1. DBMS support of the Data Mining Advisor : S.-Y. Hwang Ph.D D954020005 Tsung-Hsien Yang D954020006 Shi-Hwao Wang 1/22/2008

2. Agenda  Introduction to Data Mining  The Promise of Data Mining  KDD Process  Data Mining Algorithms  Data Mining Modeling and Language  Conclusion

3. Introduction to Data Mining  The Explosive Growth of Data: from terabytes to petabytes Major sources of abundant data  Business: Web, e-commerce, transactions, stocks, …  Science: Remote sensing, bioinformatics, scientific simulation, …  Society and everyone: news, digital cameras, YouTube Data collection and data availability  Automated data collection tools, database systems, Web, computerized society

4. What Is Data Mining?  Data mining: Discovering interesting patterns from large amounts of data  Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data  Alternative names Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.  Watch out: Is everything “data mining”? Simple search and query processing (Deductive) expert systems

5. The Promise of Data Mining  Database analysis and decision support Market analysis and management  target marketing, customer relation management, market basket analysis, cross selling, market segmentation Risk analysis and management  Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and management  Other Applications Text mining (news group, email, documents) and Web analysis.

6. Knowledge Discovery (KDD) Process Data mining—core of knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation

7. Data preprocessing Data Mining Management System (DMMS) Mining Model Define a model Train the model Training Data Test the model Test Data Prediction using the model Prediction Input Data

8. Data Mining Algorithms  Decision Trees  Naïve Bayesian  Clustering  Sequence Clustering  Association Rules  Neural Network  Time Series  Support Vector Machines  ….

9. Data Mining Function  Classification (attribute)  Estimation (regression)  Prediction (time series)  Association (cross selling)  Clustering (segmentation)

10. Data Mining Algorithms √√√√√√ √√√√√ √√√ √√√√√√ √√√ √ √ Decision Trees Naïve Bayes Clustering Seq. Clustering Time Series Association rules Neural Network Classification Regression Segmentaion Assoc. Analysis Anomaly Detect. Seq. Analysis Time series √ - second choice √ - first choice

11. Data Mining Language  New challenges in data mining API Large spectrum of applications: embedded to interactive BI Interoperability between different DM providers (engine) and DM consumers (tools) Data independence between content representation (trees, attributes, networks, etc) and data mining task (prediction, scoring, etc)  Requirements: Algorithm-neutral Task-oriented (specification of what we need, rather than how to) Vendor-neutral Flexible, extensible, declarative/self-contained  Sound familiar?  Yes, SQL

12. DMX Approach  Data Mining Extensions (DMX) to SQL  Table vs. Mining Model TABLEMINING MODEL schemaColumn definitionAttribute (variable) definition containsRowsPatterns, knowledge, cases operatio ns DDL (create,drop,alter) Create/drop/alter a model DML (insert, delete)Train (populate) a model Query (select)Prediction/browsing a model

13. Typical DM Process Using DMX Data Mining Management System (DMMS) Mining Model Define a model : CREATE MINING MODEL …. Train a model : INSERT INTO dmm …. Training Data Prediction using a model : SELECT … FROM dmm PREDICTION JOIN … Prediction Input Data

14. Defining a DM Model  Defines Shape of “ training cases ” (top-level entity being modeled) Input/output attributes (variables): type, distribution Algorithms and parameters  Example CREATE MINING MODEL CollegePlanModel ( StudentID LONG KEY, Gender TEXT DISCRETE, ParentIncome LONG NORMAL CONTINUOUS, Encouragement TEXT DISCRETE, CollegePlans TEXT DISCRETE PREDICT ) USING Microsoft_Decision_Trees (complexity_penalty = 0.5)

15. Training a DM Model: Simple INSERT INTO CollegePlanModel (StudentID, Gender, ParentIncome, Encouragement, CollegePlans) OPENROWSET(‘ ’, ‘ ’, ‘SELECTStudentID, Gender, ParentIncome, Encouragement, CollegePlans FROM CollegePlansTrainData’)

16. Prediction Using a DM Model  PREDICTION JOIN SELECT t.ID, CPModel.Plan FROM CPModel PREDICTION JOIN OPENQUERY(…,‘SELECT * FROM NewStudents’) AS t ON CPModel.Gender = t.Gender AND CPModel.IQ = t.IQ IDGenderIQ IDGenderIQPlan CPModelNewStudents

17. Classification  Model Definition CREATE MINING MODEL CPClass ( StudentID LONG KEY, Gender TEXT DISCRETE, ParentIncome LONG CONTINUOUS, Encouragement TEXT DISCRETE, CollegePlans TEXT DISCRETE PREDICT ) USING Microsoft_Decision_Trees

18. Classification (cont)  Find the new students whose predicted class (CollegePlan) is ‘Yes’ with confidence > 0.8 SELECT StudentID, PredictProbability(CPClass.CollegePlan) FROM CPClass PREDICTION JOIN OPENROWSET (’ ’,’ ’, ’SELECT * FROM NewStudents’) AS t ON t. Gender = CPClass.Gender AND t. ParentIncome = CPClass. ParentIncome AND t. Encouragement = CPClass. Encouragement WHERE CPClass.CollegePlan = ‘Yes’ AND PredictProbability(CPClass.CollegePlan) > 0.8

19. Regression  Model Definition CREATE MINING MODEL CustCredit ( CustID LONG KEY, Gender TEXT DISCRETE, Age TEXT CONTINUOUS REGRESSOR, Income LONG CONTINUOUS REGRESSOR, Credit DOUBLE CONTINUOUS PREDICT ) USING Microsoft_Decision_Trees

20. Regression (cont)  Predict Credit score (and stdev) for the new customer data entered from the web form. SELECT CustCredit.Credit, PredictStdev(CustCredit.Credit) FROM CustCredit PREDICTION JOIN (SELECT ’Female’ AS Gender, 30 AS Age, 50000 AS Income) AS t ON t. Gender = CustCredit.Gender AND t. Age = CustCredit. Age AND t. Income = CustCredit. Income

21. Segmentation  Model Definition CREATE MINING MODEL CPCluster ( StudentID LONG KEY, Gender TEXT DISCRETE, ParentIncome LONG CONTINUOUS, Encouragement TEXT DISCRETE, CollegePlans TEXT DISCRETE ) USING Microsoft_Clustering

22. Segmentation (cont.)  Find cluster and its probability for each student SELECT StudentID, $Cluster, ClusterProbability() FROM CPCluster PREDICTION JOIN OPENROWSET (’ ’,’ ’, ’SELECT * FROM NewStudents’) AS t ON t. Gender = CPCluster.Gender AND t. ParentIncome = CPCluster. ParentIncome AND t. Encouragement = CPCluster. Encouragement AND t. CollegePlans = CPCluster. CollegePlans

23. Association Prediction  Model Definition CREATE MINING MODEL FavMovieModel ( IDLONG KEY, MaritalStatus TEXT DISCRETE, FavMoviesTABLE PREDICT ( TitleTEXT KEY ) ) USING Microsoft_Decision_Trees

24. Association Prediction (cont)  As a web application, find 5 best recommendations for a customer whose shopping cart contains ‘Star Wars’ and ‘Matrix’. SELECT FLATTENED PredictAssociation(FavMovieModel.FavMovies, INCLUDE_STATISTICS, 5) FROM FavMovieModel NATURAL PREDICTION JOIN (SELECT ’Single’ AS MaritalStatus, (SELECT ’Star Wars’ AS Title UNION SELECT ’Matrix’ AS Title) AS FavMovies) AS t

25. Sequence Prediction  Model Definition CREATE MINING MODEL WebSeqModel ( SessionLONG KEY, PageSeq TABLE PREDICT ( SeqIDLONG KEY SEQUENCE, PageTEXT DISCRETE ) ) USING Microsoft_Sequence_Clustering

26. Sequence Prediction (cont)  Show the next 2 steps that a web visitor who visited ‘home’  ‘news’ is going to take. For each step, it has to show top 5 candidate pages with the highest probability. SELECT FLATTENED ( SELECT $Sequence, TopCount(PredictHistogram(Page), $Probability, 5) FROM PredictSequence(WebSeqModel.PageSeq, 2) ) FROM WebSeqModel NATURAL PREDICTION JOIN (SELECT (SELECT 1 AS SeqID, ’home’ AS Page UNION SELECT 2 AS SeqID, ’news’ AS Page) AS PageSeq ) AS t

27. Time-Series Prediction  Model Definition CREATE MINING MODEL StockModel ( SymbolLONG KEY, DateRecordedDATE KEY TIME, OpeningQuote DOUBLE CONTINUOUS, ClosingQuoteDOUBLE CONTINUOUS ) USING Microsoft_Time_Series

28. Time-Series Prediction (cont)  Predict next five days of MSFT stock closing quotes. SELECT FLATTENED PredictTimeSeries(StockModel.ClosingQuote, 5) FROM FavMovieModel WHERE StockModel.Symbol = ’MSFT’

29. Major Issues in Data Mining  Mining methodology Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web Performance: efficiency, effectiveness, and scalability Pattern evaluation: the interestingness problem Incorporation of background knowledge Handling noise and incomplete data Parallel, distributed and incremental mining methods Integration of the discovered knowledge with existing one: knowledge fusion  User interaction Data mining query languages and ad-hoc mining Expression and visualization of data mining results Interactive mining of knowledge at multiple levels of abstraction  Applications and social impacts Domain-specific data mining & invisible data mining Protection of data security, integrity, and privacy

30. Data Mining Vendors  SAS (Enterprise Miner)  IBM (DB2 Intelligent Miner)  Oracle (ODM option to Oracle 10g)  SPSS (Clementine)  Insightsful (Insightful Miner)  KXEN (Analytic Framework)  Prudsys (Discoverer and its family)  Microsoft (SQL Server 2005)  Angoss (KnowledgeServer and its family)  DBMiner (DBMiner)  Many others

31. Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP

32. Data Mining Modeling and Language  Problem Description two powerful tools  Database management systems  Efficient and effective data mining algorithms and frameworks Generally, this work asks:  “How can we merge the two?”  “How can we integrate data mining more closely with traditional database systems, particularly querying?”

33. Three Different Answers  MSQL: A Query Language for Database Mining (Imielinski & Virmani, Rutgers University)  DMQL: A Data Mining Query Language for Relational Databases (Han et al, Simon Fraser University)  Integrating Data Mining with SQL Databases: OLE DB for Data Mining (Netz et al, Microsoft)

34. MSQL  Focus on Association Rules  Seeks to provide a language both to selectively generate rules, and separately to query the rule base  Expressive rule generation language, and techniques for optimizing some commands

35. MSQL  Get-Rules and Select-Rules Queries Get-Rules operator generates rules over elements of argument class C, which satisfy conditions described in the “where” clause [Project Body, Consequent, confidence, support] GetRules(C) [as R1] [into ] [where ] [sql-group-by clause] [using-clause]

36. MSQL  may contain a number of conditions, including: restrictions on the attributes in the body or consequent  “rule.body HAS {(Job = ‘Doctor’}”  “rule1.consequent IN rule2.body”  “rule.consequent IS {Age = *}” pruning conditions (restrict by support, confidence, or size) Stratified or correlated subqueries in, has, and is are rule subset, superset, and equality respectively

37. MSQL GetRules(Patients) where Body has {Age = *} and Support >.05 and Confidence >.7 and not exists ( GetRules(Patients) Support >.05 and Confidence >.7 and R2.Body HAS R1.Body) Retrieve all rules with descriptors of the form “Age = *” in the body, except when there is a rule with equal or greater support and confidence with a rule containing a superset of the descriptors in the body

38. MSQL GetRules(C) R1 where and not exists ( GetRules(C) R2 where and R2.Body HAS R1.Body) correlated stratified GetRules(C) R1 where and consequent is {(X=*)} and consequent in (SelectRules(R2) where consequent is {(X=*)}

39. MSQL  Nested Get-Rules Queries and their optimization Stratified (non-corrolated) queries are evaluated “bottom-up.” The subquery is evaluated first, and replaced with its results in the outer query. Correlated queries are evaluated either top-down or bottom- up (like “loop-unfolding”), and there are rules for choosing between the two options

40. MSQL GetRules(Patients) where Body has {Age = *} and Support >.05 and Confidence >.7 Top-Down Evaluation For each rule produced by the outer, evaluate the inner not exists ( GetRules(Patients) Support >.05 and Confidence >.7 and R2.Body HAS R1.Body)

41. MSQL not exists ( GetRules(Patients) Support >.05 and Confidence >.7 and R2.Body HAS R1.Body) Bottom-Up Evaluation For each rule produced by the inner, evaluate the outer GetRules(Patients) where Body has {Age = *} and Support >.05 and Confidence >.7

42. DMQL  Commands specify the following: The set of data relevant to the data mining task (the training set) The kinds of knowledge to be discovered  Generalized relation  Characteristic rules  Discriminant rules  Classification rules  Association rules

43. DMQL  Commands Specify the following: Background knowledge  Concept hierarchies based on attribute relationships, etc. Various thresholds  Minimum support, confidence, etc.

44. DMQL  Syntax use database {use hierarchy for } related to from [where ] [order by ] {with [ ] threshold = [for ]} Specify background knowledge Specify rules to be discovered Collect the set of relevant data to mine Specify threshold parameters Relevant attributes or aggregations

45. DMQL use database Hospital find association rules as Heart_Health related to Salary, Age, Smoker, Heart_Disease from Patient_Financial f, Patient_Medical m where f.ID = m.ID and m.age >= 18 with support threshold =.05 with confidence threshold =.7

46. DMQL  DMQL provides a display in command to view resulting rules, but no advanced way to query them  Suggests that a GUI interface might aid in the presentation of these results in different forms (charts, graphs, etc.)

47. OLE DB for DM  An extension to the OLE DB interface for Microsoft SQL Server  Seeks to support the following ideas: Define a model by specifying the set of attributes to be predicted, the attributes used for the prediction, and the algorithm Populate the model using the training data Predict attributes for new data using the populated model Browse the mining model (not fully addressed because it varies a lot by model type)

48. OLE DB for DM  Defining a Mining Model Identify the set of data attributes to be predicted, the set of attributes to be used for prediction, and the algorithm to be used for building the model  Populating the Model Pull the information into a single rowset using views, and train the model using the data and algorithm specified

49. OLE DB for DM  Using the mining model to predict Defines a new operator prediction join. A model may be used to make predictions on datasets by taking the prediction join of the mining model and the data set.

50. OLE DB for DM CREATE MINING MODEL Heart_Health Prediction ( ID Int Key, Age Int, Smoker Int, Salary Double discretized, HeartAttack Int PREDICT, %Prediction column ) USING Microsoft_Decision_Trees Identifies the source columns for the training data, the column to be predicted, and the data mining algorithm.

51. OLE DB for DM INSERT INTO Heart_Health Prediction (Age, Smoker, Salary, HeartAttack ) OPENROWSET (’ ’,’ ’, ’SELECT Age, Smoker, Salary, HeartAttack FROM Patient_Medical M, Patient_Financial F WHERE M.ID = F.ID’) The INSERT represents using a tuple for training the model (not actually inserting it into the rowset).

52. OLE DB for DM SELECT T.ID, H.HeartAttack FROM Heart_Health Prediction H PREDICTION JOIN ( OPENROWSET (’ ’,’ ’, ’SELECT ID, Age, Smoker, Salary FROM Patient_Medical M, Patient_Financial F WHERE M.ID = F.ID’) as T ON H.Age = T.Age AND H.Smoker = T.Smoker AND H.Salary = T.Salary Prediction join connects the model and an actual data table to make predictions

53. Key Ideas  Important to have an API for creating and manipulating data mining models  The data is already in the DBMS, so it makes sense to do the data mining where the data is  Applications already use SQL, so a SQL extension seems logical

54. Key Ideas  Need a method for defining data mining models, including algorithm specification, specification of various parameters, and training set specification (DMQL, MSQL, ODBDM)  Need a method of querying the models (MSQL)  Need a way of using the data mining model to interact with other data in the database, for purposes such as prediction (ODBDM)