Displaying items by tag: data model

Data Models generated by reverse engineering an existing Database in Enterprise Architect can be shared dynamically with Prolaborate web solution. This can be useful to provide access to an existing Physical Data Model or "work in progress" database redesign views, and gather feedback via Prolaborate collaborative features (discussions, reviews, etc.).

Sparx Enterprise Architect (EA) data models support tables and columns, views, stored procedures, functions from all major DBMS (SQL server, MySQL, Postgres, etc.). This is achieved with built-in stereotyped UML classes, attributes and operations.

When such information is shared outside Enterprise Architect tool, it is important to customize or hide unnecessary details for the target users.

Prolaborate Modeling Language feature is ideal to achieve this via custom profiles and a form designer. Built-in Prolaborate languages are available for UML, BPMN, or Archimate elements (SysML is available on request). If you use your own stereotypes, the MDG XML file used for Enterprise Architect can also be imported in Prolaborate. When it comes to Data Modelling, I found that Prolaborate doesn't currently support this definition. Since data models are built in EA, there isn't any MDG XML file available to manually import. Hence my only option was to recreate a Data Modelling MDG in EA solely for the purpose of configuring Prolaborate web solution.

Below is an illustration of how it works with Sparx EA Example project.

Note: this MDG definition has been submitted to Prolaborate team and will be available to download, before it is included in Prolaborate future releases. In the meantime you can contact me on guillaume[at]umlchannel.com to get a copy. 

Initial context

To illustrate the advantage of using Modelling Languages, the following screenshot shows how the details of a table is rendered in Prolaborate:

The default table element details are displayed with all tagged values. Not only some of these fields may not be relevant, but also note the field Type mentions that it's a Class; where this element is rightly a "table" stereotyped UML class, this information to an end user can be rather confusing!

The aim of installing and configuring the Data Model Modelling Language is to have the ability to manage the visible and hidden information amongst the element fields (e.g. name, notes, author, modified date, etc.) or custom tagged values, and rename their label when needed.

• Important: Prolaborate doesn't currently support customization of attributes and operations from a Modeling Language profile. Hence table columns and constraints cannot currently be customized.

Install the Data Model Modelling Language

• Once logged in, open Prolaborate Repository configuration, and select Modelling Languages

• Fill in the form, select the provided MDG Prolaborate EAUML.xml file, and click on Add Modeling Language

• The Data Model custom language is installed and ready to be configured.

Customize the users' views with profiles

Below is an illustration to customize the fields for a DB table.

• Click on Manage Profiles
• Create a new profile or edit the default one 

• The data model stereotypes are listed e.g. table, column, view, trigger, etc.
  • Note: the eye icon can be used to hide all elements under the selected stereotype to be hidden e.g. views and stored procedures.

• Open the form designer to customize the visible fields for a given stereotype e.g. table.

• The initial configuration is illustrated below.
  • The table name, notes, author and type (e.g. Class) are listed under the Basic Attributes.
  • All three tagged values are listed under EAUML::table Attributes

• These fields can removed, hidden, made read-only, or renamed. Other fields can be added (see the lists on the right hand side).
  • Notes:
    • The element's EA fields (e.g. name, notes, alias, etc.) can be freely mixed with tagged values, within any group.
    • The order can be updated as needed.
•  The example below matches the following configuration:
  • The fields' group titles have been renamed to Main Properties (from Basic Attributes) and Additional Properties (from EAUML::table Attributes).
  • The table name field is read-only and available against the label "Table name".
  • The table notes is visible against the "Description" label, and can be updated if the user has the write permission.
  • The Type field has been removed.
  • The Author field is not visible.
  • Only Tablespace and DBVersion tagged values are visible in read-only mode.

• Important - in theory, a table index constraint can be customised as illustrated here. However Modelling Language profiles are not yet supported by Prolaborate for attributes and operations (all columns and constraints remain visible). This is a possible enhancement for Prolaborate team to consider in a future release.

• The Profile can be saved.

Note: further details are available from Prolaborate online help.

User experience

Accessing the same physical data model, the displayed information matches the new table profile definition:

It is particularly important to measure and manage carbon emissions reduction from project activities. The major objective of the Global Carbon Data program is to establish and communicate accurate monitoring algorithms to quantify emissions reduction.  Carbon abatement projects can then qualify for carbon offsets/credits using these standard methodologies.

The program is to offer a centre of excellence for those organisations who want to engage in testbeds for specific project types, facilitating knowledge and technology sharing of climate change and greenhouse gas emissions reduction. Data can be published to reach governments, NGOs and the corporate sector through a climate change specific cloud hosted knowledge base.     

The implementation of Global Carbon Data is being developed around a UML model that provides a data model for Energy and Carbon Emissions generated using MDA transformation from classes to data tables.

Providing for requirements and scenario traceability using UML modelling technology means that resources and efforts can be readily managed and ­monitored during the staged development phases of delivering information about energy and carbon emissions monitoring.

An architecture  model led approach provides for managing the hybrid private cloud deployment using model-led metadata for both Enterprise Planning and Business Process Automation.

About Global Carbon Data

The program aims to enable climate change and carbon emissions research and development, knowledge and expertise in two main ways.

     To establish an easily accessible and relevant global knowledge base of research, reports and data, using software that is able to auto classify information based on context, content and commonality of usage.

     To facilitate and monitor data from the establishment of pilot projects to collect, accurate test data to be used as the basis for emissions reduction metrics. The aim is to facilitate the certification of emissions reduction methodologies resulting in carbon credits/offsets able to be traded in global carbon markets.

Global Carbon Data online is to share and communicate structured data (carbon metrics, analytics and algorithms) as well as research, reports, and analysis of project activities that effect a net reduction in greenhouse gas emissions. High quality research and reporting provided by international organisations engaged in addressing climate change can be published through the site.

Being able to view the management of the project resources and scheduling directly from a task perspective is useful, particularly if the production of data is also linked directly to delivery of the capabilities that facilitate the business scenarios.

Data is becoming increasingly important, in view of advances in cloud security, big data analytics and the proliferation of information gathered from IIOT devices.

To work with a development team that is located in different locations, it is very useful to be able to share the entire architecture specification from a single model. This not only facilitates the focus on delivery, it provides for ready peer review and the cross pollination of knowledge and technologies.

Figure 1: Energy and Carbon Emissions Monitoring Model

This approach reflects through to the delivery of the information to the platform subscribers.  The focus on data delivery promotes information services delivery not only to the system builders, but the system users as well. Collaboration is a simple matter of providing project data, expert knowledge and information in context, and making it readily accessible.

Cloud technology is available to facilitate knowledge sharing across organisations, national and regional boundaries. Connecting people, projects and research ensures the rapid transfer of data over mobile, public and private networks.

Curated Access to Information

The program aims to facilitate organisations seeking information about climate change by providing ready access to information about greenhouse gas emissions reduction from credible sources.

Subscribers can access published reports, research and data by project category, research topic, using simple search terms The knowledge base is to provide highly focused, context specific information via a specially developed climate change taxonomic search engine.

Taxonomic search can return research, reports, data and project analysis, ranked by relevance, regardless of whether the terminology used is for precipitation or rainfall, greenhouse gas emissions reduction or carbon abatement, agriculture for hot dry climate or drought tolerant crops.. By making the information context sensitive, the online search results are far more focused than standard text search mechanisms.

Consumers of information, such as government agencies and corporations seeking to reduce emissions can use a guided process to look up relevant reports, data, research, and current thinking for strategic responses to climate change.

The Global Carbon Data program is to publish a knowledge base of climate change and carbon emissions research and data held by leading organisations. Governments and companies wishing to access the data can do so on a fee for service basis.

The key to enabling a global carbon emissions knowledge base of structured and unstructured data sharing, essential to monitor emissions across national borders and regional boundaries, is of course a common logical data model. This is also the prime enabler for a global stable price on carbon.

Data for Carbon Markets

The program is to facilitate collaboration across organisations to establish relevant, repeatable emissions reductions methodologies for a specific emissions reduction project types. Examples include:

     The collection of data from renewable energy micro grid electricity generation, to which emissions factor algorithms can be applied to produce accurate estimates of greenhouse gas reductions from replacing existing fossil fuel types with renewable energy.

     The collection of data from land management emissions abatement activities such as prevention of deforestation by forest edge cultivation, reafforestation of rainforest, restoration of mangroves in sub-tropical coastline, and soil sequestration of carbon from agriculture innovation.

The data is to be collected, analysed and made available online as required to authorised and authenticated parties using secure networks. Data collected from project activities can be certified by carbon offset standards bodies to earn carbon credits.

By collecting accurate data from a wide range of project activities, the intention is to help facilitate a stable global carbon price, essentially by improving the level of accuracy of emissions reduction data to the burgeoning global carbon markets.

Value added services for the program include the application of accurate, appropriate greenhouse gas emissions reduction algorithms to project activities and online access to analysis and infographics and time series and location based data analysis .

In the rapidly changing world of aviation, standards are the measure by which successful transition from the norm to the new can be made. Over 60 years, IATA has developed the commercial standards that built a global industry and its mission is to represent, lead and serve the airline industry. With a membership made up of 240 airlines, among them the world's leading passenger and cargo airlines, IATA represents 84% of total air traffic.

Passenger numbers topped the 3 billion mark in 2013 for the first time, with the value of goods carried as air cargo totalling one third of world trade.
Passenger and Airport Data Interchange Standards Board - or PADIS is a set of working groups responsible for the maintenance of technical specifications such as XML schemas in support of various business standards maintained by IT experts who can turn business standards into precise technical specifications that systems need to communicate.

It is against this industry backdrop that IATA has chosen Enterprise Architect to build the Aviation Industry Data model. The data model will enable interoperability across the entire spectrum of services providers and agents, who work with airlines to provide a seamless travel experience.

For more information, please read the official press release.

The Future Airborne Capability Environment (FACE™) Consortium is an aviation focused professional group managed by the Open Group. Recently Sparx Systems contributed a collection of licenses to assist members in their development of the FACE Data Model. The Open Group, via its latest news release and the FACE website thanked Sparx Systems for their assistance:

"[Enterprise Architect has] been instrumental towards the development of the FACE Data Model and DoDAF 2.0 representations of FACE Enterprise Architecture products. In addition we appreciate their assistance in setting up a shared working environment for these efforts."