Enterprise knowledge management: introducing new technologies in traditional Information Systems

Knowledge management systems described in research papers are rarely seen implemented in business realities, at least on a large scale. Companies are often tied to existing systems and cannot or would not revolutionize the situation to accommodate completely new solutions. Given this assumption, this work investigates several small-scale modifications that could be applied to in-place Information Systems so as to improve them with new technologies without major transformations and service discontinuities. The focus is interoperability, with a particular stress on the promotion of the ebXML registry standard. A universal interface for document management was defined, and the conforming “interoperable” DMSs were arranged within an architecture explicitly designed for ebXML-compliant access. This allowed standards-based manipulation of legacy DM systems. The closely related topic of Semantic knowledge management was also tackled. We developed Semantic tools integration for traditional repositories with low architectural impact. Finally, we discussed a novel issue in document categorization, and a new kind of ontology that could be used in that contex

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Strategies for the intelligent selection of components

It is becoming common to build applications as component-intensive systems - a mixture of fresh code and existing components. For application developers the selection of components to incorporate is key to overall system quality - so they want the `best\u27. For each selection task, the application developer will de ne requirements for the ideal component and use them to select the most suitable one. While many software selection processes exist there is a lack of repeatable, usable, exible, automated processes with tool support. This investigation has focussed on nding and implementing strategies to enhance the selection of software components. The study was built around four research elements, targeting characterisation, process, strategies and evaluation. A Post-positivist methodology was used with the Spiral Development Model structuring the investigation. Data for the study is generated using a range of qualitative and quantitative methods including a survey approach, a range of case studies and quasiexperiments to focus on the speci c tuning of tools and techniques. Evaluation and review are integral to the SDM: a Goal-Question-Metric (GQM)-based approach was applied to every Spiral