Data warehousing and mining Session VII (Part 1) 15:45 - 16:10 Sunita Sarawagi School of IT, IIT...

Data warehousing and mining

Session VII (Part 1) 15:45 - 16:10

Sunita Sarawagi

School of IT, IIT Bombay

Dr. Sunita Sarawagi Data Warehousing & Mining 2

Introduction• Organizations getting larger and amassing ever

increasing amounts of data• Historic data encodes useful information about working

of an organization.• However, data scattered across multiple sources, in

multiple formats.• Data warehousing: process of consolidating data in a

centralized location• Data mining: process of analyzing data to find useful

patterns and relationships


Typical data analysis tasks

• Report the per-capita deposits broken down by region and profession.

• Are deposits from rural coastal areas increasing over last five years?

• What percent of small business loans were cleared?• Why is it less than last year’s? How did similar

businesses that did not take loans perform?• What should be the new rules for loan eligibility?


Bombay branch Delhi branch Calcutta branchCensusdata

Operational data

Detailed transactionaldata

Data warehouseMergeCleanSummarize

DirectQuery

Reportingtools

MiningtoolsOLAP

Decision support tools

Oracle SAS

RelationalDBMS+e.g. Redbrick

IMS

Crystal reports Essbase Intelligent Miner

GISdata


Data warehouse construction• Heterogeneous data integration

– merge from various sources, fuzzy matches

– remove inconsistencies

• Data cleaning: – missing data, outliers, clean fields e.g. names/addresses

– Data mining techniques

• Data loading: summarize, create indices

• Products: Prism warehouse manager, Platinum info refiner, info pump, QDB, Vality


Warehouse maintenance

• Data refresh– when to refresh, what form to send updates?

• Materialized view maintenance with batch updates.

• Query evaluation using materialized views

• Monitoring and reporting tools– HP intelligent warehouse advisor


Bombay branch Delhi branch Calcutta branchCensusdata

Operational data

Detailed transactionaldata

Data warehouseMergeCleanSummarize

DirectQuery

Reportingtools

MiningtoolsOLAP

Decision support tools

Oracle SAS

RelationalDBMS+e.g. Redbrick

IMS

Crystal reports Essbase Intelligent Miner

GISdata


OLAPFast, interactive answers to large aggregate queries.• Multidimensional model: dimensions with hierarchies

– Dim 1: Bank location: • branch-->city-->state

– Dim 2: Customer:• sub profession --> profession

– Dim 3: Time:• month --> quarter --> year

• Measures: loan amount, #transactions, balance


OLAP

• Navigational operators: Pivot, drill-down, roll-up, select.

• Hypothesis driven search: E.g. factors affecting defaulters– view defaulting rate on age aggregated over other

dimensions

– for particular age segment detail along profession

• Need interactive response to aggregate queries..


OLAP products

• About 30 OLAP vendors

• Dominant ones:– Oracle Express: largest market share: 20%– Arbor Essbase: technology leader– Microsoft Plato: introduced late last year,

rapidly taking over...


Microsoft OLAP strategy• Plato: OLAP server: powerful, integrating various

operational sources • OLE-DB for OLAP: emerging industry standard

based on MDX --> extension of SQL for OLAP• Pivot-table services: integrate with Office 2000

– Every desktop will have OLAP capability.

• Client side caching and calculations• Partitioned and virtual cube• Hybrid relational and multidimensional storage


Data mining

• Process of semi-automatically analyzing large databases to find interesting and useful patterns

• Overlaps with machine learning, statistics, artificial intelligence and databases but– more scalable in number of features and instances– more automated to handle heterogeneous data


Some basic operations

• Predictive:– Regression– Classification

• Descriptive:– Clustering / similarity matching– Association rules and variants– Deviation detection


Classification

• Given old data about customers and payments, predict new applicant’s loan eligibility.

AgeSalaryProfessionLocationCustomer type

Previous customers Classifier Decision rules

Salary > 5 L

Prof. = Exec

New applicant’s data

Good/bad


Classification methods

• Nearest neighbor

• Regression: (linear or any polynomial) – a*salary + b*age + c = eligibility score.

• Decision tree classifier

• Probabilistic/generative models

• Neural networks


Clustering

• Unsupervised learning when old data with class labels not available e.g. when introducing a new product.

• Group/cluster existing customers based on time series of payment history such that similar customers in same cluster.

• Key requirement: Need a good measure of similarity between instances.

• Identify micro-markets and develop policies for each


Association rules

• Given set T of groups of items• Example: set of item sets purchased • Goal: find all rules on itemsets of the

form a-->b such that– support of a and b > user threshold s

– conditional probability (confidence) of b given a > user threshold c

• Example: Milk --> bread• Purchase of product A --> service B

Milk, cerealTea, milk

Tea, rice, bread

cereal

T


Mining market

• Around 20 to 30 mining tool vendors• Major players:

– Clementine,

– IBM’s Intelligent Miner,

– SGI’s MineSet,

– SAS’s Enterprise Miner.

• All pretty much the same set of tools• Many embedded products: fraud detection, electronic

commerce applications


Conclusions

• The value of warehousing and mining in effective decision making based on concrete evidence from old data

• Challenges of heterogeneity and scale in warehouse construction and maintenance

• Grades of data analysis tools: straight querying, reporting tools, multidimensional analysis and mining.

Data warehousing and mining Session VII (Part 1) 15:45 - 16:10 Sunita Sarawagi School of IT, IIT...

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