The Experts below are selected from a list of 93 Experts worldwide ranked by ideXlab platform
Wesley Deneke - One of the best experts on this subject based on the ideXlab platform.
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CSE - data File Layout Inference Using Content-Based Oracles
2013 IEEE 16th International Conference on Computational Science and Engineering, 2013Co-Authors: Reid A. Phillips, Craig W. Thompson, Wingning Li, Wesley DenekeAbstract:data file layout inference refers to the problem of identifying the organizational characteristics associated with a structured text file, where every record in a text file shares the same structural properties. These properties include: character encoding, record length, field length (indicated by delimiting characters or fixed length), field position, and field semantic content. Within this paper, the above information is referred to as the layout of a file. This structural layout information is required to extract, transform, and load files into workflows within various data warehouse and data mining applications. A common need, layout inference is a manual, labor intensive process requiring human expertise whenever a file's layout is unavailable, miscommunicated, or changed. This paper proposes an automated methodology for solving the layout inference problem by discovering the metadata of a structured text file and reports the results of a prototype system for real data files from customer data integration and management application.
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data File Layout Inference Using Content-Based Oracles
2013 IEEE 16th International Conference on Computational Science and Engineering, 2013Co-Authors: Reid A. Phillips, Craig Thompson, Wingning Li, Wesley DenekeAbstract:data file layout inference refers to the problem of identifying the organizational characteristics associated with a structured text file, where every record in a text file shares the same structural properties. These properties include: character encoding, record length, field length (indicated by delimiting characters or fixed length), field position, and field semantic content. Within this paper, the above information is referred to as the layout of a file. This structural layout information is required to extract, transform, and load files into workflows within various data warehouse and data mining applications. A common need, layout inference is a manual, labor intensive process requiring human expertise whenever a file's layout is unavailable, miscommunicated, or changed. This paper proposes an automated methodology for solving the layout inference problem by discovering the metadata of a structured text file and reports the results of a prototype system for real data files from customer data integration and management application.
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Towards a Domain-Specific Modeling Language for customer data integration Workflow
2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops, 2008Co-Authors: Wesley Deneke, Wingning Li, Craig Thompson, John Talburt, Jonathan Loghry, David Nash, Jeff StiresAbstract:This paper describes the workflow specification problem, how workflows are specified today, requirements for improved workflow specification, and begins to sketch a new domain-specific modeling language (DSML) approach for specifying intent that can be used to constructively generate a complete workflow meeting a collection of intent requirements. This is an interim report on work in progress.
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GPC Workshops - Towards a Domain-Specific Modeling Language for customer data integration Workflow
2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops, 2008Co-Authors: Wesley Deneke, Wingning Li, John Talburt, Jonathan Loghry, David Nash, Craig W. Thompson, Jeff StiresAbstract:This paper describes the workflow specification problem, how workflows are specified today, requirements for improved workflow specification, and begins to sketch a new domain-specific modeling language (DSML) approach for specifying intent that can be used to constructively generate a complete workflow meeting a collection of intent requirements. This is an interim report on work in progress.
David Barker - One of the best experts on this subject based on the ideXlab platform.
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customer data integration: Reaching more consumers with certainty
Journal of Database Marketing & Customer Strategy Management, 2011Co-Authors: David BarkerAbstract:data hygiene is nothing new, but in this multichannel landscape, the need to collect and manage data in a bid to recognise consumers at any touchpoint is greater than ever. This article looks at the cutting-edge techniques for gathering and maintaining data on- and offline. It takes in single customer databases and 360-degree views, and highlights how consumer channel preference can be used to inform marketing budget placement and campaign strategy. This article also includes comparison of in-house and external customer data integration management.
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Marketing automation systems integration: The art and engineering to make it all work seamlessly
Journal of Direct Data and Digital Marketing Practice, 2009Co-Authors: David Keens, David BarkerAbstract:The successful implementation of any complex information technology (IT) solution is challenging, but integrating a marketing automation solution into an organisation brings additional and unique considerations. The key decision points range from how to perform appropriate business process modelling and requirements analysis, through to the selection of customer data integration technology, software applications and implementation partners. There are aspects of both art and engineering required to make this integration appear seamless. The current economic downturn places additional pressures on marketing and IT budgets, but a successful implementation of marketing automation systems can simultaneously cut costs and drive additional revenue. This paper identifies success criteria for marketing automation systems integration, and presents the four key factors influencing the outcome of such integration projects. © 2009 Palgrave Macmillan.
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Marketing automation systems integration: The art and engineering to make it all work seamlessly
Journal of Direct Data and Digital Marketing Practice, 2009Co-Authors: David Keens, David BarkerAbstract:The successful implementation of any complex information technology (IT) solution is challenging, but integrating a marketing automation solution into an organisation brings additional and unique considerations. The key decision points range from how to perform appropriate business process modelling and requirements analysis, through to the selection of customer data integration technology, software applications and implementation partners. There are aspects of both art and engineering required to make this integration appear seamless. The current economic downturn places additional pressures on marketing and IT budgets, but a successful implementation of marketing automation systems can simultaneously cut costs and drive additional revenue. This paper identifies success criteria for marketing automation systems integration, and presents the four key factors influencing the outcome of such integration projects.
Wingning Li - One of the best experts on this subject based on the ideXlab platform.
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CSE - data File Layout Inference Using Content-Based Oracles
2013 IEEE 16th International Conference on Computational Science and Engineering, 2013Co-Authors: Reid A. Phillips, Craig W. Thompson, Wingning Li, Wesley DenekeAbstract:data file layout inference refers to the problem of identifying the organizational characteristics associated with a structured text file, where every record in a text file shares the same structural properties. These properties include: character encoding, record length, field length (indicated by delimiting characters or fixed length), field position, and field semantic content. Within this paper, the above information is referred to as the layout of a file. This structural layout information is required to extract, transform, and load files into workflows within various data warehouse and data mining applications. A common need, layout inference is a manual, labor intensive process requiring human expertise whenever a file's layout is unavailable, miscommunicated, or changed. This paper proposes an automated methodology for solving the layout inference problem by discovering the metadata of a structured text file and reports the results of a prototype system for real data files from customer data integration and management application.
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data File Layout Inference Using Content-Based Oracles
2013 IEEE 16th International Conference on Computational Science and Engineering, 2013Co-Authors: Reid A. Phillips, Craig Thompson, Wingning Li, Wesley DenekeAbstract:data file layout inference refers to the problem of identifying the organizational characteristics associated with a structured text file, where every record in a text file shares the same structural properties. These properties include: character encoding, record length, field length (indicated by delimiting characters or fixed length), field position, and field semantic content. Within this paper, the above information is referred to as the layout of a file. This structural layout information is required to extract, transform, and load files into workflows within various data warehouse and data mining applications. A common need, layout inference is a manual, labor intensive process requiring human expertise whenever a file's layout is unavailable, miscommunicated, or changed. This paper proposes an automated methodology for solving the layout inference problem by discovering the metadata of a structured text file and reports the results of a prototype system for real data files from customer data integration and management application.
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Towards a Domain-Specific Modeling Language for customer data integration Workflow
2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops, 2008Co-Authors: Wesley Deneke, Wingning Li, Craig Thompson, John Talburt, Jonathan Loghry, David Nash, Jeff StiresAbstract:This paper describes the workflow specification problem, how workflows are specified today, requirements for improved workflow specification, and begins to sketch a new domain-specific modeling language (DSML) approach for specifying intent that can be used to constructively generate a complete workflow meeting a collection of intent requirements. This is an interim report on work in progress.
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GPC Workshops - Towards a Domain-Specific Modeling Language for customer data integration Workflow
2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops, 2008Co-Authors: Wesley Deneke, Wingning Li, John Talburt, Jonathan Loghry, David Nash, Craig W. Thompson, Jeff StiresAbstract:This paper describes the workflow specification problem, how workflows are specified today, requirements for improved workflow specification, and begins to sketch a new domain-specific modeling language (DSML) approach for specifying intent that can be used to constructively generate a complete workflow meeting a collection of intent requirements. This is an interim report on work in progress.
Reid A. Phillips - One of the best experts on this subject based on the ideXlab platform.
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CSE - data File Layout Inference Using Content-Based Oracles
2013 IEEE 16th International Conference on Computational Science and Engineering, 2013Co-Authors: Reid A. Phillips, Craig W. Thompson, Wingning Li, Wesley DenekeAbstract:data file layout inference refers to the problem of identifying the organizational characteristics associated with a structured text file, where every record in a text file shares the same structural properties. These properties include: character encoding, record length, field length (indicated by delimiting characters or fixed length), field position, and field semantic content. Within this paper, the above information is referred to as the layout of a file. This structural layout information is required to extract, transform, and load files into workflows within various data warehouse and data mining applications. A common need, layout inference is a manual, labor intensive process requiring human expertise whenever a file's layout is unavailable, miscommunicated, or changed. This paper proposes an automated methodology for solving the layout inference problem by discovering the metadata of a structured text file and reports the results of a prototype system for real data files from customer data integration and management application.
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data File Layout Inference Using Content-Based Oracles
2013 IEEE 16th International Conference on Computational Science and Engineering, 2013Co-Authors: Reid A. Phillips, Craig Thompson, Wingning Li, Wesley DenekeAbstract:data file layout inference refers to the problem of identifying the organizational characteristics associated with a structured text file, where every record in a text file shares the same structural properties. These properties include: character encoding, record length, field length (indicated by delimiting characters or fixed length), field position, and field semantic content. Within this paper, the above information is referred to as the layout of a file. This structural layout information is required to extract, transform, and load files into workflows within various data warehouse and data mining applications. A common need, layout inference is a manual, labor intensive process requiring human expertise whenever a file's layout is unavailable, miscommunicated, or changed. This paper proposes an automated methodology for solving the layout inference problem by discovering the metadata of a structured text file and reports the results of a prototype system for real data files from customer data integration and management application.
Valter Šorli - One of the best experts on this subject based on the ideXlab platform.
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Master data management – customer data integration
2014Co-Authors: Valter ŠorliAbstract:In this Master’s thesis we deal with the problem of managing customer master data. Organizations are often faced with inconsistent data, which are scattered throughout the various silos applications. Since silos are living their own lives, the master data in them remains uncoordinated and business users often do not know which copy reflects the latest valid state. With a desire to raise the quality of customer master data, it is necessary to establish a system to manage them. The purpose of this work is to present and describe the properties of such systems. We introduce three usage methods of systems for management: collaborative, operational and analytical, which in addition to the four modes of implementation, define a system for managing customer master data. Considered are all four methods of implementation that significantly determine the properties of MDM hub. We describe the operation of the registry, consolidation, transactional and coexistence hub, and perform their mutual comparison. A method for determining the degree of maturity of master data management is presented. An organization can use it as a measure of the quality of its current solution and for seeking of further steps for improvement. The project of establishing a system of governance is extremely extensive and expensive process which, due to interference with the business processes of the organization, involves certain risks. In this work, we propose an appropriate methodology for development and project management, additionally there are given some guidelines for management team that will lead the project. On the market there are many providers of customer master data management solutions. To narrow the list of bidders and facilitate the organization selection, we present the results of two analytical houses, which periodically monitor the supply of the customer master data management segment. For five of the leading providers we sets out the main advantages and disadvantages of their solutions. Information that can be beneficial for an organization which is establishing a system for customer master data management, are in this work gathered on one place.
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master data management customer data integration
2014Co-Authors: Valter ŠorliAbstract:In this Master’s thesis we deal with the problem of managing customer master data. Organizations are often faced with inconsistent data, which are scattered throughout the various silos applications. Since silos are living their own lives, the master data in them remains uncoordinated and business users often do not know which copy reflects the latest valid state. With a desire to raise the quality of customer master data, it is necessary to establish a system to manage them. The purpose of this work is to present and describe the properties of such systems. We introduce three usage methods of systems for management: collaborative, operational and analytical, which in addition to the four modes of implementation, define a system for managing customer master data. Considered are all four methods of implementation that significantly determine the properties of MDM hub. We describe the operation of the registry, consolidation, transactional and coexistence hub, and perform their mutual comparison. A method for determining the degree of maturity of master data management is presented. An organization can use it as a measure of the quality of its current solution and for seeking of further steps for improvement. The project of establishing a system of governance is extremely extensive and expensive process which, due to interference with the business processes of the organization, involves certain risks. In this work, we propose an appropriate methodology for development and project management, additionally there are given some guidelines for management team that will lead the project. On the market there are many providers of customer master data management solutions. To narrow the list of bidders and facilitate the organization selection, we present the results of two analytical houses, which periodically monitor the supply of the customer master data management segment. For five of the leading providers we sets out the main advantages and disadvantages of their solutions. Information that can be beneficial for an organization which is establishing a system for customer master data management, are in this work gathered on one place.
