining Massive Datasets 38

20 20 20 Data-Mining Tasks Classification Task Regression Task Clustering Task Association-Rule Task 39 Classification Task Given: a collection of instances (training set) Each instances is represented by a set of attributes, one of the attributes is the class attribute Find: a classifier for the class attribute as a function of the values of other attributes Goal: previously unseen instances should be assigned a class as accurately as possible 40

21 Example 1 Tid Refund Marital Status Taxable Income Cheat Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes No Single 75K? Yes Married 50K? No Married 150K? Yes Divorced 90K? No Single 40K? No Married 80K? Test Set 9 No Married 75K No 10 No Single 90K Yes Training Set Learn Classifier Classifier 41 Example 2 Fraud Detection Goal: Predict fraudulent cases in credit card transactions. Approach: Use credit card transactions and the information on its accountholder as attributes When does a customer buy, what does he buy, how often he pays on time, etc Label past transactions as fraud or fair transactions. This forms the class attribute Learn a model for the class of the transactions Use this model to detect fraud by observing credit card transactions on an account 42

22 22 22 Regression Task Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency Examples: Predicting sales amounts of new product based on advertising expenditure Predicting wind velocities as a function of temperature, humidity, air pressure, etc. Time series prediction of stock market indices 43 Clustering Task Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that: Data points in one cluster are more similar; Data points in separate clusters are less similar Intra-cluster distances are minimized Inter-cluster distances are maximized 44

23 23 23 Example Market Segmentation: Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix Approach: Collect different attributes of customers based on their geographical and lifestyle related information Find clusters of similar customers Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters 45 Association-Rule Task Given a set of records each of which contain some number of items from a given collection; Produce dependency rules which will predict occurrence of an item based on occurrences of other items TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk Rules Discovered: Milk --> Coke Diaper, Milk --> Beer 46

24 24 24 Example Supermarket shelf management Goal: To identify items that are bought together by sufficiently many customers Approach: Process the point-of-sale data collected with barcode scanners to find dependencies among items. A classic rule -- If a customer buys diaper and milk, then he is very likely to buy beer So, don t be surprised if you find six-packs stacked next to diapers! 47

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