Penn State Student Chapter of the Association for Computing Machinery

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Penn State Student Chapter of the Association for Computing Machinery We welcome all interested students to our 4th general meeting of the Spring 2005 semester!

1 Penn State Student Chapter of the Association for Computing Machinery We welcome all interested students to our 4th general meeting of the Spring 2005 semester! When: Monday, April 11th, 2005 from 7-8 pm Where: Cybertorium (213 IST) Agenda: Brief overview of our ACM chapter New officer introductions Special topic presentation: No Pain, No Game Presented by IST Professor Brian K. Smith Co-op/Intern presentation: Working at IBM Presented by Rick Osowski Free refreshments will be provided

2 Data Warehousing, Data Mining, IST 210 and Advanced Applications 2

Data Rich, but Information Poor Data is stored, not explored : by its volume and complexity it represents a burden, not a support Data overload results in uninformed

3 Data Rich, but Information Poor Data is stored, not explored : by its volume and complexity it represents a burden, not a support Data overload results in uninformed decisions, contradictory information, higher overhead, wrong decisions, increased costs Data is not designed and is not structured for successful management decision making 3

4 Improving Decision Making Decisions Data Information Warehouse Data 4

5 Data Warehouse Concepts 5

6 What s a Data Warehouse? A data warehouse is a single, integrated source of decision support information formed by collecting data from multiple sources, internal to the organization as well as external, and transforming and summarising this information to enable improved decision making. A data warehouse is designed for easy access by users to large amounts of information, and data access is typically supported by specialized analytical tools and applications. 6

7 Data Warehouse Characteristics Key Characteristics of a Data Warehouse Subject-oriented Integrated Time-variant Non-volatile 7

8 Subject Oriented Example for an insurance company : Applications Area Data Warehouse Commercial Commercial and and Life Life Insurance Insurance Systems Systems Auto Auto and and Fire Fire Policy Policy Processing Processing Systems Systems Customer Customer Policy Policy Data Data Accounting Accounting System System Billing Billing System System Claims Claims Processing Processing System System Losses Losses Premium Premium 8

9 Integrated Data is stored once in a single integrated location (e.g. insurance company) Customer data stored in several databases Auto Auto Policy Policy Processing Processing System System Fire Fire Policy Policy Processing Processing System System FACTS, FACTS, LIFE LIFE Commercial, Commercial, Accounting Accounting Applications Applications Data Warehouse Database Subject = Customer 9

10 Time - Variant Data is stored as a series of snapshots or views which record how it is collected across time. Data Warehouse Data Time Data Key Data is tagged with some element of time - creation date, as of date, etc. Data is available on-line for long periods of time for trend analysis and forecasting. For example, five or more years 10

11 Non-Volatile Existing data in the warehouse is not overwritten or updated. External Sources Production Applications Production Databases Data Warehouse Environment Data Warehouse Database Update Insert Delete Load Read-Only 11

12 Transaction System vs. Data Warehouse 12

13 Transaction-Based Reporting System Day-to-day operations On-line, real time update into disparate systems System Experts Data Manipulation Users Unix VMS MVS Other 13

14 Warehouse-Based Reporting System Unix Executive Reporting and On-Line Analysis VMS MVS Interfaces Data Data Staging, Staging, Transformation Transformation and and Cleansing Cleansing Data Warehouse Summarization Other Environment OLAP BENEFIT: Reduce data processing costs BENEFIT: Integrated, consistent data available for analysis BENEFIT: Improve Network Reporting processes and analytical capabilities 14

15 Transaction - Warehouse Process Day-to-day operations Transaction Based Process On-line, real time update. Detailed Information to operational systems. Decision support for management use. Warehouse Based Process Summarize & Refine Transform Batch Load 15

16 Transaction System vs. Data Warehouse Transaction System Supports day-to-day operational processes Contains raw, detailed data that has not been refined or cleansed Volatile -- data changes from day-to-day, with frequent updates Technical issues drive the data structure and system design Disparate data structures, physical locations, query types, etc. Users rely on technical analysts for reporting needs Operational processes impacted by queries run off of system Data Warehouse Supports management analysis and decisionmaking processes Contains summarized, refined, and cleansed information Non-volatile -- provides a data snapshot ; adjustments are not permitted, or are limited Business analysis requirements drive the data structure and system design Integrated, consistent information on a single technology platform Users have direct, fast access via On-line Analytical Processing tools Minimal impact on operational processes 16

17 Data Warehouse Architecture 17

18 Data Warehouse Architecture Operational System Data Warehouse Ad-hoc Reporting Conversion & Interface OLAP Cubes ODS Staging Area Data Marts Canned Reports 18

19 Data Warehouse Architecture Conversion and Cleansing Activities Conversion & Cleansing Map source data to target Data scrubbing Derive new data Data Extraction Transform / convert data Create / modify metadata 19

20 Data Warehouse Architecture Data Warehouse Components Detailed Data Summary Data Ranges from detailed to summarized data Contains metadata Many views of the data Subject-Oriented Time-variant Metadata 20

21 Requirements Gathering Process Business Measure Definition Standard definition and related business rules and formulas Source data element(s), including quality constraints Data granularity levels (e.g., county detail for state) Data retention (e.g., one month, one quarter, one year, multiple years) Priority of the information (For example, is the information necessary to derive other business measures?) Data load frequency (e.g., monthly, quarterly, etc.) 21

22 Star Join Schema Region_Dimension_Table region _id region _doc Dimension Tables prod_grp_id Product_Dimension_Table prod_id prod_grp_desc Fewer devices Circuit boards Components prod_desc Power supply Motherboard Co-processor NE NW SE SW Northeast Northwest Southeast Southwest account _id account _doc ABC Electronics Midway Electric Victor Components Washburn, Inc. Zerox Account_Dimension_Table month prod_id region_id account_id vend_id net-sales gross_sales SW NE SW ,000 23,000 32,000 50,000 42,000 49,000 Fact Table Monthly_Sales_Summary_Table month mo_in_fiscal_yr month_name January February March Time_Dimension_Table Vendor_Dimension_Table vend_id vendor_desc PowerAge, Inc. Advanced Micro Devices Farad Incorporated 22

23 Multi-Dimensional Analysis Geography Dimension Zip Code County Region State Customer Dimension Class of Trade Client Type Account Store Net Sales by Brand by Region by Client Type Product Family Product Line BrandCategory Product Dimension Group Item Business Measure: Product Net Dimension Sales DW

24 Application Solution Classes Executive information system (EIS) : Present information at the highest level of summarization using corporate business measures. They are designed for extreme ease-ofuse and, in many cases, only a mouse is required. Graphics are usually generously incorporated to provide at-a-glance indications of performance Decision Support Systems (DSS) : They ideally present information in graphical and tabular form, providing the user with the ability to drill down on selected information. Note the increased detail and data manipulation options presented 24

25 Data Mining 25 1

26 Data Mining The process of extracting valid, previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions, (Simoudis,1996). Involves the analysis of data and the use of software techniques for finding hidden and unexpected patterns and relationships in sets of data. 26

27 Data Mining Reveals information that is hidden and unexpected, as little value in finding patterns and relationships that are already intuitive. Patterns and relationships are identified by examining the underlying rules and features in the data. Data mining can provide huge paybacks for companies who have made a significant investment in data warehousing. Relatively new technology, however already used in a number of industries. 27

28 Examples of Applications of Data Mining Retail / Marketing Identifying buying patterns of customers Finding associations among customer demographic characteristics Predicting response to mailing campaigns Market basket analysis Banking Detecting patterns of fraudulent credit card use Identifying loyal customers Predicting customers likely to change their credit card affiliation Determining credit card spending by customer groups 28

29 Examples of Applications of Data Mining Insurance Claims analysis Predicting which customers will buy new policies Medicine Characterizing patient behavior to predict surgery visits Identifying successful medical therapies for different illnesses 29

30 Data Mining Operations and Associated Techniques 30

31 Database Segmentation Aim is to partition a database into an unknown number of segments, or clusters, of similar records. Uses unsupervised learning to discover homogeneous subpopulations in a database to improve the accuracy of the profiles. Less precise than other operations thus less sensitive to redundant and irrelevant features. Sensitivity can be reduced by ignoring a subset of the attributes that describe each instance or by assigning a weighting factor to each variable. Applications of database segmentation include customer profiling, direct marketing, and cross selling. 31

32 Scatterplot 32

33 Visualization 33

34 Data Mining and Data Warehousing Major challenge to exploit data mining is identifying suitable data to mine. Data mining requires single, separate, clean, integrated, and self-consistent source of data. A data warehouse is well equipped for providing data for mining. Data quality and consistency is a pre-requisite for mining to ensure the accuracy of the predictive models. Data warehouses are populated with clean, consistent data. 34

35 Data Mining and Data Warehousing It is advantageous to mine data from multiple sources to discover as many interrelationships as possible. Data warehouses contain data from a number of sources. Selecting the relevant subsets of records and fields for data mining requires the query capabilities of the data warehouse. The results of a data mining study are useful if there is some way to further investigate the uncovered patterns. Data warehouses provide the capability to go back to the data source. 35

36 Advanced Database Topics 36

37 A Little History Prior to the 1980s hierarchical and network databases. Hardware dumb terminals using private networks Database centralized and stored on the disk packs End user terminals simply input/output devices Processing at the mainframe Data text data Networks had to handle text data No access from outside to the organization's private network. 37

38 New Needs Microcomputer enabled workstation processing power. Satellite and network technology provided for very high speed, high traffic, and low cost long distance communications networks. Internet in the late 1990s and the corresponding phenomenal growth in electronic commerce (Ecommerce) necessitated public access to data in people's homes. The volume of data needed to be transmitted increased greatly. 38

39 New Needs Business environment changed during the last two decades Information stored at different locations, on different hardware and operating systems, with different commercial DBMS products, and with different underlying data models had to be combined The centralized database was no longer feasible to handle these new demands 39

40 Distributed Database Scenario There are many advantages to using a distributed database rather than a centralized database. They are: Improved performance, because high traffic data are stored locally. More efficient data management, because the DBA workload is shared. Better network integrity, because the whole system does not stop if one computer goes down. Expansion of the database is facilitated when the organization grows, since new data does not have to be centralized. It can remain and be administered in the original location. Data for the whole organization can still be accessed from any location. 40

41 Distributed Database Data administration is improved (??) In a distributed database system even a simple task like creating a backup copy of the database can take a considerable amount of time. If the database is divided among several locations the time and workload for this task can be shared. 41

42 Replication of Data System failure in one location should not stop processing in other locations Replicate all or parts of the database in more than one location. Database replication improves performance and provides a failsafe option, but it involves considerable complexity Replication of frequently used data improves response time and reduces network traffic If the data changes at one location it must be changed at all locations 42

43 Distributed Systems in an Ideal World C. J. Date established rules for the ideal distributed DBMS system Rules are a goal that distributed systems strive toward, but have not yet reached According to Date's rules: Each site is responsible for its own portion of the distributed database, including security, backup, and recovery. Each site has equal capabilities and does not rely on any other site. The system should work regardless of the computer hardware, operating system, or network installed at any site. 43

44 Date's Rules of Distributed Databases: 1. Local site independence 2. Central site independence 3. Failure independence 4. Location transparency 5. Fragmentation transparency 6. Replication transparency 7. Distributed query processing 8. Distributed transaction processing 9. Hardware independence 10. Operating system independence 11. Network independence 12. Database independence 44

45 Complexities of Distributed Databases There also are many complications involved in the management of distributed database systems. The distributed database must be carefully designed to insure the following: Store data as close as possible to where it is used most often. Make the location of the data transparent to the end user. Make the system easy to expand. Optimize queries to improve response time in the distributed environment. 45

46 Database Design The designer must analyze the organization's needs and business processes to determine the best way to distribute the database. There are several possibilities for storing the data in more than one location: Centralized master database Replication of the entire or part of the database in several locations Horizontal partitions Vertical partitions Mixture of the above 46

Fragmentation Horizontal fragmentation of the database means that rows of a table(s) may be stored in different locations Similar to the separation of the customer table in

47 Fragmentation Horizontal fragmentation of the database means that rows of a table(s) may be stored in different locations Similar to the separation of the customer table in the retailing example above. Vertical fragmentation means that columns of a table ( i.e., attributes or groups of attributes of an entity) are stored in different locations. 47

48 Query Formulation Distributed databases require a considerable amount of network overhead Poorly formulated query it may cause unnecessary data retrieval from the database Query optimization is ideally performed by the distributed database management system 48

49 OODB In traditional relational databases E-R Modeling and normalization focuses on identifying entities, their attributes, and the relationships between entities This works well for most organizational data, especially business data The advent of the microcomputer and processing power on the desktop Computer aided design, CAD, became the norm for engineering work, so it became necessary to store drawings Powerful multimedia PCs with sound cards and color monitors enabled the manipulation of sound and video files Many other applications were developed that required more than just text and numeric processing 49

50 Why?? These new applications were facilitated by the development of Object-Oriented Programming Still evolving development of object-oriented data modeling, object-oriented databases, and object-oriented database management systems OODBMS and O/R DBMS are two types of database management systems that are currently available O/R DBMS uses the basic theory of relational database management systems with object-oriented features added OODBMS is more object-oriented and was developed separately from the relational products OODMBS suffers from a lack of standardization that is available with relational database systems 50

Lecture 18. Business Intelligence and Data Warehousing. 1:M Normalization. M:M Normalization 11/1/2017. Topics Covered

Lecture 18. Business Intelligence and Data Warehousing. 1:M Normalization. M:M Normalization 11/1/2017. Topics Covered Lecture 18 Business Intelligence and Data Warehousing BDIS 6.2 BSAD 141 Dave Novak Topics Covered Test # Review What is Business Intelligence? How can an organization be data rich and information poor?