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

2. Database Systems  During late 60s  IMS and other hierarchical DBMSs  Codasyl-compliant DBMSs using the network model  Relational DBMS were proposed [by E.F. Codd] in the 70s  10+years of R&D led to Relational DBMSs and SQL  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!

3. DBMS Vendors  IBM. SystemR, DB2  Oracle  MS SQL Server  Smaller Players:  Sybase, Informix, Teradata/NCR

4. 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  Data Mining  The WEB and XML  Publishing databases using XML  XQuery: the new query language for XML data.

5. 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, and with minor changes SQL:2003.  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.

6. 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 OO DBMSs and OR DBMSs

7. Expressive Power: 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 database query languages (not met by most of query languages at that time)  But nowadays RC is not enough!

8. 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  Similar to Prolog which is more procedural.  Big Data have brought a renewed interest in Datalog.

9. The Bigger Picture  Assemblers, Operating Systems (Early 60s …)  Languages and Compilers (Late 60s …)  Information Management Systems and Data Base Management Systems (DBMS) (70s …  GUIs (80s …)  Networks (60s) and  the WEB (90s) and beyond  Year 2000 and beyond big data analytics  2010 and so… Datalog’s renaissance.