Automated Data Integration Research Articles

A semantic model for species description applied to the ensign wasps (hymenoptera: evaniidae) of New Caledonia.

Taxonomic descriptions are unparalleled sources of knowledge of life's phenotypic diversity. As natural language prose, these data sets are largely refractory to computation and integration with other sources of phenotypic data. By formalizing taxonomic descriptions using ontology-based semantic representation, we aim to increase the reusability and computability of taxonomists' primary data. Here, we present a revision of the ensign wasp (Hymenoptera: Evaniidae) fauna of New Caledonia using this new model for species description. Descriptive matrices, specimen data, and taxonomic nomenclature are gathered in a unified Web-based application, mx, then exported as both traditional taxonomic treatments and semantic statements using the OWL Web Ontology Language. Character:character-state combinations are then annotated following the entity–quality phenotype model, originally developed to represent mutant model organism phenotype data; concepts of anatomy are drawn from the Hymenoptera Anatomy Ontology and linked to phenotype descriptors from the Phenotypic Quality Ontology. The resulting set of semantic statements is provided in Resource Description Framework format. Applying the model to real data, that is, specimens, taxonomic names, diagnoses, descriptions, and redescriptions, provides us with a foundation to discuss limitations and potential benefits such as automated data integration and reasoner-driven queries. Four species of ensign wasp are now known to occur in New Caledonia: Szepligetella levipetiolata, Szepligetella deercreeki Deans and Mikó sp. nov., Szepligetella irwini Deans and Mikó sp. nov., and the nearly cosmopolitan Evania appendigaster. A fifth species, Szepligetella sericea, including Szepligetella impressa, syn. nov., has not yet been collected in New Caledonia but can be found on islands throughout the Pacific and so is included in the diagnostic key. [Biodiversity informatics; Evaniidae; New Caledonia; new species; ontology; semantic phenotypes; semantic species description; taxonomy.]

Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration.

Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins (choline kinase, PPIase and uromodulin) and three different web-based data search engines focused on protein interaction data retrieval (PSICQUIC, DASMI and BIPS) were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions.

PLAN4BUSINESS – A SERVICE PLATFORM FOR AGGREGATION, PROCESSING AND ANALYSIS OF URBAN AND REGIONAL PLANNING DATA

Abstract. The paper presents the main objectives of the plan4business project that aims to develop a service platform for integration, analysing and presenting spatial planning data. The platform should serve as a catalogue of planning data enabling user automated data integration and spatial analyses. The contribution presents the approach for data integration, relation to INSPIRE and the technical solution for data analysis.

Secure and efficient integration of big data for multi‐cells based on micro images

AbstractInformation of multi‐cells is big data because of the enormous quantities of various cells as well as their parameters and status. To securely and efficiently integrate all the cells' information and trace multi‐cells are challenging due to varying number of the multi‐cells, as well as the complicacy of the multi‐cells' movement. In this paper, an automatic big data integration algorithm based on the optical transfer function is proposed. The experimental results show that the algorithm can securely and efficiently integrate all the cell information and simultaneously track a large quantity of cells. Copyright © 2013 John Wiley & Sons, Ltd.

An integrative clinical database and diagnostics platform for biomarker identification and analysis in ion mobility spectra of human exhaled air.

Over the last decade the evaluation of odors and vapors in human breath has gained more and more attention, particularly in the diagnostics of pulmonary diseases. Ion mobility spectrometry coupled with multi-capillary columns (MCC/IMS), is a well known technology for detecting volatile organic compounds (VOCs) in air. It is a comparatively inexpensive, non-invasive, high-throughput method, which is able to handle the moisture that comes with human exhaled air, and allows for characterizing of VOCs in very low concentrations. To identify discriminating compounds as biomarkers, it is necessary to have a clear understanding of the detailed composition of human breath. Therefore, in addition to the clinical studies, there is a need for a flexible and comprehensive centralized data repository, which is capable of gathering all kinds of related information. Moreover, there is a demand for automated data integration and semi-automated data analysis, in particular with regard to the rapid data accumulation, emerging from the high-throughput nature of the MCC/IMS technology. Here, we present a comprehensive database application and analysis platform, which combines metabolic maps with heterogeneous biomedical data in a well-structured manner. The design of the database is based on a hybrid of the entity-attribute-value (EAV) model and the EAV-CR, which incorporates the concepts of classes and relationships. Additionally it offers an intuitive user interface that provides easy and quick access to the platform’s functionality: automated data integration and integrity validation, versioning and roll-back strategy, data retrieval as well as semi-automatic data mining and machine learning capabilities. The platform will support MCC/IMS-based biomarker identification and validation. The software, schemata, data sets and further information is publicly available at http://imsdb.mpi-inf.mpg.de.

CAS-Based Approach for Automatic Data Integration

Research of automatic integration of structured and semi-structured data has not resulted in success over the past fifty years. No theory of data integration exists. It is unknown what the theoretical necessary requirements are, to fully support automatic data integration from autonomous heterogeneous data sources. Therefore, it is not possible to objectively evaluate if and how much new algorithms, techniques, and specifically Data Definition Languages, move towards meeting such theoretical requirements. To overcome the serious reverse salient the field and industry are in, it will be helpful if a data integration theory would be developed. This article proposes a new look at data integration by using complex adaptive systems principles to analyze current shortcomings and propose a direction that may lead to a data integration theory.

COSARA: Integrated Service Platform for Infection Surveillance and Antibiotic Management in the ICU

The Intensive Care Unit is a data intensive environment where large volumes of patient monitoring and observational data are daily generated. Today, there is a lack of an integrated clinical platform for automated decision support and analysis. Despite the potential of electronic records for infection surveillance and antibiotic management, different parts of the clinical data are stored across databases in their own formats with specific parameters, making access to all data a complex and time-consuming challenge. Moreover, the motivation behind physicians' therapy decisions is currently not captured in existing information systems. The COSARA research project offers automated data integration and services for infection control and antibiotic management for Ghent University Hospital. The platform not only gathers and integrates all relevant data, it also presents the information visually at the point of care. In this paper, we describe the design and value of COSARA for clinical treatment and infectious diseases monitoring. On the one hand, this platform can facilitate daily bedside follow-up of infections, antibiotic therapies and clinical decisions for the individual patient, while on the other hand, the platform serves as management view for infection surveillance and care quality improvement within the complete ICU ward. It is shown that COSARA is valuable for registration, real-time presentation and management of infection-related and antibiotics data.

Experimental validation of PID based cascade control system through SCADA–PLC–OPC and internet architectures

The paper presents the experimental validation procedure of a simple cascade control system through number of architectures, such as SCADA, PLC, OPC and internet. The performance and effectiveness of individual architecture is evaluated on the basis of data rate, rise time, peak time and settling time. In this study, PID is implemented on Micrologix-1200 PLC and RSView-32 SCADA has been used with RSLinx communication software. The PLC–SCADA control loop is implemented with the functionalities such as, real time data analysis, set point modifications, automatic report generation and integration of data with MS-Excel and MS-Access. The enhancement in project data analysis is effectively done through the integration of PLC with Labview. The remote monitoring and control of process parameters is done using NET-ENI. The obtained result proved that the conventional SCADA based control system can be enhanced further more with PLC as well as NI-OPC server significantly.

Automated extraction and semantic analysis of mutation impacts from the biomedical literature

BackgroundMutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually curating the rich and fast growing repository of biomedical literature is expensive and time-consuming. As a solution, text mining approaches have increasingly been deployed in the biomedical domain. While the detection of single-point mutations is well covered by existing systems, challenges still exist in grounding impacts to their respective mutations and recognizing the affected protein properties, in particular kinetic and stability properties together with physical quantities.ResultsWe present an ontology model for mutation impacts, together with a comprehensive text mining system for extracting and analysing mutation impact information from full-text articles. Organisms, as sources of proteins, are extracted to help disambiguation of genes and proteins. Our system then detects mutation series to correctly ground detected impacts using novel heuristics. It also extracts the affected protein properties, in particular kinetic and stability properties, as well as the magnitude of the effects and validates these relations against the domain ontology. The output of our system can be provided in various formats, in particular by populating an OWL-DL ontology, which can then be queried to provide structured information. The performance of the system is evaluated on our manually annotated corpora. In the impact detection task, our system achieves a precision of 70.4%-71.1%, a recall of 71.3%-71.5%, and grounds the detected impacts with an accuracy of 76.5%-77%. The developed system, including resources, evaluation data and end-user and developer documentation is freely available under an open source license at http://www.semanticsoftware.info/open-mutation-miner.ConclusionWe present Open Mutation Miner (OMM), the first comprehensive, fully open-source approach to automatically extract impacts and related relevant information from the biomedical literature. We assessed the performance of our work on manually annotated corpora and the results show the reliability of our approach. The representation of the extracted information into a structured format facilitates knowledge management and aids in database curation and correction. Furthermore, access to the analysis results is provided through multiple interfaces, including web services for automated data integration and desktop-based solutions for end user interactions.

Design and implementation of a haptic‐based virtual assembly system

PurposeHaptics can significantly enhance the user's sense of immersion and interactivity. Especially in an assembly task, haptic feedback can help designers to have a better understanding of virtual objects and to increase task efficiency. The purpose of this paper is to investigate the design and implementation of a haptic‐based virtual assembly system (HVAS).Design/methodology/approachA multi‐thread system structure was designed, an automatic data integration interface was developed to transfer geometry, topology, assembly and physics information from a computer‐aided design system to virtual reality application, and a hierarchical constraint‐based data model and scene graph structure was designed to construct the virtual assembly environment. Unlike traditional virtual assembly systems based on collision detection or geometry constraint only, a physics‐based modeling approach combining with haptic feedback and geometry constraint was undertaken to realize and guide the realistic assembly process. When two parts collide into each other, the force and torque can be computed and provide feedback, and a spring‐mass model is used to prevent penetration and simulate dynamic behaviour. When two parts are close enough to each other and the assembly simulation state is activated, a geometry constraint can be captured, an attractive force can be generated to guide the user to assemble the part along the correct position, and the repulsive force can also be generated to realize the mating process as natural and realistic as in real life.FindingsThe implementation details and application examples demonstrate that haptic‐based virtual assembly is a valuable tool for assembly design and process planning.Originality/valueThe paper presents an HVAS.

Improved verifiability scheme for data storage in cloud computing

In Cloud computing, data and service requests are responded by remote processes calls on huge data server clusters that are not totally trusted. The new computing pattern may cause many potential security threats. This paper explores how to ensure the integrity and correctness of data storage in cloud computing with user’s key pair. In this paper, we aim mainly at constructing of a quick data chunk verifying scheme to maintain data in data center by implementing a balance strategy of cloud computing costs, removing the heavy computing load of clients, and applying an automatic data integrity maintenance method. In our scheme, third party auditor (TPA) is kept in the scheme, for the sake of the client, to periodically check the integrity of data blocks stored in data center. Our scheme supports quick public data integrity verification and chunk redundancy strategy. Compared with the existing scheme, it takes the advantage of ocean data support and high performance.

A new type haptics-based virtual environment system for assembly training of complex products

Virtual reality (VR)-based assembly training has been an interesting topic for the last decades. Generally, there are two shortcomings for nowadays virtual assembly training systems. One is that the operators cannot move around the virtual environment in a natural way as people activity in the real world: they are constrained in a fixed position or can only move in a limited space. The other is that most of the virtual assembly training systems are based on geometry constraint modeling only, which lacks haptics feedback. A new type haptics-based virtual environment system for assembly training of complex products is described in this paper. A new low-cost motion simulator is designed and integrated with the virtual environment to realize free walking by human. An automatic data integration interface is developed to transfer geometry, topology, assembly, and physics information from a computer-aided design system to a VR application, and a hierarchical constraint-based data model is rebuilt to construct the virtual assembly environment. Physics-based modeling and haptics feedback are undertaken to simulate the realistic assembly operations. The application examples and evaluation experiments demonstrate that both motion simulator and haptics have great value for training of assembly processes.

Development of an integrated Cropland and Soil Data Management system for cropping system applications

Most cropping system models and decision support tools are structured for site-specific (i.e. field- or point-based) simulation and analysis. As the need grows for analyses on crop production and management at local, county, state, national, and even global scales, cropping system models and decision support tools are increasingly structured to provide the capability for area-wide simulation and analysis at a range of spatial scales. A major challenge is the development of a data management system that can provide dynamic access to large volumes of geo-referenced data needed by such applications. The objective of this paper is to present a methodology to develop a Cropland and Soil Data Management system that is capable of automatic data consolidation and integration, and can provide dynamic access to the integrated data by cropping system applications. The Cropland Data Management component of the system is based on the Cropland Data Layer (CDL) products from the USDA National Agricultural Statistics Service and is implemented with seven program modules: Data Requester, Data Fetcher, Data Parser, Geodatabase Builder, Map Service Builder, Map Cache Generator, and Cropland Map Viewer. The Soil Data Management component is based on the Soil Survey Geographic (SSURGO) database from the USDA Natural Resources Conservation Service and is implemented with six program modules: Data Requester, Data Fetcher, Data Parser, Database Builder, Soil Map Generator, and Soil Map Viewer. The approaches and methodology presented in the paper can serve as a reference for those who are interested in developing integrated cropping system applications.

Efficient data management in a large-scale epidemiology research project

This article describes the concept of a “Central Data Management” (CDM) and its implementation within the large-scale population-based medical research project “Personalized Medicine”. The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable “Extract Transform Load” (ETL) software for automatic integration of data as well as “electronic Case Report Forms” (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies.

Advancements in web-database applications for rabies surveillance

BackgroundProtection of public health from rabies is informed by the analysis of surveillance data from human and animal populations. In Canada, public health, agricultural and wildlife agencies at the provincial and federal level are responsible for rabies disease control, and this has led to multiple agency-specific data repositories. Aggregation of agency-specific data into one database application would enable more comprehensive data analyses and effective communication among participating agencies. In Québec, RageDB was developed to house surveillance data for the raccoon rabies variant, representing the next generation in web-based database applications that provide a key resource for the protection of public health.ResultsRageDB incorporates data from, and grants access to, all agencies responsible for the surveillance of raccoon rabies in Québec. Technological advancements of RageDB to rabies surveillance databases include 1) automatic integration of multi-agency data and diagnostic results on a daily basis; 2) a web-based data editing interface that enables authorized users to add, edit and extract data; and 3) an interactive dashboard to help visualize data simply and efficiently, in table, chart, and cartographic formats. Furthermore, RageDB stores data from citizens who voluntarily report sightings of rabies suspect animals. We also discuss how sightings data can indicate public perception to the risk of racoon rabies and thus aid in directing the allocation of disease control resources for protecting public health.ConclusionsRageDB provides an example in the evolution of spatio-temporal database applications for the storage, analysis and communication of disease surveillance data. The database was fast and inexpensive to develop by using open-source technologies, simple and efficient design strategies, and shared web hosting. The database increases communication among agencies collaborating to protect human health from raccoon rabies. Furthermore, health agencies have real-time access to a wide assortment of data documenting new developments in the raccoon rabies epidemic and this enables a more timely and appropriate response.

Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

BackgroundExtensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type.ResultsSince entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2.ConclusionsIn order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.

ObjectRunner

We present in this paper ObjectRunner, a system for extracting, integrating and querying structured data from the Web. Our system harvests real-world items from template-based HTML pages (the so-called structured Web). It illustrates a two-phase querying of the Web, in which an intentional description of the targeted data is first provided, in a flexible and widely applicable manner. ObjectRunner follows then a lightweight, best-effort approach, leveraging both the input description and the source structure. This process is domain-independent, in the sense that it applies to any relation, either flat or nested, describing real-world items. We advocate via our prototype that fully automatic extraction and integration of structured data can be done fast and effectively, when the redundancy of the Web meets knowledge over the to-be-extracted data. We present the technical details and the overall platform through several application scenarios on real-life Web sources.

Improving data management and dissemination in web based information systems by semantic enrichment of descriptive data aspects

The German–Vietnamese water-related information system for the Mekong Delta (WISDOM) project supports business processes in Integrated Water Resources Management in Vietnam. Multiple disciplines bring together earth and ground based observation themes, such as environmental monitoring, water management, demographics, economy, information technology, and infrastructural systems. This paper introduces the components of the web-based WISDOM system including data, logic and presentation tier. It focuses on the data models upon which the database management system is built, including techniques for tagging or linking metadata with the stored information. The model also uses ordered groupings of spatial, thematic and temporal reference objects to semantically tag datasets to enable fast data retrieval, such as finding all data in a specific administrative unit belonging to a specific theme. A spatial database extension is employed by the PostgreSQL database. This object-oriented database was chosen over a relational database to tag spatial objects to tabular data, improving the retrieval of census and observational data at regional, provincial, and local areas. While the spatial database hinders processing raster data, a “work-around” was built into WISDOM to permit efficient management of both raster and vector data. The data model also incorporates styling aspects of the spatial datasets through styled layer descriptions (SLD) and web mapping service (WMS) layer specifications, allowing retrieval of rendered maps. Metadata elements of the spatial data are based on the ISO19115 standard. XML structured information of the SLD and metadata are stored in an XML database. The data models and the data management system are robust for managing the large quantity of spatial objects, sensor observations, census and document data. The operational WISDOM information system prototype contains modules for data management, automatic data integration, and web services for data retrieval, analysis, and distribution. The graphical user interfaces facilitate metadata cataloguing, data warehousing, web sensor data analysis and thematic mapping.

Generational analysis of tension and entropy in data structures: impact on automatic data integration and on the semantic web

The move toward automatic data integration from autonomous and heterogeneous sources is viewed as a transition from a closed to an open system, which is in essence an adaptive information processing system. Data definition languages from various computing eras spanning almost 50 years to date are examined, assessing if they have moved from closed systems to open systems paradigm. The study proves that contemporary data definition languages are indistinguishable from older ones using measurements of Variety, Tension and Entropy, three characteristics of complex adaptive systems (CAS). The conclusion is that even contemporary data definition languages designed for such integration exhibit closed systems characteristics along with open systems aspirations only. Plenty of good will is insufficient to make them more suitable for automatic data integration than their oldest predecessors. A previous report and these new findings set the stage for the development and proposal of a mathematically sound data definition language based on CAS, thus potentially making it better suited for automatic data integration from autonomous heterogeneous sources.

Development and evaluation of a compartmental picture archiving and communications system model for integration and visualization of multidisciplinary biomedical data to facilitate student learning in an integrative health clinic

Information technology (IT) has the potential to improve the clinical learning environment. The extent to which IT enhances or detracts from healthcare professionals’ role performance can be expected to affect both student learning and patient outcomes. This study evaluated nursing students’ satisfaction with a novel compartmental Picture Archiving and Communications System (PACS) for the automatic object-oriented integration and visualization of heterogeneous biomedical data. The compartmental PACS was specially designed to support client assessment and clinical education in the integrative health clinic of a university, which is run by a multidisciplinary service team. The sample was 63 nursing students, who were asked to complete a series of realistic tasks using the compartmental PACS. Upon completing the tasks, the Computer System Usability Questionnaire (CSUQ) was administered to assess their satisfaction with the system. Results from data analysis showed that nursing students who completed the evaluation had a satisfactory experience with the system. The Information Quality subscale mean was the highest mean of the CSUQ subscales. This is an important finding as the multidisciplinary data visualization feature of the system provides a technology-enhanced learning environment that can support nursing students’ efforts to both organize and represent knowledge. Through the compartmental PACS, students are assisted in connecting relevant knowledge via various representations of medical data for the clinical conditions under study.