Information Systems: Modelling Complexity with Categories Four lectures given by Nick Rossiter at Universidad de Las Palmas de Gran Canaria, 15th-19th May 2000, under the Socrates-Erasmus Programme

Lectures 1. Interoperability in Information Systems 2. Introduction to Category Theory 3. Object Concepts as Categories 4. Handling Heterogeneity with Information Resource Dictionary System

Lecture 1: Interoperability in Information Systems Nick Rossiter, Computing Science, Newcastle University, England

Motivations Diversity of modelling techniques Distributed businesses may exercise local autonomy in platforms Data warehousing requires heterogeneous systems to be connected Data mining enables new rules to be derived from heterogeneous collections

Basic Definitions 1 Distribution: information bases are stored on multiple computer systems interconnected by a communication medium. Homogeneous system: one that adheres to the same software at all sites. Heterogeneous system: one that does not adhere to the same software at all sites.

Basic Definitions 2 Autonomy: the ability of a site to control its own activities with respect to one or more of: –design –communication –execution –association

Interoperability 1 Interoperability: the ability to request and receive services between various systems and use their functionality. More than data exchange. Implies a close integration.

Interoperability 2 Features: exchange of messages and requests use of each other’s functionality client-server abilities distribution operate multiple systems as single unit communication despite incompatibilities extensibility and evolution

Architectures for Interoperability 1 1. Global schema integration Produces single new schema (C) for the different information systems with schemas (A, B). A C B

Global Schema Integration Advantages –Transparent to end users -- appears as single information system Disadvantages –Difficult -- needs human understanding to perform integration –Local autonomy lost –Static - does not evolve automatically

Architectures for Interoperability 2 2. Federated Database Systems Less tightly coupled schema (than in 1) Each service through an export schema specifies sharable objects Common data model Internal command language Decentralised control (local autonomy) Five-level architecture for federated system

Federated Databases: Loosely- coupled Created by users A E,B E are export V is view schema A,B are base schemas A B V AEAE BEBE

Federated Databases: Tightly- Coupled Created by administrators Global schema integration on all export schemas More formal than loosely-coupled Much effort to resolve semantic inconsistencies

Federated Database Systems - General Advantages Preserves local autonomy Not all data needs to be integrated Provides metadata structures for views (external and export schema, data dictionary)

Federated Database Systems - Disadvantages by Approach Tightly-coupled –similar to global schema integration 1) complex, difficult to make changes dynamically 2) much effort in resolving semantic inconsistencies Loosely-coupled –duplication by different users in building views –updating data defined in views can be difficult

Multidatabase Language Approach No attempt at schema integration Various schema in services provided can be heterogeneous, inconsistent and duplicate information in different ways. Language (e.g. MSQL) is used to integrate databases at run time. Relational data model used as Common Data model

Multidatabase Language Approach - Diagram A,B are schema MSQL is runtime language A B MSQL

Multidatabase Language Approach - Advantages No preparatory work to understand semantics of schema Dynamic -- access latest versions Very skilled users can succeed in reaching their goals Interesting work on multidatabase dependencies

Example Multidatabase Language MSQL (Multidatabase SQL) –Biased towards relational model –Illustrates problems Consider 2 databases –Each on publications of a computing society –And query: –“What is the name, , title for each publication of an author appearing in both of the society’s databases?”

MSQL - Schema Schema 1 (for AIIA): –Contacts (PersonID, Name, , …) –Conference (Name, Type, …) –Attendees(ID, Conf_ID, Speaker, …) –Publ_Papers(P_ID, Title, Author_ID, …) Schema 2 (for IFIP): –Member_Socs(Soc_Name, …) –Conf (Conf_ID, …) –Publ_Papers(P_Ref, Title, Conf_Ref, …) –Authors(Name, , Paper_ID, …) Underlined attributes are primary key; attributes in italics are foreign key.

MSQL for Query USE AIIA, IFIP SELECT Name, , Title FROM Authors, IFIP.Publ_Papers IFIP_Paper, Contacts, AIIA.Publ_papers AIIA_Paper WHERE Authors.Name = Contacts.Name AND Contacts.Person_ID = AIIA_Paper. Author_ID AND Authors.Paper_ID = IFIP_Paper.P_Ref; The USE statement declares the multidatabases which are aliased in the FROM statement to distinguish tables with the same name. Retrieves Name, and Title from both databases.

Potential Problems with MSQL Are domains on name comparable? Can use LET command to create equivalencies of names but does not solve domain mismatch. What if one schema not relational? Entity- Relationship model often used as neutral schema for translation and comparison of heterogeneous features

Multidatabase Language - Disadvantages in General Distribution is not transparent Users must resolve inconsistencies themselves Common language may restrict scope of heterogeneity (relational bias) Local autonomous system may change schema freely (so that existing queries fail)

Comparison of Approaches By coupling: –how tightly is the interoperable system connected to its underlying systems By adaptability: –the ability for the interoperable system to evolve in line with underlying schema By transparency: –the need for the end-user to understand the underlying schema

Comparison of Approaches Coupling AdaptabilityTransparency Approach Global Schema TightLowHigh Integration FederatedMediumMediumMedium Data Bases Multidatbase LowHighLow Languages

Summary Trend: From Global Schema Integration Federated Database Multidatabase Language of lower coupling, higher adaptability, and lower transparency.

Further Reading Management of Heterogeneous and Autonomous Database Systems Elmagarmid, Ahmed Rusinkiewicz, Marek Sheth, Amit Morgan Kaufmann 1999.