A Data Transformation System for Biological Data Sources

Scientific data of importance to biologists in the Human Genome Project resides not only in conventional databases, but in structured files maintained in a number of different formats (e.g. ASN.1 and ACE) as well a.s sequence analysis packages (e.g. BLAST and FASTA). These formats and packages contain a number of data types not found in conventional databases, such as lists and variants, and may be deeply nested. We present in this paper techniques for querying and transforming such data, and illustrate their use in a prototype system developed in conjunction with the Human Genome Center for Chromosome 22. We also describe optimizations performed by the system, a crucial issue for bulk data

BIM semantic-enrichment for built heritage representation

In the built heritage context, BIM has shown difficulties in representing and managing the large and complex knowledge related to non-geometrical aspects of the heritage. Within this scope, this paper focuses on a domain-specific semantic-enrichment of BIM methodology, aimed at fulfilling semantic representation requirements of built heritage through Semantic Web technologies. To develop this semantic-enriched BIM approach, this research relies on the integration of a BIM environment with a knowledge base created through information ontologies. The result is knowledge base system - and a prototypal platform - that enhances semantic representation capabilities of BIM application to architectural heritage processes. It solves the issue of knowledge formalization in cultural heritage informative models, favouring a deeper comprehension and interpretation of all the building aspects. Its open structure allows future research to customize, scale and adapt the knowledge base different typologies of artefacts and heritage activities

An XML Framework for Integrating Continuous Queries, Composite Event Detection, and Database Condition Monitoring for Multiple Data Streams

With advancements in technology over the last ten years, data management issues have evolved from a stored persistent form to also include streaming data generated from sensors and other software monitoring tools. Furthermore, distributed, event-based systems are becoming more prevalent, with a need to develop applications that can dynamically respond to information extracted from data streams. This research is investigating the integration of stream processing and event processing techniques, with expressive filtering capabilities that include queries over persistent databases to provide application context to the filtering process. Distributed Event Processing Agents (DEPAs) continuously filter events from multiple data streams of different formats that provide XML views. Composite events for data streams are expressed using the Composite Event Detection Language (CEDL) and mapped to Composite XQuery (CXQ) for implementation. CXQ is a language that extends XQuery with features from CEDL, including operators for expressing sequence, disjunction, conjunction, repetition, aggregation, and time windows for events. Continuous queries and composite event filters are integrated with techniques for materialized view maintenance and incremental evaluation in condition monitoring to provide efficient ways of enhancing stream filters with database queries. The filtering and event detection load is distributed among multiple DEPAs, with CXQ expressions decomposed to allocate subcomponents of the expression to DEPAs that efficiently communicate in the global detection of composite events. A unique aspect of our research is that it extends XQuery with temporal, composite event features to combine techniques for continuous queries in stream processing, incremental evaluation in condition monitoring, and detection and filtering of composite events, creating an expressive environment for the extraction of meaningful events from multiple data streams with XML views

The normalization of frames as a superclass of relations

M.Sc. (Computer science)Knowledge representation suffers from certain problems, which is not a result of the inadequacies of knowledge representation schemes, but of the way in which they are used and implemented. In the first part of this dissertation we examine the relational model (as used in relational database management systems) and we examine frames (a knowledge representation scheme used in expert systems), as proposed by M. Minsky [MIN75]. We then provide our own definition of frames. In the second part, we examine similarities between the two models (the relational model and our frame model), establishing frames as a superclass of relations. We then define normalization for frames and examine how normalization might solve some of the problems we have identified. We then examine the integration of knowledge-based systems and database management systems and classify our normalization of frames as such an attempt. We conclude by examining the place of normalization within the expert system development life cycl

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion