Data Structure & File Systems Hun Myoung Park, Ph.D., Public Management and Policy Analysis Program Graduate School of International Relations International University of Japan

Outline  Traditional Data Approach  Database Approach  Database Schema  External Schema  Conceptual Schema  Internal Schema  Relational Database  Relations and Primary Key  Database Management Systems 2

Traditional Data Approach 1  One program/application/system and its own data sets  Program-data dependency; data are tightly tied to a program.  Program A  Data file a; Program B may not access data fie a that is specific to A.  Department C  Master data file c; Department D may not use master data file c 3

Traditional Data Approach 2  Data redundancy (duplication of data)  Lack of data integrity (accurate, consistent, and up to date)  Limited data sharing  Lack of standardization in data structure and access  Time consuming and costly development and maintenance 4

Database Approach  A data set shared by many programs and systems  Program-data independence  Minimize data redundancy  Data integrity (consistency)  Data sharing and standardization  More complex and expensive  More risk in database management 5

Database Applications  Distributed databases  Objective-oriented databases  Data warehousing  Online transaction processing (OLTP)  Online analytical processing (OLAP)  Data mining, business intelligence (BI) 6

Database Management  Failure in database results in big loss  Data center and data grid (Indiana Univ.)  Database administrator (DBA)  Database management systems (DBMS)  Protect from natural disaster, misuse, or illegal access (inside and outside) 7

Types of Databases  Hierarchical database: tree structure, Root- parent-child levels records, one-to-many relationship  Network database: each child record can have more than on parent record.  Relational database: key field (identifier) to link tables (relations)  Object-oriented databases: objects (data and methods) 8

Database Schema

 Views of database structure (e.g., tables, views, procedures, functions)  Described by a formal language in DBMS (data dictionary). 10

Types of Database Schema  Degrees of data abstraction  External schema: Users’ view  Conceptual schema: HW/SW independent  Internal schema: HW independent, SW dependent)  Physical schema: HW/SW dependent 11

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External Schema  External schema describes (end) users’ view of databases  Represents a subset of the database  Multiple external schemas can exist depending on applications  Ensure data security by limiting data access to the subset of a database 13

Conceptual Schema  Conceptual schema describes a global views of the entire database  Integrate all external views  Basic blueprint of a database  Only one conceptual schema  ER-diagram represents this schema  Software/hardware independence 14

Internal Schema  Internal schema is a representation of conceptual schema from a specific DBMS’ view (not users’ view).  Hardware independent but software dependent.  Logical independent if a internal schema is changeable without touching its corresponding conceptual schema 15

Physical Schema  Physical schema describes how data are actually stored on storage unit.  Dependent on DBMS, methods of accessing files, types of hardware storage units, etc.  Physical independent if a physical schema (model) is changeable without touching its corresponding internal schema (model) 16

Relational Database

Relational Database 1  A collection of tables.  A table (relation) consists of records each of which consists of a list of related fields  Each table is related to other tables using keys (primary and secondary keys 18

Relational Database 2 19

Relational Database 3  Table, relation, file, or object (entity set)  Record, tuple, entity (instance or occurrence), or row  Field, attribute, or columns. 20

Relational Database 4  Bit  Byte   Field (dot)   Record (line or 1 st dimension)   Table (plane or 2 nd dimension)   Database (cubic or 3 rd dimension)   Data Warehousing 21

Attribute Types 1  Character (string)  Variable character  Integer (tiny, small): 1-2 bytes  Long integer  Float (decimal) 4 bytes  Double precession: 8 bytes  Numbers is preferred in terms of efficiency 22

Attribute Types 2  Date/time  Logical or boolean (binary)  Text (memo)  Image/video/audio  Object, etc 23

Variable Properties  Length of an attribute is determined by types of data, and maximum length of data & systems.  Allowable values and ranges (picture clause) are considered to improve data quality and integrity 24

Relations and Primary Key

Relations  Joining combines two or more tables (relations) 26

Primary Key  Primary key consists of one of more attributes whose values uniquely identify a record in a table.  To identify a unique record  No duplicate is allowed.  Secondary key 27

Linking Tables Using Keys 28

Joining Types 1  Joining combines two or more tables (relations)  Inner (intersection of left and right tables)  Outer join (union of left and right)  Left (outer) join (first table as a reference)  Right (outer) join (second as reference) 29

Joining Types 2  Left outer join: Include all entities in the left table and entities in the right table matched to any entity in the left table.  When the left table has A and B and the right contains A and C, the left outer join produce A, matched A (data items from the right table), and B excluding C  Right outer join is the opposite to left outer join (reference table is switched) 30

Database Management Systems

DB Management System  Database management system (DBMS)  Controls the structure of a database and access to the data.  Relational DBMS (RDBMS)  Object-oriented DBMS (OODBMS) 33

DBMS Components 1  Data dictionary descries the structure of data in databases such as name, type, length, access control.  Utilities to create, modify, delete tables, records, and fields.  Report generator  Access security, and system recovery 34

DBMS Components 2  Query languages manipulate databases  Structured query language (SQL) 35

DBMS Server  Oracle  DB2 (IBM)  SQL Server (Microsoft)  ASE (Adaptive Server Enterprise) and IQ (Sybase  SAP)  Informix (Informix  IBM)  Teradata (Teradata Co.)  Open source: MySql, PostgreSQL 36

DBMS Client  dBase III (+), IV (Ashton Tate  Borland)  Clipper (programming language and compiler for dBase III) (Nantucket Co.)  FoxBase (+), FoxPro, Visual FoxPro (Fox Software  Microsoft’s Visual Studio)  Paradox (Corel ’ s WordPerfect Office)  Access (Microsoft’s Office Suite) with z- engine 38

DBMS Service  Database as a Service (DaaS) 39

Considerations  Data dictionary and E-R modeling  Attributes for the public sector  Performance measures  Data security: minimize use of private information when designing conceptual schemas, each department and application has its own schemas  Database administrator’s role in ensuring security in response to evolving threats. 40