The Experts below are selected from a list of 39 Experts worldwide ranked by ideXlab platform
Joc Cing Tay - One of the best experts on this subject based on the ideXlab platform.
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approximate string matching for multiple attribute large scale Customer Address databases
International Conference on Asian Digital Libraries, 2003Co-Authors: Y M Cheong, Joc Cing TayAbstract:The default pattern matching capabilities in today’s RDBMS are generally unable to cope with errors and variations that may exist in stored textual information. In this paper, we present SKIPPER, a simple search methodology that allows approximate string matching on multiple-attribute, large-scale Customer Address information for the Credit Collection industry. The proposed solution relies on the edit distance error model and the q-gram string filtering technique. We present an algorithm that integrates the methodology with existing RDBMS through SQL-based stored procedures.
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ICADL - Approximate String Matching for Multiple-Attribute, Large-Scale Customer Address Databases
Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access, 2003Co-Authors: Y M Cheong, Joc Cing TayAbstract:The default pattern matching capabilities in today’s RDBMS are generally unable to cope with errors and variations that may exist in stored textual information. In this paper, we present SKIPPER, a simple search methodology that allows approximate string matching on multiple-attribute, large-scale Customer Address information for the Credit Collection industry. The proposed solution relies on the edit distance error model and the q-gram string filtering technique. We present an algorithm that integrates the methodology with existing RDBMS through SQL-based stored procedures.
Vinay S Bhaskar - One of the best experts on this subject based on the ideXlab platform.
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Social and Business Intelligence Analysis Using PSO
arXiv: Artificial Intelligence, 2014Co-Authors: Jyoti Chaturvedi, Anubha Parashar, Amrita A. Manjrekar, Vinay S BhaskarAbstract:The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. .The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data enterprises maintain Customer Address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behavior.
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Business and Social Behaviour Intelligence Analysis Using PSO
International Journal of Interactive Multimedia and Artificial Intelligence, 2014Co-Authors: Vinay S Bhaskar, Anubha Parashar, Abhishek Kumar Singh, Jyoti Dhruw, Mradula SharmaAbstract:The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain Customer Address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and selfdescriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour.
Y M Cheong - One of the best experts on this subject based on the ideXlab platform.
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approximate string matching for multiple attribute large scale Customer Address databases
International Conference on Asian Digital Libraries, 2003Co-Authors: Y M Cheong, Joc Cing TayAbstract:The default pattern matching capabilities in today’s RDBMS are generally unable to cope with errors and variations that may exist in stored textual information. In this paper, we present SKIPPER, a simple search methodology that allows approximate string matching on multiple-attribute, large-scale Customer Address information for the Credit Collection industry. The proposed solution relies on the edit distance error model and the q-gram string filtering technique. We present an algorithm that integrates the methodology with existing RDBMS through SQL-based stored procedures.
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ICADL - Approximate String Matching for Multiple-Attribute, Large-Scale Customer Address Databases
Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access, 2003Co-Authors: Y M Cheong, Joc Cing TayAbstract:The default pattern matching capabilities in today’s RDBMS are generally unable to cope with errors and variations that may exist in stored textual information. In this paper, we present SKIPPER, a simple search methodology that allows approximate string matching on multiple-attribute, large-scale Customer Address information for the Credit Collection industry. The proposed solution relies on the edit distance error model and the q-gram string filtering technique. We present an algorithm that integrates the methodology with existing RDBMS through SQL-based stored procedures.
Jyoti Chaturvedi - One of the best experts on this subject based on the ideXlab platform.
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Social and Business Intelligence Analysis Using PSO
arXiv: Artificial Intelligence, 2014Co-Authors: Jyoti Chaturvedi, Anubha Parashar, Amrita A. Manjrekar, Vinay S BhaskarAbstract:The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. .The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data enterprises maintain Customer Address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behavior.
Anubha Parashar - One of the best experts on this subject based on the ideXlab platform.
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Social and Business Intelligence Analysis Using PSO
arXiv: Artificial Intelligence, 2014Co-Authors: Jyoti Chaturvedi, Anubha Parashar, Amrita A. Manjrekar, Vinay S BhaskarAbstract:The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. .The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data enterprises maintain Customer Address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behavior.
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Business and Social Behaviour Intelligence Analysis Using PSO
International Journal of Interactive Multimedia and Artificial Intelligence, 2014Co-Authors: Vinay S Bhaskar, Anubha Parashar, Abhishek Kumar Singh, Jyoti Dhruw, Mradula SharmaAbstract:The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain Customer Address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and selfdescriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour.
