A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
State-space problem-reduction, and theorem proving—some relationships
Communications of the ACM
Implementation of integrity constraints and views by query modification
SIGMOD '75 Proceedings of the 1975 ACM SIGMOD international conference on Management of data
Performing inferences over relation data bases
SIGMOD '75 Proceedings of the 1975 ACM SIGMOD international conference on Management of data
Experiments with a resolution-based deductive question-answering system and a proposed clause representation for parallel search.
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
A problem-oriented inferential database system
ACM Transactions on Database Systems (TODS)
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
Logic and Databases: A Deductive Approach
ACM Computing Surveys (CSUR)
A Formal System for Reasoning about Programs Accessing a Relational Database
ACM Transactions on Programming Languages and Systems (TOPLAS)
The logic of a relational data manipulation language
POPL '79 Proceedings of the 6th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Classification and Compilation of Linear Recursive Queries in Deductive Databases
IEEE Transactions on Knowledge and Data Engineering
Toward Practical Constraint Databases
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Fundamental and secondary issues in the design of non-procedural relational languages
VLDB '79 Proceedings of the fifth international conference on Very Large Data Bases - Volume 5
The divide-and-conquer subgoal-ordering algorithm for speeding up logic inference
Journal of Artificial Intelligence Research
Evaluating queries in deductive databases by generating
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
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An inferential relational system is one in which data in the system consists of both explicit facts and general axioms (or “views”). The general axioms are used together with the explicit facts to derive the facts that are implicit (virtual relations) within the system. A top-down algorithm, as used in artificial intelligence work, is described to develop inferences within the system. The top-down approach starts with the query, a conjunction of relations, to be answered. Either a relational fact solves a given relation in a conjunct, or the relation is replaced by a conjunct of relations which must be solved to solve the given relation. The approach requires that one and only one relation in a conjunction be replaced (or expanded) by the given facts and general axioms. The decision to expand only a single relation is termed a selection function. It is shown for relational systems that such a restriction still guarantees that a solution to the problem will be found if one exists.The algorithm provides for heuristic direction in the search process. Experimental results are presented which illustrate the techniques. A bookkeeping mechanism is described which permits one to know when subproblems are solved. It further facilitates the outputting of reasons for the deductively found answer in a coherent fashion.