The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Database abstractions: aggregation and generalization
ACM Transactions on Database Systems (TODS)
A relational model of data for large shared data banks
Communications of the ACM
An Introduction to Database Systems
An Introduction to Database Systems
Remarks on the algebra of non first normal form relations
PODS '82 Proceedings of the 1st ACM SIGACT-SIGMOD symposium on Principles of database systems
Methods for the administration of textual data in database systems
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
Composite document extended retrieval: an overview
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Design of an Integrated Information Retrieval/Database Management System
IEEE Transactions on Knowledge and Data Engineering
Toward the design of a methodology to predict relevance through multiple sources of evidence
PIKM '10 Proceedings of the 3rd workshop on Ph.D. students in information and knowledge management
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The data model, i.e. data structures and operations needed for a system integrating the management of formated textual data (DBMIRS) are discussed. It is investigated how this data model fits into the ANSI-SPARC three schema architecture for data base management systems. The conclusion is that the DBMIRS should be regarded to be a new external data model. This would require only small changes to the concepts discussed so far for the conceptual and internal level. The advantages of this approach are:- The dichotomy between IRS and DBMS does not further exist.- The main part of the software can be used for IR systems as well as DB systems.- IR research can concentrate on the problems inherent in the retrieval of texts, e.g. automatic indexing, relevance feedback techniques.