INTRODUCTION TO INFORMATION TECHNOLOGY IS01

1. INTRODUCTION TO INFORMATION TECHNOLOGYIS01 Dr.Anita Seth

2. Managing Organizational Data • Today’s business enterprises cannot survive without quality data about their internal operations and external environment. • Data can be anything…numbers, image or raw fact. • Information-when the data is processed and converted into meaningful and useful form. Dr.Anita Seth

3. Foundation Data Concepts • Bit- Smallest unit of data; binary digit (0,1) • Byte- Group of bits that represents a single character. • Character – single alphabetic, numeric or other symbol • Field – group of related characters. E.g student’s name etc. Dr.Anita Seth

4. Foundation Data Concepts • Record – logical grouping of related fields. • File – group of related records • Entity- Person, place, thing, event about which information is maintained • Attribute- Description of a particular entity Dr.Anita Seth

5. Foundation Data Concepts Dr.Anita Seth

6. Foundation Data Concepts • Key field- Identifier field used to retrieve, update, sort a record • Primary Key- that uniquely identifies a record so that the record can be retrieved, updated. • Foreign Key- primary key of one file and appears in another file. Dr.Anita Seth

7. Data Access Methods • Sequential Access– data records retrieved in the same physical sequence in which they are stored. e.g. magnetic tape • Direct Access- records can be retrieved in any sequence. e.g. floppy disk • Indexed sequential Access-uses the key field to locate physical address of a record. • - employs transform algorithm to translate the key field into record’s storage location on disk Dr.Anita Seth

8. Types of Data Processing • Batch processing • Changes to data file accumulated and stored, processing is done periodically. e.g. generation of student’s mark sheet. • Online processing • Transactions are entered directly into computer and processed immediately. • - In real time applications, data is captured and processed. e.g. airline reservation system Dr.Anita Seth

9. Traditional File Processing • Data are organized, stored in independent files each organized in a different way. • Each file was organized to be used by different application program. • Difficult to get the required information. Dr.Anita Seth

10. Problems of File Processing • Data Redundancy – independent data files included lot of duplicated data; duplicated data had to be updated. • Data inconsistency-various copies of data may not agree. • Lack of Data Integrity – data values may not be accurate across multiple data files. • Lack ofData security – new applications may be added to the system on ad-hoc basis and more people access the data. Dr.Anita Seth

11. Database: Modern approach • Logically organized collection of similar or related data. • Serves a base from which the desired information can be retrieved and further processing or reorganizing can be done. • Eliminates problems associated with traditional file approach. Dr.Anita Seth

12. Types of Databases Dr.Anita Seth

13. Types of Databases • Operational – contain the data to support the business processes and operations of a company. e.g. customer database. • Centralized database • - All the related files in one physical location. • - When centralized database computer fails, all users affected. Dr.Anita Seth

14. Types of Databases • Distributed – complete copies of database in more than one physical location. • - Two types: replicated and partitioned. • - Replicated database has complete copy of entire database in many locations; creates too much overhead. • - In Partitioned database, data is subdivided; data can be entered quickly; • widespread access to sensitive company data increases security problems. Dr.Anita Seth

15. Databases Management System • A collection of programs that enable to store, modify, and extract information form a database. • Few examples • - computerized library • - flight reservation system • - computerized inventory system Dr.Anita Seth

16. Data Abstraction • Process of distilling the data • Physical view specifies how the data actually stored. • Logical view describes what relationship exists between the various data. Dr.Anita Seth

17. Database Structures • Hierarchical – relationships between records form a hierarchy or treelike structure; Structure characterized by one to many relationship. • Network – data can be accessed by one of several paths because any data element or record can be related to any number of other data elements • - Depicts data logically as many-to-many relationships Dr.Anita Seth

18. Hierarchical and Network DBMS • Disadvantages • Time consuming; difficult to install. • Less flexible compared to RDBMS • Lack support for ad-hoc and English language-like queries Dr.Anita Seth

19. Relational Database Structure • All data elements within the database are viewed as being stored in the form of 2D tables called as relations • Relates data across tables based on common data element • Examples: DB2, Oracle, MS SQL Server Dr.Anita Seth

20. Object-Oriented Database Structure • Multi-dimensional database structure. • Can accommodate more complex data types including graphics, pictures, voice and text • Inheritance – automatically creating new objects by replicating some or all of the characteristics of one or more existing objects Dr.Anita Seth

21. Evaluation of Database Structures • Hierarchical data structure is best for structured, routine types of transaction processing. • Network data structure is best when many-to-many relationships are needed. • Relational data structure is best when ad hoc reporting is required. Dr.Anita Seth

22. Database Management Approach • Consolidates data records into one database that can be accessed by many different application programs. • Software interface between users and databases • Data definition is stored once, separately from application programs Dr.Anita Seth

23. Database Interrogation • Capability of a DBMS to report information from the database in response to end users’ requests • Query Language – allows easy, immediate access to ad hoc data requests • Report Generator - allows quick, easy specification of a report format for information users have requested Dr.Anita Seth

24. Database Language • To create or manipulate a database • Data definition language (DDL) • - defines types of information in the database and how they will be structured. • - provides the link between logical and physical view of database. • - defines physical characteristics of each record, fields within a record, field’s logical name, data type and character length. Dr.Anita Seth

25. Database Language • Data manipulation language (DML) • - used to query, retrieve, store, update, delete or display the contents of the database • - Query languages like SQL (Structured Query Language), an important component of DBMS. • - SQL combines both DML and DDL features. • - can perform complicated searches with simple statements Dr.Anita Seth

26. Structured Query Language • Uses keywords like • SELECT (specify the desired attribute ) • FROM ( specify the table to be used) • WHERE (specify conditions to apply) • Example: To find from university database, all those students graduating with honors and belonging to general category. • SQL statement would be • SELECT (student name), FROM (student database), WHERE (category=G and Grade point average >=5) Dr.Anita Seth

27. Data Dictionary • In relational database, information organized and accessed according to logical structure . • When relational database created, data dictionary prepared. • Data dictionary contains logical properties of field values. • e.g. Field name • Type- alphabetic, numeric etc. • Default value etc. Dr.Anita Seth

28. On-line Transaction Processing (OLTP) • Implies gathering information, processing and updating. • DBMS and databases support OLTP. Dr.Anita Seth

29. On-line analytical Processing (OLAP) • Multidimensional data analysis • Supports manipulation and analysis of large volumes of data from multiple dimensions/perspectives Dr.Anita Seth

30. On-line analytical processing (OLAP) Dr.Anita Seth

31. Data Warehouse • Large database that stores data that have been extracted from the various operational, external, and other databases of an organization • Supports reporting and query tools • Stores current and historical data • Consolidates data for management analysis and decision making Dr.Anita Seth

32. Meta Data • Data about data • What data is available, what their sources are; where they are; how to access them? • Technical metadata- where the data come from; how the data was changed?; how the data is organized? how the data is stored? who owns the data etc. • Business metadata- what data is available?; how to access the data?; how current the data is?; what the data mean? Dr.Anita Seth

33. Data Warehouse System Dr.Anita Seth

34. Data Mart • Scaled down version of data warehouse and hold subsets of data from a data warehouse. • Focus on specific aspects of a company, such as a department or a business process. Dr.Anita Seth

35. Data Warehouse & Data Marts Dr.Anita Seth

36. Data Mining • Analyzing the data in a data warehouse to reveal hidden patterns and trends. • Data mining tools include sophisticated, automated algorithms to identify hidden patterns, correlations and relationships. Dr.Anita Seth

37. Data Mining • Predict trends and behavior to make proactive decisions. • E.g. forecasting bankruptcy; detecting fraudulent credit card transactions; discovering pattern in the retail sales data for the products that are often purchased together Dr.Anita Seth

38. Data Mining Uses • Perform “market-basket analysis” to identify new product bundles. • Find root causes to quality or manufacturing problems. • Prevent customer attrition and acquire new customers. • Profile customers with more accuracy Dr.Anita Seth

39. Database Schema • Graphical presentation of whole database. • Database system may have different schemas: • - Physical schema • describes database design at the physical level. • - Logical schema • describes database design at the logical level Dr.Anita Seth

40. Case #1: Data base Business Value • Successful sellers of books, music other entertainment on internet owe their success to Muze company. • Muze aggregates and classifies millions of products from thousands of publishers. • Muze stores this massive amount of information in relational database and license its database at a fraction of what it would cost sellers to compile their own information. Dr.Anita Seth

41. Case #1: Data Base Business Value • Information provided by Muze enables retail customers to get in-depth information regarding books, CDs, videotapes without having the product in hand. • Muze also provides classification data that helps retailer’s search engine to opertae more efficiently. Dr.Anita Seth

42. Important Considerations • Data warehouse and data mining tools are expensive. • Organization need to devote considerable time to create a Data warehouse. • Training to use data minning tools is also expensive. • Some organizations may not need data warehouse; necessary information to support decision making from operational databases. Dr.Anita Seth

43. Summary • Managing organizational data requires IT and software tools. • The database management approach consolidates data needed by different applications. • DBMS are software packages that simplify the creation, use, and maintenance of databases. • Several types of databases are used by business organizations including operational, distributed, and external databases. Dr.Anita Seth

44. Summary • Data warehouses are a central source of data from other databases that have been transformed and cataloged for business analysis and decision support applications. Dr.Anita Seth