A Deductive Database is a type of database that can make conclusions or we can say deductions using a sets of well defined rules and fact that are stored in the database. In today’s world as we deal with a large amount of data, this deductive database provides a lot of advantages. It helps to combine the RDBMS with logic programming. To design a deductive database a purely declarative programming language called Datalog is used.
The implementations of deductive databases can be seen in LDL (Logic Data Language), NAIL (Not Another Implementation of Logic), CORAL, and VALIDITY.
The use of LDL and VALIDITY in a variety of business/industrial applications are as follows.
1. LDL Applications:
This system has been applied to the following application domains:
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Enterprise modeling:
Data related to an enterprise may result in an extended ER model containing hundreds of entities and relationship and thousands of attributes.This domain involves modeling the structure, processes, and constraints within an enterprise. -
Hypothesis testing or data dredging:
This domain involves formulating a hypothesis, translating in into an LDL rule set and a query, and then executing the query against given data to test the hypothesis. This has been applied to genome data analysis in the field of microbiology, where data dredging consists of identifying the DNA sequences from low-level digitized auto radio graphs from experiments performed on E.Coli Bacteria. -
Software reuse:
A small fraction of the software for an application is rule-based and encoded in LDL (bulk is developed in standard procedural code). The rules give rise to a knowledge base that contains, A definition of each C module used in system and A set of rules that defines ways in which modules can export/import functions, constraints and so on. The “Knowledge base” can be used to make decisions that pertain to the reuse of software subsets. This is being experimented within banking software.
2. VALIDITY Applications:
Validity combines deductive capabilities with the ability to manipulate complex objects (OIDs, inheritance, methods, etc). It provides a DOOD data model and language called DEL (Datalog Extended Language), an engine working along a client-server model and a set of tools for schema and rule editing, validation, and querying.
The following are some application areas of the VALIDITY system:
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Electronic commerce:
In electronic commerce, complex customers profiles have to be matched against target descriptions. The matching process is also described by rules, and computed predicates deal with numeric computations. The declarative nature of DEl makes the formulation of the matching algorithm easy. -
Rules-governed processes:
In a rules-governed process, well defined rules define the actions to be performed. In those process some classes are modeled as DEL classes. The main advantage of VALIDITY is the ease with which new regulations are taken into account. -
Knowledge discovery:
The goal of knowledge discovery is to find new data relationships by analyzing existing data. An application prototype developed by University of Illinois utilizes already existing minority student data that has been enhanced with rules in DEL. -
Concurrent Engineering:
A concurrent engineering applications deals with large amounts of centralized data, shared by several participants. An application prototype has been developed in the area of civil engineering. The design data is modeled using the object-oriented power of the DEL language. DEL is able to handle transformation of rules into constraints, and it can also handle any closed formula as an integrity constraint.