Abstract: In our research, we propose several techniques for executing document preprocessing, stop-word removal, term extraction and information retrieval with two fundamental IR models in SQL: the Vector Space Model and the Okapi Probabilistic Model.

Abstract: Digital libraries are generally queried to and specific documents, but not to compute aggregations in an exploratory fashion. We developed a system that enables OLAP processing on the metadata of Digital Libraries by exploiting a combination of efficient UDFs and optimized SQL queries. We propose the creation of OLAP networks, a data structure that contains a summarized representation of the original data set, on a document metadata collection to enrich ordinary retrievals. With such a network, we provide users the ability to quickly explore the metadata collection. Additionally, our system provides capabilities for setting support thresholds, which we evaluate using iceberg queries, visualizing, and navigating large digital libraries.

We extended this work in future papers to obtain query recommendations and ontologies.

Abstract: There exist many interrelated information sources on the Internet that can be categorized into structured (database) and semistructured (documents). A key challenge is to integrate, query and analyze such heterogeneous collections of information. In this paper, we defend the idea of building web metadata repositories using relational databases as the main source and central data management technology of structured data, enriched by the semistructured data surrounding it. Our proposal rests on the assumption that heterogeneous relational databases can be integrated (i.e. entity resolution is assumed to work well) and thus can serve as references for external data. That is, we tackle the problem of integrating information in the deep web, departing from databases. We discuss a prototype system that can integrate and query metadata and related documents, based on relational database technology. Metadata includes database ER model elements like database name, table, and column (entity, attribute). Web document data include files, documents and web pages. Links between metadata and external documents are built with SQL queries. Once databases and documents are linked, they are managed and queried with SQL. We discuss an interesting scientific application of our solution with a water pollution database.