Summary Principles of Database management

Summary Principles of Database
Management

Chapter 1: fundamental concepts of database management

Key definitions
 Database = collection of related data items withing specific business process or
setting
 Database Management System (DBMS) = software package used to define, create,
use and maintain data
 Database system = combination of database and DBMS


File versus Database approach to data management
 File approach
o Duplicate/redundant data
will be stored for each
application separately
o Danger of inconsistent data
o Hard to integrate applications
/ make changes
 Database approach
o Better than file approach
o More efficient, consistent,
less storage needed
o Facilities provided for data
querying




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,Elements of a database system
 Database model versus instances
o Database model: provides description of database at different levels of detail
 Specified during database design, don’t normally change
 Stored in catalog
 Example:
 Student (number, name, email, …)
 Course (number, name, location, …)
o Database state: represents data in the database at a particular moment
 Also called current set of instances
 Changes every time data gets updated / removed




 Data model
o Database model: comprised of different data models
 Each describing the data from different perspectives
o Conceptual data model: high-level description of data items
 Used for communication between architect and user
 Usually represented using Enhanced-Entity Relationship (EER) model
o Logical data model: translation of conceptual model towards a specific
implementation environment
 Can be hierarchical, CODASYL, relational, OO, extended relation, XML
or NoSQL
 Can be mapped to internal data model that represents data’s physical
storage details
 Describes what’s stored where, what format, …


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,  Can only describe structured data
o External data model: contains subsets of data in logical model (=views),
specialized for specific applications for certain groups of users


 The three layer architecture




 Catalog
o Heart of the DBMS
o Contains data definitions/metadata of the database application
o Stores definitions of the views, logical and internal data models
o Synchronizes all models to guarantee consistency
 Database users
o Information architect: designs conceptual data model
 Thorough interaction with business user
o Database designer: translates conceptual model to logical/internal model
o Database administrator (DBA): implementation and monitoring of database
o Application developer: develops the actual applications in ex. Python
o Business user: runs these applications to perform specific operations
 Database languages
o Data definition language (DDL): used by DBA to express internal, external and
logical data models
 Definitions stored in the catalog
o Data manipulation language (DML): retrieve, insert, delete and modify data


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,  DML statements: can be embedded in programming language
o Structured query language (SQL): offers both DDL and DML statements for
relational database systems


Advantages of database systems and management
 Data independence
o = changes in data definitions: little to no impact on applications
o Physical data independence: when changes are made to internal data model
(data storage):
 Neither application, views, logical data model must be changed
 DBMS: should provide interface between logical/internal data models
o Logical data independence = software applications are minimally affected by
changes in conceptual/logical data model
 Views in external model: act as protective shield
 DBMS: must provide interface between conceptual/logical/extended
layer
 Database modelling
o Data model = explicit representation of data items, together with their
characteristics/relationships
o Conceptual data model: should provide mapping of the data requirements of
the business user
 Translated into logical/internal data model
o Data model’s assumptions/shortcomings must be clearly documented
 Managing structured, semi-structured and unstructured data
o Structured data (ex. Spreadsheet)
 Can be described with logical data model
 Ability to force integrity rules, correctness of data
 Facilitates searching, processing and analysing the data
o Unstructured data (ex. Text, picture)
 No components in a file that can be interpreted by a DBMS/application
 Volume of unstructured data > volume of structured data
o Semi-structured data (ex. Emails, HTML)
 Has certain structure, but irregular and highly volatile

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