A unified ontology-based data integration approach for the internet of things

Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

RDF-Based Data Integration for Workflow Systems

To meet the requirements of interoperability, the enactment of workflow systems for processes should tackle the problem of data integration for effective data sharing and exchange. This paper aims at flexibly describing workflow entities and relationships by innovative ontology engineering, which are emerging in process-centred environments, supported by Resource Description Framework (RDF) based languages and tools. Our novel framework takes into consideration to position the ontology level in the data integration dimension. Having taken a more realistic approach towards interoperability, we present basic constructs of a workflow specific ontology, with a suite of classes and properties selectively created. In particular, we demonstrate an example description of Event Condition Action (ECA) rules by extensions of RDF. As an inter-lingua, the proposed vocabulary and semantics can be mapped onto other process description languages as well as the simple XML-based data representation of our earlier workflow prototype

Methods and techniques for generation and integration of Web ontology data

University of Technology, Sydney. Faculty of Information Technology.Data integration over the web or across organizations encounters several unfavorable features: heterogeneity, decentralization, incompleteness, and uncertainty, which prevent information from being fully utilized for advanced applications such as decision support services. The basic idea of ontology related approaches for data integration is to use one or more ontology schemas to interpret data from different sources. Several issues will come up when actually implementing the idea: (1) How to develop the domain ontology schema(s) used for the integration; (2) How to generate ontology data for domain ontology schema if the data are not in the right format and to create and manage ontology data in an appropriate way; (3) How to improve the quality of integrated ontology data by reducing duplications and increasing completeness and certainty. This thesis focuses on the above issues and develops a set of methods to tackle them. First, a key information mining method is developed to facilitate the development of interested domain ontology schemas. It effectively extracts from the web sites useful terms and identifies taxonomy information which is essential to ontology schema construction. A prototype system is developed to use this method to help create domain ontology schemas. Second, this study develops two complemented methods which are light weighted and more semantic web oriented to address the issue of ontology data generation. One method allows users to convert existing structured data (mostly XML data) to ontology data; another enables users to create new ontology data directly with ease.In addition, a web-based system is developed to allow users to manage the ontology data collaboratively and with customizable security constraints. Third, this study also proposes two methods to perform ontology data matching for the improvement of ontology data quality when an integration happens. One method uses the clustering approach. It makes use of the relational nature of the ontology data and captures different situations of matching, therefore resulting in an improvement of performance compared with the traditional canopy clustering method. The other method goes further by using a learning mechanism to make the matching more adaptive. New features are developed for training matching classifier by exploring particular characteristics of ontology data. This method also achieves better performance than those with only ordinary features. These matching methods can be used to improve data quality in a peer-to-peer framework which is proposed to integrate available ontology data from different peers

Bioinformatics service reconciliation by heterogeneous schema transformation

This paper focuses on the problem of bioinformatics service reconciliation in a generic and scalable manner so as to enhance interoperability in a highly evolving field. Using XML as a common representation format, but also supporting existing flat-file representation formats, we propose an approach for the scalable semi-automatic reconciliation of services, possibly invoked from within a scientific workflows tool. Service reconciliation may use the AutoMed heterogeneous data integration system as an intermediary service, or may use AutoMed to produce services that mediate between services. We discuss the application of our approach for the reconciliation of services in an example bioinformatics workflow. The main contribution of this research is an architecture for the scalable reconciliation of bioinformatics services