Heterogeneous Data Resources Research Articles

Hypertext configurations: Genres in networked digital media

The article presents a conceptual framework for distinguishing different sorts of heterogeneous digital materials. The hypothesis is that a wide range of heterogeneous data resources can be characterized and classified due to their particular configurations of hypertext features such as scripts, links, interactive processes, and time scalings, and that the hypertext configuration is a major but not sole source of the messiness of big data. The notion of hypertext will be revalidated, placed at the center of the interpretation of networked digital media, and used in the analysis of the fast‐growing amounts of heterogeneous digital collections, assemblages, and corpora. The introduction summarizes the wider background of a fast‐changing data landscape.

Retracted: Semantic Information Integration with Linked Data Mashups Approaches

The introduction of semantic web and Linked Data helps facilitate sharing of data on the Internet more easily. Subsequently, the resource description framework (RDF) is the standard in publishing structured data resources on the Internet and is used in interconnecting with other data resources. To remedy the data integration issues of the traditional web mashups, the semantic web technology uses the Linked Data based on RDF data model as the unified data model for combining, aggregating, and transforming data from heterogeneous data resources to build Linked Data mashups. There have been tremendous amounts of efforts of semantic web community to enable Linked Data mashups but there is still lack of a systematic survey on concepts, technologies, applications, and challenges. Therefore, in this paper, we investigate in detail semantic mashups research and application approaches in the information integration. This paper also presents a Linked Data mashup application as an illustration of the proposed approa...

STSM: An Infrastructure for Unifying Steel Knowledge and Discovering New Knowledge

There are rich data resources in materials science, but these data resources are heterogeneous in level of system, structure, syntax, and semantics. Therefore, a domain ontology is necessary and helpful for the integration of these heterogeneous data resources, and it is also one of the main tasks of materials informatics. In this paper, we propose a steel semantic model (named STSM) based on ontology and logic rules for the representation of the steel knowledge. STSM is developed with the consideration of the features of materials data and the developed process is presented. Then, we describe the content and organization of STSM which covers the basic knowledge in steel domain. Further, domain axioms and logic rules are designed to enhance the reasoning ability of STSM. STSM is built and tested in protege, and an experimental prototype based on Jena is also developed to demonstrate the effectiveness of STSM.

Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26-27 June 2013, Research Triangle Park, NC, USA.

A joint US-EU workshop on enhancing data sharing and exchange in toxicogenomics was held at the National Institute for Environmental Health Sciences. Currently, efficient reuse of data is hampered by problems related to public data availability, data quality, database interoperability (the ability to exchange information), standardization and sustainability. At the workshop, experts from universities and research institutes presented databases, studies, organizations and tools that attempt to deal with these problems. Furthermore, a case study showing that combining toxicogenomics data from multiple resources leads to more accurate predictions in risk assessment was presented. All participants agreed that there is a need for a web portal describing the diverse, heterogeneous data resources relevant for toxicogenomics research. Furthermore, there was agreement that linking more data resources would improve toxicogenomics data analysis. To outline a roadmap to enhance interoperability between data resources, the participants recommend collecting user stories from the toxicogenomics research community on barriers in data sharing and exchange currently hampering answering to certain research questions. These user stories may guide the prioritization of steps to be taken for enhancing integration of toxicogenomics databases.

Retracted: Semantic Information Integration with Linked Data Mashups Approaches

The introduction of semantic web and Linked Data helps facilitate sharing of data on the Internet more easily. Subsequently, the resource description framework (RDF) is the standard in publishing structured data resources on the Internet and is used in interconnecting with other data resources. To remedy the data integration issues of the traditional web mashups, the semantic web technology uses the Linked Data based on RDF data model as the unified data model for combining, aggregating, and transforming data from heterogeneous data resources to build Linked Data mashups. There have been tremendous amounts of efforts of semantic web community to enable Linked Data mashups but there is still lack of a systematic survey on concepts, technologies, applications, and challenges. Therefore, in this paper, we investigate in detail semantic mashups research and application approaches in the information integration. This paper also presents a Linked Data mashup application as an illustration of the proposed approaches.

An Efficient and Scalable Approach for Ontology Instance Matching

Ontology instance matching is a key interoperability enabler across heterogeneous data resources in the Semantic Web for integrating data semantically. Although most of the research has been emphasized on schema level matching so far, research on ontology matching is shifting from ontology schema or concept level to instance level to fulfill the vision of “Web of Data”. Ontology instances define data semantically and are kept in knowledge base. Since, heterogeneous sources of massive ontology instances grow sharply day-by-day, scalability has become a major research concern in ontology instance matching of semantic knowledge bases. In this study, we propose a method by filtering instances of knowledge base into two stages to address the scalability issue. First stage groups the instances based on the relation of concepts and next stage further filters the instances based on the properties associated to instances. Then, our instance matcher works by comparing an instance within a classification group of one knowledge base against the instances of same sub-group of other knowledge base to achieve interoperability. We experiment our proposed method with several benchmark data sets namely OAEI-2009, OAEI-2010 and OAEI-2011. On comparison with other baseline methods, our proposed method shows satisfactory result.

Visualizing Information Science Knowledge by Modelling Domain Ontology (OIS)

Information Science (IS) is one of the areas that need a unified ontology model to facilitate information in order to access the heterogeneous data resources, and share a common understanding of the domain knowledge. The aim of the study is to develop a generic model of ontology that serves as a foundation of knowledge modelling for applications and aggregation with other ontologies. This study adopts the Methontology methodology to develop an Ontology of Information Science (OIS). To support ontology development, the virtual community of practice of Information science (Ontocop) was employed. The Ontology was coded using Protégé and the OWL web ontology language. The main achievement of the study is that a new model of Information Science Ontology (OIS) has been constructed, which is a generic model that contains only the key objects and associated attributes with their relationships. The model defines the 706 concept, which will be widely used in Information Science applications. The research reveals that OIS ontology is a model that meets the ontology quality criteria for the subject area. It is concluded that OIS ontology unifies information science knowledge, which is composed of Library, Computer, and Archival science, by creating a theoretical base that is useful for further practical systems. The OIS ontology can be reused as the basis for any domain development.

Querying Uncertain Data in Geospatial Object-relational Databases Using SQL and Fuzzy Sets

Abstract This paper deals with uncertainty modeling in spatial object-relational databases by the use of Structured Query Language (SQL). The fundamental principles of uncertainty modeling by fuzzy sets are applied in the area of geographic information systems (GIS) and spatial databases. A spatial database system includes types of spatial data and implements the spatial extension of SQL. The implementation of the principles of fuzzy logic to spatial databases brings an opportunity for the efficient processing of uncertain data, which is important, especially when using various data sources (e.g., multi-criteria decision making (MCDM) on the basis of heterogeneous spatial data resources). The modeling and data processing of uncertainties are presented in relation to the applicable International Organization for Standardization (ISO) standards (standards of the series 19100 Geographic information) and the relevant specifications of the Open Geospatial Consortium (OGC). The fuzzy spatial query approach is applied and tested on a case study with a fundamental database for GIS in Slovakia.

Study on Data Warehouse Based Equipment Support Data Management

There are more and more data produced in equipment management systems. For decision making, we must collect and integrate all kinds of data and information from different management systems. By analyzing the distributional and heterogeneous data resources, the architecture of equipment management data warehouse is proposed in this paper. We discussed important components of data warehouse for equipment support. The components include metadata, data market, data granularity and data partitioning. By discussing the organization of the data warehouse from the macroscopic point of view, the equipment support data management system with the function of decision making will be used in the future.

Research on Heterogeneous Data resource Management Model in Cloud Environment

With the development of the cloud computing, more and more enterprises migrate their application systems to the cloud computing environment, these systems need multiple data resource collaborative work, and integrate existing heterogeneous data storage system. This paper aiming at the massive data processing, proposes a kind of heterogeneous data resource management model. This model implement massive resources storage, massive storage network’ generation, update and balance of workload, proposes the security management and monitoring methods. The model proposed by this paper gives a novel solution to the heterogeneous data resource management and application in the cloud.

Visualizing Information Science Knowledge by Modelling Domain Ontology (OIS)

Information Science (IS) is one of the areas that need a unified ontology model to facilitate information in order to access the heterogeneous data resources, and share a common understanding of the domain knowledge. The aim of the study is to develop a generic model of ontology that serves as a foundation of knowledge modelling for applications and aggregation with other ontologies. This study adopts the Methontology methodology to develop an Ontology of Information Science (OIS). To support ontology development, the virtual community of practice of Information science (Ontocop) was employed. The Ontology was coded using Protégé and the OWL web ontology language. The main achievement of the study is that a new model of Information Science Ontology (OIS) has been constructed, which is a generic model that contains only the key objects and associated attributes with their relationships. The model defines the 706 concept, which will be widely used in Information Science applications. The research reveals that OIS ontology is a model that meets the ontology quality criteria for the subject area. It is concluded that OIS ontology unifies information science knowledge, which is composed of Library, Computer, and Archival science, by creating a theoretical base that is useful for further practical systems. The OIS ontology can be reused as the basis for any domain development.

Hybrid Ground Data Model for Interacting Simulations in Mechanized Tunneling

Because of the ongoing expansion of urban areas, transportation systems are being increasingly moved beneath the ground into tunnels. However, the construction of tunnels is very challenging regarding unexpected geological conditions, the protection of existing buildings from damage, and the control of financial and safety risks. During the tunnel construction process, different simulations are performed to predict, for example, settlements, soil excavation, or grouting behavior. Although these simulations should strongly interact, they often use independent heterogeneous geological data models and resources, thus resulting in unreliable predictions. This paper presents a hybrid ground data model along with necessary access concepts to support the automated management and exchange of continuously updated geological information among interacting simulations. The ground data model uses a boundary and a decomposition representation of the ground geometry with material information attached to each representation. These representations are converted to one another on demand to make up-to-date ground information available for participating tunneling simulations. To validate the proposed model and concepts, a case study is presented in which two typical simulations are coupled using the prototypically implemented hybrid ground data model.

Metadata-based Information Resource Integration for Research Management

With the continuous development of information technology, information data for research management show s an explosive growth. However, it is not an effective integration method which leads to the phenomenon of the islands of information. Various types of data exchange technology can integrate heterogeneous data resources, and provide data support for comprehensive data utilization and analysis. This research proposes the data integration framework and technology based on metadata. By taking the information resource for research management as a case, the research also analyzes the information resources integration for research management.

Applying knowledge-anchored hypothesis discovery methods to advance clinical and translational research: the OAMiner project

The conduct of clinical and translational research regularly involves the use of a variety of heterogeneous and large-scale data resources. Scalable methods for the integrative analysis of such resources, particularly when attempting to leverage computable domain knowledge in order to generate actionable hypotheses in a high-throughput manner, remain an open area of research. In this report, we describe both a generalizable design pattern for such integrative knowledge-anchored hypothesis discovery operations and our experience in applying that design pattern in the experimental context of a set of driving research questions related to the publicly available Osteoarthritis Initiative data repository. We believe that this 'test bed' project and the lessons learned during its execution are both generalizable and representative of common clinical and translational research paradigms.

A Bayesian Antedependence Model for Whole Genome Prediction

Hierarchical mixed effects models have been demonstrated to be powerful for predicting genomic merit of livestock and plants, on the basis of high-density single-nucleotide polymorphism (SNP) marker panels, and their use is being increasingly advocated for genomic predictions in human health. Two particularly popular approaches, labeled BayesA and BayesB, are based on specifying all SNP-associated effects to be independent of each other. BayesB extends BayesA by allowing a large proportion of SNP markers to be associated with null effects. We further extend these two models to specify SNP effects as being spatially correlated due to the chromosomally proximal effects of causal variants. These two models, that we respectively dub as ante-BayesA and ante-BayesB, are based on a first-order nonstationary antedependence specification between SNP effects. In a simulation study involving 20 replicate data sets, each analyzed at six different SNP marker densities with average LD levels ranging from r(2) = 0.15 to 0.31, the antedependence methods had significantly (P < 0.01) higher accuracies than their corresponding classical counterparts at higher LD levels (r(2) > 0. 24) with differences exceeding 3%. A cross-validation study was also conducted on the heterogeneous stock mice data resource (http://mus.well.ox.ac.uk/mouse/HS/) using 6-week body weights as the phenotype. The antedependence methods increased cross-validation prediction accuracies by up to 3.6% compared to their classical counterparts (P < 0.001). Finally, we applied our method to other benchmark data sets and demonstrated that the antedependence methods were more accurate than their classical counterparts for genomic predictions, even for individuals several generations beyond the training data.

Multi Dimension Knowledge Mining in Heterogeneous Data Resources

With study on heterogeneous network environment and data source object, this paper has explored a variety of data in the feasibility knowledge discovery in accordance with the model. The method has been achieved on the data validation of ideas in how to effectively use the network data mining resources and the access to potentially obtain the valuable domain knowledge. The main research activities include: 1.presenting the domain knowledge mining model on the network environment. 2. Presenting a new model of the probability of topic: Topic- Author model. 3. Presenting a Blog knowledge framework with the analysis and diffusion of ideas on the theme of mining, the results shows that the field of knowledge is proposed the mining method, which is able to find a large number of valuable, potentially multi-dimensional knowledge and application of knowledge. Therefore, it can provide the users with a variety of services and support the information age, knowledge acquisition and learning.

ConsensusPathDB: toward a more complete picture of cell biology

ConsensusPathDB is a meta-database that integrates different types of functional interactions from heterogeneous interaction data resources. Physical protein interactions, metabolic and signaling reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With 155 432 human, 194 480 yeast and 13 648 mouse complex functional interactions (originating from 18 databases on human and eight databases on yeast and mouse interactions each), ConsensusPathDB currently constitutes the most comprehensive publicly available interaction repository for these species. The Web interface at http://cpdb.molgen.mpg.de offers different ways of utilizing these integrated interaction data, in particular with tools for visualization, analysis and interpretation of high-throughput expression data in the light of functional interactions and biological pathways.

Achieving Interoperation of Grid Data Resources via Workflow Level Integration

Production Grids are becoming widely utilized by the e-Science community to run computation and data intensive experiments more efficiently. Unfortunately, different production Grid infrastructures are based on different middleware technologies, both for computation and for data access. Although there is significant effort from the Grid community to standardize the underlying middleware, solutions that allow existing non-standard tools to interoperate are one of the major concerns of Grid users today. This paper describes the generic requirements towards the interoperation of Grid data resources within computational workflows, and suggests integration techniques that allow workflow engines to access various heterogeneous data resources during workflow execution. Reference implementations of these techniques are presented and recommendations on their applicability and suitability are made.

Bringing together an ocean of information: An extensible data integration framework for biological oceanography

As increasing volumes and varieties of data are becoming available online, the challenges of accessing and using heterogeneous data resources are growing. We have developed a mediator-based data integration system called Cartel for biological oceanography data. A mediation approach is appropriate in cases where a single central warehouse is not desirable, such as when the needed data sources change frequently through time, or when there are advantages for holding heterogeneous data in their native formats. Through Cartel, data sources of a variety of types can be registered to the system, and users can query against simplified virtual schemas, without needing to know the underlying schema and computational capabilities of each data source. The system can operate on a variety of relational and geospatial data formats, and can perform joins between formats. We tested the performance of the Cartel mediator in two biological oceanography application areas, and found that the system was able to support the variety of data types needed in a typical ecology study, but that the response times were unacceptably slow when very large databases (i.e. Ocean Biogeographic Information System and the World Ocean Atlas) were used. Indexing and caching are currently being added to the system to improve response times. The mediator is an open-source product, and was developed to be a generic, extensible component available to projects developing oceanography data systems.

SDS: A Scalable Data Services System in Data Grid

It is very complex and of low efficiency for grid users to access heterogeneous diverse data resources distributed over the whole wide area network. Data Grid has denoted a network of storage resources, from archival systems, to caches, to databases, that are linked across a distributed network. And it should provide integrated, scalable data services which implement more wide range of transparent access to data resources in Data Grid, such as location transparency, time transparency. In this paper, we describe a SDS system which can implement integrated, scalable data services. We have implemented one of the important building blocks for SDS is the server called DSB which can provide integrated data services. The DSB bases on cluster and agent technologies. Agent-based DSB can cleverly prefetch required data, replicate them among DSBs. Each DSB based on cluster implements a single entry and a virtual integrated storage system in a data domain. As far as whole SDS, Multiple DSBs are formed cluster data services, which are scalable, and provide a single entry for all data grid users, and provide a virtual integrated mass storage system, and hide distributed heterogeneous low-level data resources, and insure load balance of each server. SDS architecture supporting these various scenarios, are also described.