CS240A: Databases and Knowledge Bases Introduction Carlo Zaniolo Department of Computer Science University of California, Los Angeles WINTER 2002

Database Systems  Relational database systems were proposed [by E.F. Cod] in the 70s  In the 80s relational DBMSs and SQL start replacing the previous generation database systems, such as  IMS and other hierarchical DBMSs  Codasyl-compliant DBMSs using the network model  Extraordinary success—due to many factors, including:  a shift from the procedural and algorithmic aspects of computing to the representation of informational aspects---a shift that has been fully realized by the web.  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!

Challenges and Changes  Expert Systems and rule-based computing and knowledge management:  Deductive Databases and recursive queries  Active databases and rules,  New Applications and data types (e.g., spatio- temporal and multimedia information)  Object Oriented databases  Datablades and extenders  Decision Support and Knowledge Discovery  OLAP applications  Datamining  The WEB and XML  Publishing databases using XML  XQuery: the new query language for XML data.

Evolution of SQL Standards  SQL­89 and SQL2 (a.k.a. SQL­92): Strictly relational.  SQL­3: working documents discussing new specs for  O­R systems, but also for  recursion,  active rules,  OLAP.  SQL:1999, actual specs released. But evolution continues:  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  For instance, SQL cannot support complex queries and recursion needed in several applications, such as Bill­of­ Materials applications.  Thus database applications are now developed in procedural languages with embedded SQL statements  An impedance mismatch between SQL the host language (different data types programming paradigm) slows down application development and their execution.  Two approaches to solve the problem:  Making query language more powerful: deductive databases  Extending programming languages with DB capabilities—this is approach taken by O-O DBMSs

Relational Completeness All relational languages suffer from the same expressive- power 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.  The notion of Relational Completeness (RC) defines the class of queries expressible using relational algebra or, equivalently, using safe relational calculus queries.  RC was proposed in the 70s as a minimum required for all databasequery languages (not met by most of query languages at that time)  RC is no longer sufficient.

Datalog  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.  Datalog is the best candidate for more powerful query languages because  Its formal framework based on first order logic,  It supports the rule­based programming paradigm, that is the key of expert systems and knowledge­based systems  Similarity with Prolog.

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