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CS240A: Databases and Knowledge Bases
Introduction
Carlo Zaniolo
Department of Computer Science
University of California, Los Angeles
Database Systems
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During late 60s
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Relational DBMS were proposed [by E.F. Codd] in the 70s
10+years of R&D led to Relational DBMSs and SQL
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IMS and other hierarchical DBMSs
Codasyl-compliant DBMSs using the network model
Extraordinary success from a research and a commercial view
point (IBM, Oracle, …)
Relational DBMS were covered in CS143
But starting in the mid 80s, DBMSs have faced major
technical and commercial challenges, forcing a major
evolution in these systems---this is the topic of CS240A!
DBMS Vendors
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IBM. SystemR, DB2
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Oracle
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MS SQL Server
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Smaller Players:
 Sybase,
Informix, Teradata/NCR
Changes and Challenges and
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Expert Systems and rule-based computing and knowledge
management:
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New Applications and data types (e.g., spatio- temporal and
multimedia information)
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Object Oriented databases
Datablades and extenders
The WEB and XML
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Deductive Databases and recursive queries
Active databases and rules,
Publishing databases using XML
XQuery: the new query language for XML data.
Decision Support, Knowledge Discovery, Big Data, Machine
Learning, …, Data Science
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OLAP applications
Data Mining
Evolution of SQL Standards
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SQL89 and SQL2 (a.k.a. SQL92): Strictly relational.
SQL3: working documents discussing new specs for
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OR systems, but also for
recursion,
active rules,
OLAPs and OLAP functions.
SQL:1999, and with minor changes SQL:2003.
But evolution continues:
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User-defined indexes,
user-defined aggregates,
XML, etc.
In this course we investigate how SQL and relational systems
are being extended to face the new applications. We will
often study languages other than SQL as a framework for
research.
The main Problem of SQL:
Inadequate Expressive Power
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For instance, SQL cannot support complex queries and
recursion needed in several applications, such as BillofMaterials applications.
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Thus database applications are now developed in
procedural languages with embedded SQL statements
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An impedance mismatch between SQL the host language
(different data types programming paradigm) slows down
application development and their execution.
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Two approaches to solve the problem:
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Making query language more powerful: deductive databases
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Extending programming languages with DB capabilities—this is
approach taken by OO DBMSs and OR DBMSs
Expressive Power: Relational Completeness
All relational languages suffer from the same expressivepower problems:
1. Relational Algebra, 2. Domain Relational Calculus, 3. Tuple
Relational Calculus, and 4. Nonrecursive safe Datalog rules.
These languages are equivalent in terms of the expressive
power, and programs (I.e. queries) written in one language
are easily mapped into programs written in another.
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The notion of Relational Completeness (RC) defines the class of
queries expressible using relational algebra or, equivalently, using
safe relational calculus queries.
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RC was proposed in the 70s as a minimum required for all
database query languages (not met by most of query languages at
that time)
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But nowadays RC is not enough!
Datalog
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SQL’s Close Relations
1. QBE (Query by Example): twodimensional rendering of domain
calculus
2. QUEL and SQL: inline, keywordbased versions of tuple relational
calculus---with extensions such as updates and aggregates.
3. Datalog: ruleoriented, logicbased refinement of domain calculus.
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Datalog is the best candidate for more powerful query
languages because
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Its formal framework based on first order logic,
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It supports the rulebased programming paradigm, that is the key of
expert systems and knowledgebased systems
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Similar to Prolog which is more procedural.
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Big Data have brought a renewed interest in Datalog.
The Bigger Picture
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Assemblers, Operating Systems (Early 60s …)
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Languages and Compilers (Late 60s …)
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Information Management Systems and Data Base
Management Systems (DBMS) (70s …
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GUIs (80s …)
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Networks (60s) and
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the WEB (90s) and beyond
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Year 2000 and beyond big data analytics
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2010 and so… Datalog’s renaissance.
Workplan and Grade Basis
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---Grade Basis for CS240A
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Midterm : 40%
Homework and Assignements: 10%
Final Projects and Reports 50% (XML 15%, DM
35%)
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Take home final Consists of two projects:
 The
first project will be about supporting temporal
queries in XML and JSON.
 The
second project will ask you to write decision
support queries in SQL and DeALS.