The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform
James Bean - One of the best experts on this subject based on the ideXlab platform.
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W3C XML Schema Data Type Facets
XML for Data Architects, 2020Co-Authors: James BeanAbstract:This chapter describes W3C XML schema Data facets that can be applied to a Data type. Data Architects quickly recognize that facets provide extended Data type support. Commonly applied facets include length, value limits, decimal digits, enumeration, patterns, and white space. The most familiar facets are those that support lengths and fractional decimal digits. W3C XML Schemas provide extensive support for facets. Depending upon the Data type to which they are applied and the context in which they are used, facets can also be combined. The responsibility for Data types and their constraining facets should be fundamental to the role of the Data Architect. The Data Architect is also responsible for defining Data elements, assembling them into groups, and specifying relationships among groups.
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Architectural Container Forms
XML for Data Architects, 2020Co-Authors: James BeanAbstract:Patterns applied to eXtensible Markup Language (XML) and other information technologies have similarities to patterns applied to the Architecture of a building. With the application of XML, Architectural container forms present a similar analogy. There are three basic Architectural container forms that can be applied to an XML document—rigid, abstract, and hybrid. Reuse presents one of the greatest potential benefits to application development. MetaData reuse and the proliferation of metaData-based standards present tremendous opportunities for the Data Architect. The chapter concludes that there are numerous similarities and synergies between traditional Data modeling and XML structure modeling. Additionally, observable and repeatable Data patterns can be applied with the application of Architectural container forms. These activities fit well with the role and responsibilities of the Data Architect.
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The Importance of Naming Standards (Taxonomy)
XML for Data Architects, 2020Co-Authors: James BeanAbstract:Publisher Summary This chapter provides techniques for naming eXtensible Markup Language (XML) element and attribute containers in a manner that aligns with enterprise practices and that leverages the descriptive strengths of XML. A taxonomy is a form of naming or identification. The most common taxonomies also incorporate hierarchical classification and grouping. Names assigned with a specific taxonomy identify a particular object and allow that thing to be classified or grouped with other objects that have similar characteristics. Data Architecture taxonomies classify, identify, and describe the content of a Data element. Strengths of a taxonomy lie in both specificity and abstraction. The Data Architect is generally well versed in taxonomy and naming processes and has a tremendous knowledge of enterprise information assets. Regardless of the level of abstraction, specificity, or other taxonomy processes applied to XML, there is an obvious opportunity for Data Architects to participate and apply their skills.
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Data, the Missing Link
SOA and Web Services Interface Design, 2020Co-Authors: James BeanAbstract:The services, their definitions, and their interactions within the service-oriented Architecture (SOA) can vary. The general notion of a service often appears to be application centric. Data sits at the core of most services and is critical to SOA. While SOA and services are often interpreted as process centric, this is not always the case. Underlying most SOA services are one or more Data sources. This raises the importance of Data in the SOA paradigm and emphasizes the need for Data and metaData in the process of Web service interface design. This emphasis on Data is not limited to traditional Data Architecture. There are two important views of Data to consider in the SOA. One is “Data at rest,” where Data instances and values are persisted in some form of Data repository, and the other is “Data in motion,” as the Data contained in messages that are moved between service participants and defined by the Web service interface. The disciplines for exploiting and optimizing Data at rest sources and structures are supported by the roles of Data modeler, Data administrator, Data Architect, and Database Architect, all of which are all reasonably well-known and accepted. These Data at rest structures are most often supported by some type of Database. The Data sources could be flat files, documents, or almost any other type of Data repository. MetaData plays a critical role in defining not only the characteristics and rules for Data at rest sources but also those of Data in motion as messages.
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design and engineering for the Data Architect
XML for Data Architects#R##N#Designing for Reuse and Integration, 2004Co-Authors: James BeanAbstract:Publisher Summary This chapter proposes a collaborative process in which the design of eXtensible Markup Language (XML) structures and engineering of schemas are a shared responsibility of both the development and metaData communities. The XML design and engineering process incorporates several tasks that borrow from traditional development processes. Initial XML design and engineering tasks include the identification of Data and functional requirements. The activities of identifying, documenting, and validating requirements are fundamentally the same processes used for other application development projects. Depending upon the intended use of the transaction, the contained Data can be persisted in XML form and syntax. It may be more advantageous to leverage the traditional Data capabilities and strengths of a Database that also includes XML support and extensions for extraction of Data values rather than to store the Data as an XML-formatted structure. XML can be validated as a good-fit technology with a criteria checklist or a more rigorous set of evaluation criteria. The most valuable aspect of the XML engineering process is the development of a prototype XML document.
S. Chowdhury - One of the best experts on this subject based on the ideXlab platform.
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HICSS - Best practices in Data warehousing to support business initiatives and needs
37th Annual Hawaii International Conference on System Sciences 2004. Proceedings of the, 2004Co-Authors: J. Lawyer, S. ChowdhuryAbstract:The paper presents the Data warehousing Architecture and practices used at a major U. S. retailing company. Many considerations were assessed when deciding which Data warehousing Architecture to adopt. The paper discusses the two pre-dominant styles in Data warehousing, namely the "Bill Inmon Style" or the top-down approach and the "Ralph Kimball Style "or the bottom-up approach. The company chose the Inmon style due to a unique combination of circumstances in their business and technical environments, which are being discussed in detail. Much of the information presented in this paper is based upon the direct experiences of the lead Data Architect assigned to the projects under which this U. S. retailing company's customer Data warehouse evolved. The Architecture has evolved over time and currently has been accepted at the company as a best practice. It is interesting to mention that both the hardware platform (CPU and disk drives) and relational Database management system (RDBMS) software employed today at this company for Data warehousing is not the same as was selected for the first instantiation. The implication was that the best plan or practice was a flexible one. There were many challenges, like organizational, technical, Data sourcing and Data naming, needed to be solved during the pre-project, initial stages, and throughout the project and beyond. The initial Data warehouse, implemented in 1996, was termed an overall success and approved for expansion. The current Data warehouse Data are being used by over six hundred registered users to fine-tune customer marketing and leverage and share Data in an enterprise manner. The Data warehouse has allowed the company to strengthen customer relationship management (CRM) core capabilities and business partnerships. Today, there are many departments benefiting from queries and requests for Data warehouse Data, many anticipated, some not. Although not planned, the Data warehouse has been a valuable source of purchase and customer Data in case of a manufacturer recall of merchandise. Above all, the company has been able to leverage and share enterprise customer Data to the benefit of the entire company.
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Best practices in Data warehousing to support business initiatives and needs
37th Annual Hawaii International Conference on System Sciences 2004. Proceedings of the, 2004Co-Authors: J. Lawyer, S. ChowdhuryAbstract:The paper presents the Data warehousing Architecture and practices used at a major U. S. retailing company. Many considerations were assessed when deciding which Data warehousing Architecture to adopt. The paper discusses the two pre-dominant styles in Data warehousing, namely the "Bill Inmon Style" or the top-down approach and the "Ralph Kimball Style "or the bottom-up approach. The company chose the Inmon style due to a unique combination of circumstances in their business and technical environments, which are being discussed in detail. Much of the information presented in this paper is based upon the direct experiences of the lead Data Architect assigned to the projects under which this U. S. retailing company's customer Data warehouse evolved. The Architecture has evolved over time and currently has been accepted at the company as a best practice. It is interesting to mention that both the hardware platform (CPU and disk drives) and relational Database management system (RDBMS) software employed today at this company for Data warehousing is not the same as was selected for the first instantiation. The implication was that the best plan or practice was a flexible one. There were many challenges, like organizational, technical, Data sourcing and Data naming, needed to be solved during the pre-project, initial stages, and throughout the project and beyond. The initial Data warehouse, implemented in 1996, was termed an overall success and approved for expansion. The current Data warehouse Data are being used by over six hundred registered users to fine-tune customer marketing and leverage and share Data in an enterprise manner. The Data warehouse has allowed the company to strengthen customer relationship management (CRM) core capabilities and business partnerships. Today, there are many departments benefiting from queries and requests for Data warehouse Data, many anticipated, some not. Although not planned, the Data warehouse has been a valuable source of purchase and customer Data in case of a manufacturer recall of merchandise. Above all, the company has been able to leverage and share enterprise customer Data to the benefit of the entire company.
Audris Mockus - One of the best experts on this subject based on the ideXlab platform.
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WIP: Live Restructuring of Data Architecture
2017 IEEE ACM 12th International Workshop on Software Engineering for Science (SE4Science), 2017Co-Authors: Walton Macey, Dali Wang, Peter Thornton, Audris MockusAbstract:In large-scale Earth System simulation codes, such asthe Accelerated Climate Model for Energy (ACME), complex user derived Data types (containing large numberof variables) are designed to represent the interactionsof atmosphere, ocean, land, ice, and biosphere toproject global climate under a wide variety of conditions. The following is our proposed approach to restructurethe Data Architecture of a land component within theACME project while the project is undergoing activedevelopment. The Data Architect for the land subsystemdefines the new Datatype requirements that wouldgreatly simplify the implementation of terrestrial landsubmodels by converting more than 50 to just eight primaryData-types. Since the code is developed with thecommunity governance, we have to ensure that the restructuringdoes not interface the other developmentwhich, with dozens of changes occurring every day, makeit impossible to work on a shared development branch. The active development also occurs on almost five hundredbranches, making it extremely difficult to assesspotential interactions. To address these challenges we have designed andstarted an iterative procedure for implementing the Datarestructuring and estimating both the effort it takes torestructure and the effort would save once the restructuringis implemented.
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Live restructuring of Data Architecture: WIP
2017Co-Authors: Walton Macey, Dali Wang, Peter E. Thornton, Audris MockusAbstract:In large-scale Earth System simulation codes, such as the Accelerated Climate Model for Energy (ACME), complex user derived Data types (containing large number of variables) are designed to represent the interactions of atmosphere, ocean, land, ice, and biosphere to project global climate under a wide variety of conditions. The following is our proposed approach to restructure the Data Architecture of a land component within the ACME project while the project is undergoing active development. The Data Architect for the land subsystem defines the new Datatype requirements that would greatly simplify the implementation of terrestrial land submodels by converting more than 50 to just eight primary Data-types. Since the code is developed with the community governance, we have to ensure that the restructuring does not interface the other development which, with dozens of changes occurring every day, make it impossible to work on a shared development branch. The active development also occurs on almost five hundred branches, making it extremely difficult to assess potential interactions. To address these challenges we have designed and started an iterative procedure for implementing the Data restructuring and estimating both the effort it takes to restructure and the effort would save once the restructuring is implemented.
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SE4Science@ICSE - WIP: Live Restructuring of Data Architecture
2017 IEEE ACM 12th International Workshop on Software Engineering for Science (SE4Science), 2017Co-Authors: Walton Macey, Dali Wang, Peter E. Thornton, Audris MockusAbstract:In large-scale Earth System simulation codes, such asthe Accelerated Climate Model for Energy (ACME), complex user derived Data types (containing large numberof variables) are designed to represent the interactionsof atmosphere, ocean, land, ice, and biosphere toproject global climate under a wide variety of conditions. The following is our proposed approach to restructurethe Data Architecture of a land component within theACME project while the project is undergoing activedevelopment. The Data Architect for the land subsystemdefines the new Datatype requirements that wouldgreatly simplify the implementation of terrestrial landsubmodels by converting more than 50 to just eight primaryData-types. Since the code is developed with thecommunity governance, we have to ensure that the restructuringdoes not interface the other developmentwhich, with dozens of changes occurring every day, makeit impossible to work on a shared development branch. The active development also occurs on almost five hundredbranches, making it extremely difficult to assesspotential interactions. To address these challenges we have designed andstarted an iterative procedure for implementing the Datarestructuring and estimating both the effort it takes torestructure and the effort would save once the restructuringis implemented.
J. Lawyer - One of the best experts on this subject based on the ideXlab platform.
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HICSS - Best practices in Data warehousing to support business initiatives and needs
37th Annual Hawaii International Conference on System Sciences 2004. Proceedings of the, 2004Co-Authors: J. Lawyer, S. ChowdhuryAbstract:The paper presents the Data warehousing Architecture and practices used at a major U. S. retailing company. Many considerations were assessed when deciding which Data warehousing Architecture to adopt. The paper discusses the two pre-dominant styles in Data warehousing, namely the "Bill Inmon Style" or the top-down approach and the "Ralph Kimball Style "or the bottom-up approach. The company chose the Inmon style due to a unique combination of circumstances in their business and technical environments, which are being discussed in detail. Much of the information presented in this paper is based upon the direct experiences of the lead Data Architect assigned to the projects under which this U. S. retailing company's customer Data warehouse evolved. The Architecture has evolved over time and currently has been accepted at the company as a best practice. It is interesting to mention that both the hardware platform (CPU and disk drives) and relational Database management system (RDBMS) software employed today at this company for Data warehousing is not the same as was selected for the first instantiation. The implication was that the best plan or practice was a flexible one. There were many challenges, like organizational, technical, Data sourcing and Data naming, needed to be solved during the pre-project, initial stages, and throughout the project and beyond. The initial Data warehouse, implemented in 1996, was termed an overall success and approved for expansion. The current Data warehouse Data are being used by over six hundred registered users to fine-tune customer marketing and leverage and share Data in an enterprise manner. The Data warehouse has allowed the company to strengthen customer relationship management (CRM) core capabilities and business partnerships. Today, there are many departments benefiting from queries and requests for Data warehouse Data, many anticipated, some not. Although not planned, the Data warehouse has been a valuable source of purchase and customer Data in case of a manufacturer recall of merchandise. Above all, the company has been able to leverage and share enterprise customer Data to the benefit of the entire company.
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Best practices in Data warehousing to support business initiatives and needs
37th Annual Hawaii International Conference on System Sciences 2004. Proceedings of the, 2004Co-Authors: J. Lawyer, S. ChowdhuryAbstract:The paper presents the Data warehousing Architecture and practices used at a major U. S. retailing company. Many considerations were assessed when deciding which Data warehousing Architecture to adopt. The paper discusses the two pre-dominant styles in Data warehousing, namely the "Bill Inmon Style" or the top-down approach and the "Ralph Kimball Style "or the bottom-up approach. The company chose the Inmon style due to a unique combination of circumstances in their business and technical environments, which are being discussed in detail. Much of the information presented in this paper is based upon the direct experiences of the lead Data Architect assigned to the projects under which this U. S. retailing company's customer Data warehouse evolved. The Architecture has evolved over time and currently has been accepted at the company as a best practice. It is interesting to mention that both the hardware platform (CPU and disk drives) and relational Database management system (RDBMS) software employed today at this company for Data warehousing is not the same as was selected for the first instantiation. The implication was that the best plan or practice was a flexible one. There were many challenges, like organizational, technical, Data sourcing and Data naming, needed to be solved during the pre-project, initial stages, and throughout the project and beyond. The initial Data warehouse, implemented in 1996, was termed an overall success and approved for expansion. The current Data warehouse Data are being used by over six hundred registered users to fine-tune customer marketing and leverage and share Data in an enterprise manner. The Data warehouse has allowed the company to strengthen customer relationship management (CRM) core capabilities and business partnerships. Today, there are many departments benefiting from queries and requests for Data warehouse Data, many anticipated, some not. Although not planned, the Data warehouse has been a valuable source of purchase and customer Data in case of a manufacturer recall of merchandise. Above all, the company has been able to leverage and share enterprise customer Data to the benefit of the entire company.
Walton Macey - One of the best experts on this subject based on the ideXlab platform.
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WIP: Live Restructuring of Data Architecture
2017 IEEE ACM 12th International Workshop on Software Engineering for Science (SE4Science), 2017Co-Authors: Walton Macey, Dali Wang, Peter Thornton, Audris MockusAbstract:In large-scale Earth System simulation codes, such asthe Accelerated Climate Model for Energy (ACME), complex user derived Data types (containing large numberof variables) are designed to represent the interactionsof atmosphere, ocean, land, ice, and biosphere toproject global climate under a wide variety of conditions. The following is our proposed approach to restructurethe Data Architecture of a land component within theACME project while the project is undergoing activedevelopment. The Data Architect for the land subsystemdefines the new Datatype requirements that wouldgreatly simplify the implementation of terrestrial landsubmodels by converting more than 50 to just eight primaryData-types. Since the code is developed with thecommunity governance, we have to ensure that the restructuringdoes not interface the other developmentwhich, with dozens of changes occurring every day, makeit impossible to work on a shared development branch. The active development also occurs on almost five hundredbranches, making it extremely difficult to assesspotential interactions. To address these challenges we have designed andstarted an iterative procedure for implementing the Datarestructuring and estimating both the effort it takes torestructure and the effort would save once the restructuringis implemented.
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Live restructuring of Data Architecture: WIP
2017Co-Authors: Walton Macey, Dali Wang, Peter E. Thornton, Audris MockusAbstract:In large-scale Earth System simulation codes, such as the Accelerated Climate Model for Energy (ACME), complex user derived Data types (containing large number of variables) are designed to represent the interactions of atmosphere, ocean, land, ice, and biosphere to project global climate under a wide variety of conditions. The following is our proposed approach to restructure the Data Architecture of a land component within the ACME project while the project is undergoing active development. The Data Architect for the land subsystem defines the new Datatype requirements that would greatly simplify the implementation of terrestrial land submodels by converting more than 50 to just eight primary Data-types. Since the code is developed with the community governance, we have to ensure that the restructuring does not interface the other development which, with dozens of changes occurring every day, make it impossible to work on a shared development branch. The active development also occurs on almost five hundred branches, making it extremely difficult to assess potential interactions. To address these challenges we have designed and started an iterative procedure for implementing the Data restructuring and estimating both the effort it takes to restructure and the effort would save once the restructuring is implemented.
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SE4Science@ICSE - WIP: Live Restructuring of Data Architecture
2017 IEEE ACM 12th International Workshop on Software Engineering for Science (SE4Science), 2017Co-Authors: Walton Macey, Dali Wang, Peter E. Thornton, Audris MockusAbstract:In large-scale Earth System simulation codes, such asthe Accelerated Climate Model for Energy (ACME), complex user derived Data types (containing large numberof variables) are designed to represent the interactionsof atmosphere, ocean, land, ice, and biosphere toproject global climate under a wide variety of conditions. The following is our proposed approach to restructurethe Data Architecture of a land component within theACME project while the project is undergoing activedevelopment. The Data Architect for the land subsystemdefines the new Datatype requirements that wouldgreatly simplify the implementation of terrestrial landsubmodels by converting more than 50 to just eight primaryData-types. Since the code is developed with thecommunity governance, we have to ensure that the restructuringdoes not interface the other developmentwhich, with dozens of changes occurring every day, makeit impossible to work on a shared development branch. The active development also occurs on almost five hundredbranches, making it extremely difficult to assesspotential interactions. To address these challenges we have designed andstarted an iterative procedure for implementing the Datarestructuring and estimating both the effort it takes torestructure and the effort would save once the restructuringis implemented.
