The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Jaewoo Chang - One of the best experts on this subject based on the ideXlab platform.
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a range query processing algorithm hiding data access patterns in outsourced Database Environment
International Conference on Data Mining, 2016Co-Authors: Hyeongil Kim, Hyeongjin Kim, Jaewoo ChangAbstract:Research on secure range query processing techniques in outsourced Databases has been spotlighted with the development of cloud computing. The existing range query processing schemes can preserve the data privacy and the query privacy of a user. However, they fail to hide the data access patterns while processing a range query. So, in this paper we propose a secure range query processing algorithm which hides data access patterns. Our method filters unnecessary data using the encrypted index. We show from our performance analysis that the proposed range query processing algorithm can efficiently process a query while hiding the data access patterns.
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DMBD - A Range Query Processing Algorithm Hiding Data Access Patterns in Outsourced Database Environment
Data Mining and Big Data, 2016Co-Authors: Hyeongil Kim, Hyeongjin Kim, Jaewoo ChangAbstract:Research on secure range query processing techniques in outsourced Databases has been spotlighted with the development of cloud computing. The existing range query processing schemes can preserve the data privacy and the query privacy of a user. However, they fail to hide the data access patterns while processing a range query. So, in this paper we propose a secure range query processing algorithm which hides data access patterns. Our method filters unnecessary data using the encrypted index. We show from our performance analysis that the proposed range query processing algorithm can efficiently process a query while hiding the data access patterns.
Christopher Jeris - One of the best experts on this subject based on the ideXlab platform.
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A robust and scalable clustering algorithm for mixed type attributes in large Database Environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '01, 2001Co-Authors: Tom Chiu, Dongping Fang, John Chen, Yao Wang, Christopher JerisAbstract:Clustering is a widely used technique in data mining applications to discover patterns in the underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either continuous or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining problems. In this paper, we propose a distance measure that enables clustering data with both continuous and categorical attributes. This distance measure is derived from a probabilistic model that the distance between two clusters is equivalent to the decrease in log-likelihood function as a result of merging. Calculation of this measure is memory efficient as it depends only on the merging cluster pair and not on all the other clusters. Zhang et al [8] proposed a clustering method named BIRCH that is especially suitable for very large datasets. We develop a clustering algorithm using our distance measure based on the framework of BIRCH. Similar to BIRCH, our algorithm first performs a pre-clustering step by scanning the entire dataset and storing the dense regions of data records in terms of summary statistics. A hierarchical clustering algorithm is then applied to cluster the dense regions. Apart from the ability of handling mixed type of attributes, our algorithm differs from BIRCH in that we add a procedure that enables the algorithm to automatically determine the appropriate number of clusters and a new strategy of assigning cluster membership to noisy data. For data with mixed type of attributes, our experimental results confirm that the algorithm not only generates better quality clusters than the traditional k-means algorithms, but also exhibits good scalability properties and is able to identify the underlying number of clusters in the data correctly. The algorithm is implemented in the commercial data mining tool Clementine 6.0 which supports the PMML standard of data mining model deployment.
J Mcmahon - One of the best experts on this subject based on the ideXlab platform.
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ismart sup tm i spatial sup tm information server deploying integrated web based spatial applications within an oracle Database Environment
Web Information Systems Engineering, 2001Co-Authors: Michela Bertolotto, James D. Carswell, L Mcgeown, J McmahonAbstract:In this paper, we describe the architectural and functional characteristics of e-Spatial/sup TM/ technology, comprising an innovative software package that represents a timely alternative to traditional and complex proprietary GIS application packages. The two main components of the package, developed by e-Spatial Solutions Ltd., are the iSMART/sup TM/ Database development technology and the i-Spatial/sup TM/ Information Server (iSIS), both implemented within an Oracle 9i spatial Database Environment. This technology allows users to build and deploy spatially-enabled or standard Internet applications without requiring any application-specific source code. It can be deployed on any Oracle-supported hardware platform and on any device that supports the Java Virtual Machine, thus providing full support for wireless, PDA and other mobile devices.
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WISE (2) - iSMART/sup TM/+i-Spatial/sup TM/ Information Server: deploying integrated Web-based spatial applications within an Oracle Database Environment
Proceedings of the Second International Conference on Web Information Systems Engineering, 1Co-Authors: Michela Bertolotto, James D. Carswell, L Mcgeown, J McmahonAbstract:In this paper, we describe the architectural and functional characteristics of e-Spatial/sup TM/ technology, comprising an innovative software package that represents a timely alternative to traditional and complex proprietary GIS application packages. The two main components of the package, developed by e-Spatial Solutions Ltd., are the iSMART/sup TM/ Database development technology and the i-Spatial/sup TM/ Information Server (iSIS), both implemented within an Oracle 9i spatial Database Environment. This technology allows users to build and deploy spatially-enabled or standard Internet applications without requiring any application-specific source code. It can be deployed on any Oracle-supported hardware platform and on any device that supports the Java Virtual Machine, thus providing full support for wireless, PDA and other mobile devices.
Hyeongil Kim - One of the best experts on this subject based on the ideXlab platform.
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a range query processing algorithm hiding data access patterns in outsourced Database Environment
International Conference on Data Mining, 2016Co-Authors: Hyeongil Kim, Hyeongjin Kim, Jaewoo ChangAbstract:Research on secure range query processing techniques in outsourced Databases has been spotlighted with the development of cloud computing. The existing range query processing schemes can preserve the data privacy and the query privacy of a user. However, they fail to hide the data access patterns while processing a range query. So, in this paper we propose a secure range query processing algorithm which hides data access patterns. Our method filters unnecessary data using the encrypted index. We show from our performance analysis that the proposed range query processing algorithm can efficiently process a query while hiding the data access patterns.
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DMBD - A Range Query Processing Algorithm Hiding Data Access Patterns in Outsourced Database Environment
Data Mining and Big Data, 2016Co-Authors: Hyeongil Kim, Hyeongjin Kim, Jaewoo ChangAbstract:Research on secure range query processing techniques in outsourced Databases has been spotlighted with the development of cloud computing. The existing range query processing schemes can preserve the data privacy and the query privacy of a user. However, they fail to hide the data access patterns while processing a range query. So, in this paper we propose a secure range query processing algorithm which hides data access patterns. Our method filters unnecessary data using the encrypted index. We show from our performance analysis that the proposed range query processing algorithm can efficiently process a query while hiding the data access patterns.
Tom Chiu - One of the best experts on this subject based on the ideXlab platform.
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A robust and scalable clustering algorithm for mixed type attributes in large Database Environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '01, 2001Co-Authors: Tom Chiu, Dongping Fang, John Chen, Yao Wang, Christopher JerisAbstract:Clustering is a widely used technique in data mining applications to discover patterns in the underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either continuous or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining problems. In this paper, we propose a distance measure that enables clustering data with both continuous and categorical attributes. This distance measure is derived from a probabilistic model that the distance between two clusters is equivalent to the decrease in log-likelihood function as a result of merging. Calculation of this measure is memory efficient as it depends only on the merging cluster pair and not on all the other clusters. Zhang et al [8] proposed a clustering method named BIRCH that is especially suitable for very large datasets. We develop a clustering algorithm using our distance measure based on the framework of BIRCH. Similar to BIRCH, our algorithm first performs a pre-clustering step by scanning the entire dataset and storing the dense regions of data records in terms of summary statistics. A hierarchical clustering algorithm is then applied to cluster the dense regions. Apart from the ability of handling mixed type of attributes, our algorithm differs from BIRCH in that we add a procedure that enables the algorithm to automatically determine the appropriate number of clusters and a new strategy of assigning cluster membership to noisy data. For data with mixed type of attributes, our experimental results confirm that the algorithm not only generates better quality clusters than the traditional k-means algorithms, but also exhibits good scalability properties and is able to identify the underlying number of clusters in the data correctly. The algorithm is implemented in the commercial data mining tool Clementine 6.0 which supports the PMML standard of data mining model deployment.
