A Data Structure Model Supporting Railway Distributed System Integration

BackgroundRobust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.MethodsA dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.ResultsThe results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.ConclusionsBased on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.

Object-oriented data model for scanning probe microscopy image processing

BackgroundThe volume and complexity of patient data – especially in personalised medicine – is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare.MethodsSemantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don’t provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository.ResultsA web-based tool called ODMedit (https://odmeditor.uni-muenster.de/) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal.ConclusionUniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository.

A bi-level object-oriented data model together with a user query language called OFQL is designed which can support applications like GIS (geographic information systems). The data model is divided into two layers, the higher-level data model and the lower-level data model. The higher-level data model or the geographic object data model primarily consists of the geographic objects and a set of semantic spatial functions through which the topological relationships of the geographic objects are defined or derived. The lower-level data model or the geometric object data model has geometric objects which are the actual spatial representations of the geographic objects in the higher-level data model. It also has a set of geometric functions that retrieve, manipulate, and compute for geometric objects. The general architecture of a GIS system using this data modeling approach consists of two modules: the query processor and the function implementor. With the OFQL user-interface, the user is able to pose queries of a geographic nature without knowing the details of the spatial representation and computation of the geographic objects.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.

Today, one of the state-of-the-art technologies that have shown its importance towards data integration and analysis is the linked open data (LOD) systems or applications. LOD constitute of machine-readable resources or mechanisms that are useful in describing data properties. However, one of the issues with the existing systems or data models is the need for not just representing the derived information (data) in formats that can be easily understood by humans, but also creating systems that are able to process the information that they contain or support. Technically, the main mechanisms for developing the data or information processing systems are the aspects of aggregating or computing the metadata descriptions for the various process elements. This is due to the fact that there has been more than ever an increasing need for a more generalized and standard definition of data (or information) to create systems capable of providing understandable formats for the different data types and sources. To this effect, this chapter proposes a semantic-based linked open data framework (SBLODF) that integrates the different elements (entities) within information systems or models with semantics (metadata descriptions) to produce explicit and implicit information based on users’ search or queries. In essence, this work introduces a machine-readable and machine-understandable system that proves to be useful for encoding knowledge about different process domains, as well as provides the discovered information (knowledge) at a more conceptual level.

To deal with issues of data model transformation and to make combined use of different data models, this paper proposed a database reengineering scheme which includes schema translation and data conversion from object-oriented data model to flattened XML data model with is-a and cardinality data semantics preservation. In this paper, conceptual schema of object-oriented data model and flattened XML data model are elaborated by UML class diagram and XML Schema Definition(XSD) graph respectively. Logical schema is described by UNISQL class definition and XSD respectively. This paper firstly analyzes class definitions from OODB and sorts them from most independent classes to most dependent classes. Secondly schema translation is processed by mapping class to complexType and then creating element definitions in XSD file. Thirdly data conversion is processed automatically. Performance tests have shown that the proposed database reengineering scheme is reliable and efficient.

Engineering enterprise is a complex system and the data of it is large and complex, especially the relationship between them is complicated. Therefore, to establish a complete set of data model which covers the full life cycle and sum up a set of means of data preparation which can be applied to enterprises are the key points to implement enterprise information system. This paper indicates how to prepare the data and gives proposals of data collection, analysis and collation, how to build the implementation of templates and examples and how to establish ways and means of implementation. Keywords-engineering company; enterprises informatization; data integration and model

In the field of environmental management, inefficient piecemeal sectorial approaches are still widespread, particularly in water resources management, where concerns and decisions are usually split among a set of rather weakly linked stakeholders. Planning and management problems to be addressed are in most cases complex and need to be tackled in a global, integrated and holistic way, including from the different areas of science and knowledge from the various stakeholders. Therefore, the sharing and integration of data, knowledge and models has become a major issue. A lot of effort has been made in recent years to develop such integrative tools, including systems (IS), decision support systems (DSS) or integrated modelling frameworks (IMF). Although these tools achieved great progress in terms of linkages, there is currently no environment providing a full integration of the various kinds of data, models, and their visualisation. This is the general issue tackled by the present thesis, with developments based on a semantic, systemic formalism, and applications focusing on water management issues. The first development step taken was the creation of a systemic description of water management. Albeit the various aspects of water management are abundantly documented and well-known, an integrated description including interconnections was lacking. Therefore, a graphic representation of interconnected elements annotated with definitions was created. This innovative result constitutes an ontology (a formal representation of a knowledge domain) providing a robust reference and a fairly exhaustive reminder of the complexity of the water management field, including transdisciplinary aspects. In the second step, a data model was created, based on the new SYSMOD language. The latter proposes a new graphical language uniting the ontological and systemic approaches, with a strong emphasis on interactions. Whereas existing data models are generally specific to a field or designed for a target application, this new data model is generic and transdisciplinary. It can handle any kind of systemically organised data, which potentially includes any sort of environmental data. To visualise and manage the systems-based, generic data stored within the database (created on the basis of the generic data model) a new kind of visualisation was developed, proposing diagrams of interconnected nodes. Whereas existing systems are often data warehouses without a user-friendly graphical user interface, or geographic systems focussing on spatial data, this new diagram visualisation proposes views including spatial and non-spatial elements (such as lakes, power plants, stakeholders or laws) interconnected by interactions (such as fluxes or influences). Bound to the database, the tool was called an information on the system (ISS). It provides a way of graphically managing and integrating the systemically organised, generic, transdisciplinary data. Furthermore, the systems approach fosters the apprehension and communication of complex situations in a holistic way (involving many different elements, influences and interactions from various fields), through their representation into thematic views. In complement to the diagram visualisation, a reporting tool was developed to display the data linked to elements (numeric values, comments, files, etc.) in a web-browser fashion. To interoperate with external models, another module was developed on the top of the ISS. Finally, a fully-fledged general user interface was created to integrate all the previous developments into a single environment. To visualise the different facets of elements, a geographic and a chart view were added, in complement to the reporting view. This final tool was called the generic decision support (GenDSS), as its application to given situations can give rise to a series of views and linkages to models, thereby providing specific DSS functionalities for these situations. The successive developments were tested in Birmingham (United Kingdom). Test results indicated that the GenDSS is currently limited to managing a few hundred elements; beyond this quantity, the loading times and the user interface reactivity can become long and unresponsive. On the other hand, the application also demonstrated the strengths of the GenDSS which provides an innovative integrated solution to centralise transdisciplinary data in various formats (e.g. numeric values, texts, geometries, files), to holistically describe situations using complementary visualisations, and to test scenarios using externally-linked models. Thus, the GenDSS may prove an adequately versatile solution for large institutions or enterprises dealing with complex situations, heterogeneous and transdisciplinary data, and where requirements are continuously evolving, such as: a car manufacturing corporation (which would involve several different production and research sites in different countries with different regulations), a hospital, or a strategic environmental planning network (where the tool could help to break the transdisciplinary, sectorial barriers). In the case of water, the GenDSS could prove very useful to achieve a truly integrated water management approach.

The Shipborne combat system (SCS) includes many application systems. The integration of application systems is necessary because they are distributed and need to complete combat tasks together. For such integration, an integration model needs to be developed. Firstly, the integration framework of application systems of SCS is introduced, which involves three layers: data integration, function integration and workflow integration. Secondly, integration metamodel based on the framework is discussed. Data, application system, function, component, workflow and their relationships are explained in detail. Finally, a system integration modeling language for application system design (SIML4ASD) based on the metamodel is defined by extending UML using profile mechanism. A case study of anti-submarine combat is given to explain the use of SIML4ASD. Since it is an extension of UML, SIML4ASD is easy to learn, understand and use for the system designers and can be used to describe the integration model clearly and exactly in multi-view.

Developing a three-dimensional (3D) data model for Geographic Information Systems (GIS) is an essential and complex issue. 3D modelling in GIS is becoming ever more important for the development of cyber cities and digital earth, which have recently become feasible. A competent 3D model forms an efficient foundation for 3D visualization, query and spatial analysis. As a development of the existing 3D models, this study proposes particular improvements in handling complex 3D objects. We present an object-oriented data model for handling complex 3D objects in GIS. First, the conceptual data model is developed based on the principle of object-oriented (OO) data modelling. This model is designed based on the following three basic geometric elements: node, segment and triangle. Accordingly, the abstract geometric objects are defined: including points, lines, surfaces and volumes. Second, the corresponding 3D logical model is designed based on the defined abstract objects and the relationships between them. Third, a formal representation of the 3D spatial objects is described in detail. Fourth, a prototype 3D GIS is developed based on the proposed 3D data model. Finally, we describe the results of an experimental study to reconstruct 3D objects using this 3D GIS and a comparison with the performance of other 3D data models. The proposed model is able to handle complex objects, such as complex buildings and TV towers, which is an essential functionality for building large-scale cyber cities, such as for Hong Kong. The proposed data model proves to be very efficient, particularly in visualization and rendering. The experimental results show which the data volume of the proposed model is compacted and the visualization speed for 3D objects is improved, compared with the existing models.

A data model for moving objects is a blueprint that describes data of interest related to movements of objects and a set of operations represented in an information or database management system. Designing an appropriate data model fosters conceptual understanding of the dynamic phenomena of moving objects, yields efficient data storage and retrieval, and further facilitates spatiotemporal analytical processes. Various data models have been proposed to efficiently and effectively store, manage, query, and analyze data about moving objects. This entry starts with the description of fundamental elements of moving objects and their abstract data types. Then it reviews data modeling approaches for representing moving objects, including conventional spatiotemporal data model, constraint model, moving object data model, semantic trajectory data model, event‐based data model, and models for handling big moving object data.

An object wrapper [3,4] is a piece of middleware that provides an object view on top of a legacy database management system. But legacy database management systems such as relational database systems (RDS) and multidimensional database systems (MDS) [5,6,7] are different in many aspects from an Object-oriented Data Model. Among these aspects are semantic management, data structures and data types supported in these models. As a result, it is crucial to understand these data models in order to design and implement an efficient piece of middleware. In this paper, a short discussion of the real-world data modelling requirements is presented. Then, this is followed by a discussion on an Object View’s requirements. The third section of the paper presents the architecture of the Object-to-Multidimensional Mapping Engine (O-MME) [10,11,12,13,14,15,16,17,18,19]. A detail discussion on major interfaces and classes in the Object-to-Multidimensional mapping algorithm are presented next. Issues such as data type conversion and database language statements generation are discussed in this paper also. Finally, some performance issues are presented and future possible researches are outlined.KeywordsStructure Query LanguageMapping EngineCache ManagerBusiness ClassisRelational Database SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

SummaryIntroduction:This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers.Objectives:The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing.Governance and Policies:Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change.Architectural Framework and Methodology:The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing.Use Cases:From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagnosis, tailored therapies and therapy decision tools with focuses on neurology, oncology and further disease entities. Our early uses cases will serve as blueprints for the following ones, verifying that the infrastructure developed by DIFUTURE is able to support a variety of application scenarios.Discussion:Own previous work, the use of internationally successful open source systems and a state-of-the-art software architecture are cornerstones of our approach. In the conceptual phase of the initiative, we have already prototypically implemented and tested the most important components of our architecture.

A lot of scientific data sources currently are concentrated within data infrastructures supporting the whole cycle of data processing and management. Heterogeneous data are represented using various data models from the relational model to various NoSQL models. That is why data integration issues within data infrastructures become more and more important. This paper is devoted to the implementation of data model unification techniques considered as a formal basis for data interoperability, integration, and reuse within data infrastructures. The implementation applies a model-driven engineering approach, namely, formal methods allowing us to define data model syntax and semantic transformations of one data model to the other. To emphasize the advantages of the model-driven approach MDE-based implementation is compared with previous implementation utilizing metacompilation and term rewriting techniques.