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Sharing and reusing anatomical models over the Web offers a significant opportunity to progress the investigation of cardiovascular diseases. CellML and PMR have gained popularity in the biomedical community; however, to date the central focus has been on cellular models. To date, anatomical models of the heart have been shared through a conventional Web approach, i.e. In this paper, we aim to overcome the limitations discussed above with the introduction of euHeartDB (Gianni et al. The limitations of static delivery of anatomic data and of the disaggregated view present in the current sharing methodology of anatomical models of the heart have motivated new needs in the cardiovascular community. One strategy to address the foundational need in the context outlined is through the introduction of a community supported Web-enabled database of anatomical models. After having identified the needs that motivated the introduction of euHeartDB, we derive the requirements that the databasing system must satisfy to meet the community's expectations fully (see traceability matrix in table 1). The functional requirements are divided into those for model users and those for model owners. Decentralized control of the database content means that users can autonomously access the database to upload, search and download their models. The universal accessibility offered by the Web also implies that non-authenticated and non-authorized Web users could access the interface services or could sample the database content when transmitted over the network.
Finally, integration or coupling with established VPH tools and technologies, such as CellML and PMR, must be implemented in order to effectively contribute to the implementation of the VPH project. The requirements identified in the above section have driven the implementation of the current euHeartDB. The authors menu redirects to a Web page offering the functions: insert a new author, search for an existing one and list all authors. Similar to the authors menu, the bibliographic references menu offers three options: insert bibliographic reference, search for existing ones and list all publications. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Christian IhleSAP-Projekte_Oesterreich.ppt Folie: 12 ARIS Wie sieht der Zusammenhang zwischen Aufgaben, Organisationseinheiten und Medien aus?
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Tech TimesThe news comes from the New York Times (NYT), which reports that the Chinese government is shutting down phone service for Xinjiang residents who download and use software that enables them to bypass Internet filters. Free Online UML Activity Diagram and UML Use Case Diagram Creator - Create Activity and Use Case Diagrams in MS PowerPoint and PNG image formats. Visual Paradigm for UML Community Edition (VP-UML CE) is full feature UML modeling tool plus ERD support.
Edraw is a new UML diagram and software diagram drawing tool for software engineers and designers. QuickUML is a software design tool that tightly integrates a core set of UML models with code generation for several languages. MaintainJ, an Eclipse plug-in, generates runtime UML sequence and class diagrams for a given use case, helping the users to quickly understand a complex Java or J2EE application. MaintainJ logs runtime method execution sequence to a file and uses that trace file to render sequence and class diagrams. EventStudio System Designer is an application that helps you generate Use Case Diagrams, Sequence Diagrams, Process Workflows, Collaboration Diagrams and Message Sequence Charts. SDE for JDeveloper is a full-featured UML modeling tool seamlessly integrated with JDeveloper. However, the current sharing methodology suffers from the limitations of static model delivery (i.e. This scope limitation has neglected cardiovascular anatomical models and atlases, which represent an entire research area (Young & Frangi 2009) and require considerable investments in terms of human resources and scientific equipments to be produced. Section 2 details the demands of the cardiovascular community for a databasing system for anatomical models of the heart and shows how these needs translate into database requirements. These can be summarized as foundational need, control need, monitoring need, communication need and integrative need. Within this framework, the control need requires that delivery of the models is implemented in a dynamic manner that leaves the ultimate control of actions to the model owners.
These requirements can be split into functional and non-functional requirements (Gianni et al.
For model users, these include searching (R1), downloading (R2), commenting on (R3) and curating of models (R4). While searching and downloading are generally decentralized by hosting these resources on a distributed environment, uploading is sometimes administered centrally by individuals charged with managing the database data-entry phase. This should be supported by semantic-based criteria, such as a specified structural part of the heart for instance, in order to ensure more effective model retrieval. The database requires that the users are authenticated before being allowed to access the repository content.
The insert function creates a database record in which all the details of the author, such as name, email and affiliation, are contained. Um die Prasentation herunterzuladen, empfehlen Sie diese Ihren Freunden uber beliebiges soziales Netzwerk. Einsatz von SAP HR in der Personaladmini- stration fur Aktive und Pensionisten per 2005 Aufbau eines MIS (HV, PM, BKLR, sonst. SDE supports full software development life-cycle - analysis, design, implementation, testing and deployment. SDE supports full software development life-cycle -analysis, design, coding, testing and deployment. A conceptual data model is developed based on the data requirements for the application that is being developed, perhaps in the context of an activity model. 2008) projects are international efforts to develop multi-scale models of eukaryotic and human physiology, respectively.


Cellular models published in research papers are coded in CellML format and shared through PMR, thus making them instantaneously accessible to the entire research community.
Achieving an economy of scale in the production and use of anatomical models now requires that these research products also be efficiently shared among a wider audience in the cardiovascular community. The database implements a dynamic delivery of the models by securing access to the data and by tracing the use of the models.
Section 3 illustrates how these requirements are implemented, including a description of the main characteristics of the current database content and of the coupling with PMR.
The foundational need represents the essential demand of sharing and reusing anatomical models to progress research in cardiovascular diseases.
The monitoring need can similarly be addressed with the adoption of logging capabilities on the actions taken.
In addition, for model owners, the requirements are uploading (R5), activity monitoring (e.g. In particular, the interface access must be restricted solely to authenticated and authorized users, and all the database content must be transmitted over encrypted connections. Interested users can apply for an account through the available Web form, and once enabled, they are allowed to log onto the system and to visualize the main menu and content pages over encrypted connections. The search and the list all authors functions display the resulting sequence of names, each of which can be clicked to reach the author's page containing all the associated data. This page contains the details of a publication and the references to the anatomical models the publication describes or cites. The data model will normally consist of entity types, attributes, relationships, integrity rules, and the definitions of those objects.
The control need reflects the model owners' desire to maintain full control of their models. The communication need requires collaborative technologies that promote discussion and the exchange of ideas on a specific anatomical model. For example, the foundational need is satisfied with the implementation of functional requirements R1, R2, R4 and R5, and non-functional requirements R9, R10, R12, R13, R14 and R15.
This decentralized control, however, implicitly requires that users are able to connect to the databasing system independently from their computer platforms.
Within the VPH, the FieldML markup language is currently being developed to meet this need of describing models that include spatial information (Christie et al. This will provide both researchers using and developing cellular models and researchers using and developing anatomical models with an integrative view of the applications of the respective models spanning the related research areas.
In addition to the user-typed data, this page displays links to the author's publications and anatomical models available within euHeartDB.
A publication home page can be reached by browsing the search results, by navigating the list of all the bibliographic references or by accessing the pages of the anatomical models menu. EventStudio analyzes the model for design errors and automatically generates use case and sequence diagrams for multiple scenarios.
The database implements a dynamic sharing methodology by managing data access and by tracing all applications. 1997), one of the most reused computer models of the cardiac ventricles, has been available through the website of the Auckland Bioengineering Institute (ABI) for many years. Similarly, the monitoring need meets the expectation of both model owners and users in providing an overview on the ongoing activities using the models.
Finally, the integrative need, which partially overlaps with the guidelines of the VPH road map (Fenner et al.
In technical terms, this requires the use of Web technologies, which overcome platform heterogeneity and location issues. The data requirements are recorded as a conceptual data model with associated data definitions.
As a consequence, considerable time is required to discover current and past applications of the Auckland model. By taking such an approach, euHeartDB establishes a knowledge link, and thus provides an integrative view of the anatomical models and of the related applications within simulation contexts. In addition to this, the communication need identifies the request for a virtual discussion forum covering the exchange of ideas and results. 2008), requires the establishment of a knowledge link among all the parties and on all the data involved in the development and use of the anatomical models. The goal is for FieldML files to contain all the parameters needed to mathematically specify spatial fields. This step consists of browsing of the foundational model of anatomy (FMA) ontology (Rosse & Mejino 2003) to semantically classify the model being uploaded. Similarly, PMR includes graphical diagrams, representing the model structure, and links to publications describing and citing the models.
Finally, the integrative need highlights the importance of providing researchers with a comprehensive view of information related to the models, such as typical applications, publications describing and citing models, and related models in adjacent subdomains.
These functional requirements demand minimal description and, therefore, are not discussed further. The most common form of parameterization is a finite-element mesh, where the parameters are nodal parameters, element topology and the element-basis functions that interpolate the nodal parameters over the elements.
The browsing starts with the heart as root concept, and continues with the exploration of the heart subparts, depending on the user selections. Conversely, the non-functional requirements have to be addressed in conjunction with the functional requirements and deserve more attention. However, FieldML is intended to handle more general parameterizations than just finite-element fields and also includes dense data formats (e.g. Data modeling defines not just data elements, but their structures and relationships between them [2] Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource.
In this case, the left side of the heart and the cavity of left atrium concepts are associated with the model being loaded into euHeartDB. Data models are progressive; there is no such thing as the final data model for a business or application.


Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. 2002a,b) within the Auckland geometry to obtain whole organ representations of the pump cycle. If the same data structures are used to store and access data then different applications can share data.
However, systems and interfaces often cost more than they should, to build, operate, and maintain. A major cause is that the quality of the data models implemented in systems and interfaces is poor.[1]Business rules, specific to how things are done in a particular place, are often fixed in the structure of a data model. This means that small changes in the way business is conducted lead to large changes in computer systems and interfaces.Entity types are often not identified, or incorrectly identified. This can lead to replication of data, data structure, and functionality, together with the attendant costs of that duplication in development and maintenance.Data models for different systems are arbitrarily different.
The result of this is that complex interfaces are required between systems that share data.
These interfaces can account for between 25-70% of the cost of current systems.Data cannot be shared electronically with customers and suppliers, because the structure and meaning of data has not been standardised. This consists of entity classes, representing kinds of things of significance in the domain, and relationships assertions about associations between pairs of entity classes. A conceptual schema specifies the kinds of facts or propositions that can be expressed using the model. This is concerned with partitions, CPUs, tablespaces, and the like.The significance of this approach, according to ANSI, is that it allows the three perspectives to be relatively independent of each other. Storage technology can change without affecting either the logical or the conceptual model.
Early phases of many software development projects emphasize the design of a conceptual data model. It complements business process modeling, which results in application programs to support the business processes.[5]The actual database design is the process of producing a detailed data model of a database.
This logical data model contains all the needed logical and physical design choices and physical storage parameters needed to generate a design in a Data Definition Language, which can then be used to create a database.
The term database design can be used to describe many different parts of the design of an overall database system. Principally, and most correctly, it can be thought of as the logical design of the base data structures used to store the data.
In an Object database the entities and relationships map directly to object classes and named relationships.
However, the term database design could also be used to apply to the overall process of designing, not just the base data structures, but also the forms and queries used as part of the overall database application within the Database Management System or DBMS.In the process system interfaces account for 25% to 70% of the development and support costs of current systems.
If data models are developed on a system by system basis, then not only is the same analysis repeated in overlapping areas, but further analysis must be performed to create the interfaces between them. While there are many ways to create data models, according to Len Silverston (1997)[6] only two modeling methodologies stand out, top-down and bottom-up:Bottom-up models are often the result of a reengineering effort.
They usually start with existing data structures forms, fields on application screens, or reports. These models are usually physical, application-specific, and incomplete from an enterprise perspective.
They may not promote data sharing, especially if they are built without reference to other parts of the organization.[6]Top-down logical data models, on the other hand, are created in an abstract way by getting information from people who know the subject area. Unfortunately, in many environments the distinction between a logical data model and a physical data model is blurred. The constraints are expressed as sentences in the formal theory of the meta model.[7]There are several notations for data modeling. The actual model is frequently called "Entity relationship model", because it depicts data in terms of the entities and relationships described in the data.[3] An entity-relationship model (ERM) is an abstract conceptual representation of structured data. Entity-relationship modeling is a relational schema database modeling method, used in software engineering to produce a type of conceptual data model (or semantic data model) of a system, often a relational database, and its requirements in a top-down fashion.These models are being used in the first stage of information system design during the requirements analysis to describe information needs or the type of information that is to be stored in a database.
While these methodologies guide data modelers in their work, two different people using the same methodology will often come up with very different results. They define standardized general relation types, together with the kinds of things that may be related by such a relation type. For example, a generic data model may define relation types such as a 'classification relation', being a binary relation between an individual thing and a kind of thing (a class) and a 'part-whole relation', being a binary relation between two things, one with the role of part, the other with the role of whole, regardless the kind of things that are related.Given an extensible list of classes, this allows the classification of any individual thing and to specify part-whole relations for any individual object. By standardization of an extensible list of relation types, a generic data model enables the expression of an unlimited number of kinds of facts and will approach the capabilities of natural languages.
Conventional data models, on the other hand, have a fixed and limited domain scope, because the instantiation (usage) of such a model only allows expressions of kinds of facts that are predefined in the model.Semantic data modelingThe logical data structure of a DBMS, whether hierarchical, network, or relational, cannot totally satisfy the requirements for a conceptual definition of data because it is limited in scope and biased toward the implementation strategy employed by the DBMS. That is, techniques to define the meaning of data within the context of its interrelationships with other data.
As illustrated in the figure the real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Creating a strategic plan for configuration management using Computer Aided Software Engineering (CASE) tools.
Accessed 1 Nov 2008.^ a b c d FIPS Publication 184 released of IDEF1X by the Computer Systems Laboratory of the National Institute of Standards and Technology (NIST).




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