Dr. Alexandra I. Cristea

http://www.dcs.warwick.ac.uk/~acristea/

CS 319: Theory of Databases: C1

Lecturers

• Alexandra I. Cristea

• Hugh Darwen: hughdarwen@gmail.com

• Other invited talks?: TBA

Schedule• Usual:

– Mo 16-17– Wed 9-10– Thu 16-17

• Exceptions:– week 2: 11.10.06: no course– week 3: 16.10.06: 2 hours– week 3: 18.10.06: no course– Others: TBA: check forum, website, course

Slides• Thanks to:

– Dr. Paul Goldberg:• http://www.dcs.warwick.ac.uk/people/academic/Paul.Goldberg/cs319/

cs319index.html

– Dr. Meurig Beynon:• http://www.dcs.warwick.ac.uk/people/staff/Meurig.Beynon/

– Dr. Ad Aerts:• http://wwwis.win.tue.nl/~aaerts/

– Prof. Dr. Paul De Bra:• http://wwwis.win.tue.nl/~debra/

– Others: mentioned directly

Contact

• Forum:

http://forums.warwick.ac.uk/wf/browse/category.jsp?cat=24 • IF (and ONLY IF) a question is personal, you

might address it to acristea@dcs.warwick.ac.uk

– FORMAT: subject of email should contain ‘CS319’ and topic of the email (otherwise it will be filtered out)

Course site(s):• Current:

– http://www.dcs.warwick.ac.uk/~acristea/courses/CS319/ – Will contain current slides, as taught at the course– Will contain notifications: check BEFORE & AFTER the

course

• Official:– http://www.dcs.warwick.ac.uk/undergraduate/modules/cs319.html

• Past:– http://www.dcs.warwick.ac.uk/people/academic/Paul.Goldberg/cs319/cs

319index.html

(provisionary) Content: Topics1. Generalities DB2. Integrity constraints (FD revisited)3. Temporal Data4. Relational Algebra (revisited)5. Query optimisation6. Tuple calculus7. Domain calculus8. Query equivalence9. LLJ, DP and applications10. The Askew Wall11. Datalog

Books

• Korth and Silberschatz, Database System Concepts, McGraw-Hill,1991.

• Ullman J D, Principles of Database Systems (Vols 1 and 2), Computer Science Press,1988.

Purpose of this course

• More in-depth information on the theory of databases

• How (some of) the existing db languages fit in the theory (and how they don’t)

Overlaps and sequencing

• Form: optional

• Prerequisites – CS252: Fundamentals of Database Systems– Optional: CS253: Topics in Database Systems – (actual previous CS233: Database Systems)

Organization of the course• 15 CATS• CS, CSE, CBS, Mathematics• ~ 30 1-hour lectures• Exam at the end: 3 hours• Rules of the game:

– Read also comments on the slides.– Presence is optional, but beware: slides-only are

NOT ENOUGH to learn from for the exam; you need to participate, take your own notes, so self-study!

Scope of CS 319

• Expressive power of db query languages• Algorithms for the computational problems • Limitations to classical relational theory

• A central contribution of theory is to say what cannot be done, not just what can.

• Consider how this observation is relevant to the above topics.

A theme of CS319• How do theory and computing practice relate to

specific reference to databases?• Key ideas:• Theory: based on Codd's relational theory.

– There is an excellent correspondence between relational theory and practical database application of a certain kind.

• Relational databases can be seen as a precursor of 2 principal kinds of computer application: – environments for end-user programming and – computer-based models of real-world state.

1. Database Generalities

DB generalities : What is a database?

• Chris Date:– Database = computer-based record keeping system

• R.W. Engles: "A Tutorial on DB Organisation" (1974)– Db = collection of stored operational data used by

the applications system of a particular enterprise– enterprise: hospital, university, bank, company etc– operational data:

• data on products, accounts, patients etc• typically persistent cf conventional program

IO data

DB generalities : Why use a db?• Case-study: Banking (Korth &Silberschatz Chap. 1)• ? How to meet needs using a traditional file-

processing system supported by a conventional OS• Files: permanent records of customers, accounts• Applications programs (APs): enable user to modify

files– to credit or debit an account– to add a new account– to find the balance in an account– to generate monthly statements

• APs written by systems programmers as required• new requirements new files + new programs

Original context for data modelling 1• 1970s style applications• unsophisticated computer users• batch mode interaction• modest response times• no visualisation or GUI• modest expectations for ease-of-use• programming perceived as technical• simple infrastructure and environment• no PC, web etc• no live feeds of data• textual interaction the norm

Original context for data modelling 2• 1970s style applications

– Business context• simple business model, limited automation, access

etc• low volume of data• not initially distributed

– Computing context• existing/emerging DB proposals unconvincing• computers not very powerful• human and computing resources very expensive

Summary of issues for data management

1. Data redundancy and inconsistency

2. Difficulty in accessing data

3. Data isolation

4. Concurrent access anomalies

5. Security problems

6. Integrity problems

DB generalities : +’s and –’s of DB use

• Conventional file systems have certain characteristics

• we will review the key issues for data management:+ indicates a positive impact of using a database

– indicates a potentially negative impact of using a DB

Data redundancy & inconsistency programmer uses ≠ format file, developed at

≠ stage in history of enterprise

=> data duplicated:

+ in a DB rationalise and standardise data

[rationalise: shared source for data]

… rationalise doesn't necessarily mean centralise

– compromises are needed

• where users suit themselves => efficient results perfect data organisation to suit all users

– duplication: insurance against info loss

Data redundancy & inconsistency

Difficulty in accessing data

• unforeseen requests, new functionality

• new programs, possibly new data structures

+ in a DB, simplify access & manipulation by intelligent organisation of data cf. modelling approach to requirements

e.g. in use of UML in OOSE

Data isolation

• data to retrieve from many sources in APs

• + in DB, hide source physical data : higher level of abstraction

– automation: less human interaction with data– risk of corrupted data

Concurrent access anomalies

• would like multiple access (efficiency & faster response)e.g. simultaneous withdrawal

+ concurrency can't be managed without a form of overall control

Security problems

• to restrict access to (un)authorised users for confidential info

+ security needs a form of overall control

– issues: where should the control be? inside or outside computer system

* e.g., non-trivial problem to determine what can be inferred from responses to queries that aren't explicit

Integrity problems

• integrity constraints– may arise dynamically: – difficult to modify programs to cope; – hard to guarantee if data stored in ≠ files

+ automated management demands a form of overall control

– automation reduces scope for human intervention / interpretation

Conclusions: Issues for data management

• For many commercial applications, a good solution is offered by a database management system (DBMS).

• A DBMS is an unconventional OS operating over a structured file system.

Motivating the DBMS concept

• devise an abstract model of the entire corpus of operational data that simplifies the data processing activity, so that– simple queries can be handled without writing

new application programs– if APs => accessing & manipulating operational

data consistently and efficiently is greatly simplified

The ingredients of a database

• Data• integrated• shared• (possibly distributed)• Hardware• storage• Software• database management system: DBMS• protects users from hardware level detail• serves the needs of all users

DB Users• end-user:

– non-specialist accessing data via a query language

– naïve user accessing data via a special-purpose interface

performs data retrieval and update (extend / modify)

• applications programmer:– writes programs that use the DB by embedding queries to

the DB in a HLL

– develops interfaces for the naïve user

DB Users • Database Administrator (DBA):• responsible for overall control• decides what data is to be stored• designs the conceptual scheme

used to represent the operational data• implements authorisation checks• decides strategy for backup and recovery• monitors performance• oversees modification to suit user requirements

Data abstraction in a db

• addresses issues of design, use, management and implementation

• Data model describes (formally) 3 different levels of abstraction:

• physical level

• conceptual level

• view level

3 Levels• physical level:• how is the data actually represented in the hardware?• bits, bytes• conceptual level:• what meaningful relationships are expressed by the

physical data?• entities, and relationships between entities• view level:• what particular relationships are required by users?• more abstract because partial typically very

high-level knowledge constitutes the view

Illustrating data abstraction:

• DB w. date of birth of a client (bit string).

• senior citizens?: clients aged > 65

• Representations at ≠ abstraction levels:

– Conceptual: date of birth

– Physical: bit string

– View: age (not stored in DB!!)

Data abstraction in a database

views conceptual model

physical layout

external schemas

internal schemas

sub-schemes

conceptual scheme

physical scheme

USE

DESIGN&

MANAGEMENT IMPLEMENTATION

Role of abstraction (1)

• Internal & external translation schemas:

* protect conceptual model from change

• when physical organisation changes / new views are required

* protect user from a need to change views

* protect altering physical organisation

• if conceptual model is modified

Role of data abstraction (2)

• physical data independence:

protecting conceptual model from change when

physical organisation changes

• logical data independence:

protecting user from need to change views when

conceptual model changes

Characteristics of electronic data 1970

• “Abstract model of the entire corpus of operational data”• Demands of the abstract model in 1970 quite low …

– small volumes of data, modest performance– limited levels of volatility and automation tolerated

• Today different, BUT – (subject to viewing human agency as a metaphor for any

agency, )– key issues of a classical database are still vital

• Any DB modelling paradigm must handle 70s problems

db models

• 2 principal kinds of abstract data model:– object-based models– record-based models

• earliest• reflects file system culture they displaced

Object-based models

• main models:– entity-relationship models– object-oriented data models

• Others: semantic & functional data models.

• E-R model widely used to model data abstractly

• OO model gaining acceptance in practice: effectively represents data + operations on it.

Record-based Logical Models

• Used at the conceptual and view levels. Specify both– overall logical structure of the database– higher-level description of the implementation.

• Record-based: uses records in fixed-format of several types. simplifies implementation <> trend towards richness

and variety in structures used to implement OODBs

Varieties of record-based logical model

• hierarchical model

• records & links organised as a family of trees

• network model

• records & links organised as a family of graphs

• relational model

• uses tables to record relationships between data

Physical Data Models

• not our primary concern in this module.• Relevant issues:

– are data tables stored using hashing?– how are data tables indexed?– how are entries in data tables encoded and ordered?– what algorithms are used to retrieve and update?

Classical database features• Instances and Schemes• State of DB changes over time: structure vs. current state.

• overall design of DB = database schema• current content of DB = instance of the DB

• Useful analogy with procedural variables:• database schema type definition for variable• instance of database value of the variable

Data abstraction and schemas

• physical scheme at lowest level

• conceptual scheme at intermediate level

• several sub-schemes (possibly user-defined) at highest level (views of the DB)

Data Definition Language (DDL)

• for database schema • compiling DDL: Data Dictionary• the storage & access methods: specified in storage &

definition languageImplementation details usually hidden from users

Data Manipulation Language (DML)

• data manipulation: accessing DB to retrieve, insert, delete, or modify data

• data retrieval– most common – "querying the DB"

• retrieval component of DML = query language (abusively: ‘query language’ ~ synonym DML)

Varieties of Data Manipulation Language

• There is a tension between• efficiency at physical level• intelligibility / ease of use at higher level• Have both procedural and non-procedural DMLs• procedural: requires knowledge of data

implementation• non-procedural: need only specify what data is

needed

Data Manipulation Languages (DML) for typical data models

• Procedural: – object-based, hierarchical, network models – user explicit responsibility: optimising queries,

• needs knowledge of data organisation

• Non-procedural: – relational models– formulate queries without above,

• implementation has to be optimised

Optimisation• Database Manager (program module!)

– interfaces between low-level data in DB and application programs & user queries.

• Large volumes of data (available technology)– gigabytes thousand megabytes = 1 billion bytes [!]– terabytes million megabytes = 1 trillion bytes

• Requires auxiliary storage media, such as disk, CD etc.

• Optimisation is primarily concerned with eliminating data transfers between main and auxiliary memory.

Functions of the DB manager program module

• query processing– interacting with the file manager modules doing actual

operations on physical data• integrity enforcement

– checking that data in the DB conforms to specified constraints

• security enforcement– ensuring that authorisation is given for access to data

• backup and recovery– coping with failure, and recovery to consistent DB state

• concurrency control– ensuring that simultaneous transactions do not interfere.

Role of Database Administrator

• scheme definition using DDL

• specification of storage structure & access method

• modification of schemes and physical storage as

required

• granting authorisation for data access

• specifying integrity constraints

Overall DB system structure• Processing components

– file manager– database manager– query processor– DML precompiler (to process DML embedded in APs)– DDL compiler

• Data structures– data files: actual content of db– data dictionary meta-data– Indices: auxiliary files to assist fast access

Summary

• We have revisited the generalities of database systems– Background of appearance– Main problems they try to solve– Components & languages

… to follow

How to reason with Integrity Enforcements (Constraints):Functional Dependencies (FDs) applied