The Experts below are selected from a list of 42936 Experts worldwide ranked by ideXlab platform

Yanchun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • executing sql queries over encrypted character strings in the Database as service model
    Knowledge Based Systems, 2012
    Co-Authors: Enhong Chen, Yanchun Zhang
    Abstract:

    Rapid advances in the networking technologies have prompted the emergence of the ''software as service'' model for Enterprise computing, moreover, which is becoming one of the key industries quickly. ''Database as service'' model provides users power to store, modify and retrieve Data from anywhere in the world, as long as they have access to the Internet, thus, being increasingly popular in current Enterprise Data Management systems. However, this model introduces several challenges, an essential issue being how to implement SQL queries over encrypted Data efficiently. To ensure Data security, this model generally encrypts sensitive Data at the trusted client's site, before storing them into the non-trusted Database service provider's site, which, unfortunately, results in that SQL queries cannot be executed over the encrypted Data immediately at the Database service provider. In this paper we only focus on how to query encrypted character strings efficiently. Our strategy is that when storing character strings to the Database service provider, we not only store the encrypted character strings themselves, but also generate some characteristic index values for these character strings, and store them in an additional field; and when querying the encrypted character strings, we first execute a coarse query over the characteristic index fields at the Database service provider, in order to filter out most of tuples not related to the querying conditions, and then, we decrypt the rest tuples and execute a refined query over them again at the client site. In our strategy, we define an n-phase reachability matrix for a character string and use it as the characteristic index values, and based on such a definition, we present some theorems to split a SQL query into its server-side representation and client-side representation for partitioning the computation of a query across the client and the server and thus improving query performance. Finally, experimental results validate the functionality and effectiveness of our strategy.

Yue Pan - One of the best experts on this subject based on the ideXlab platform.

  • semantic enhancement for Enterprise Data Management
    International Semantic Web Conference, 2009
    Co-Authors: Xingzhi Sun, Feng Cao, Chen Wang, Xiaoyuan Wang, Nick Kanellos, Dan Wolfson, Yue Pan
    Abstract:

    Taking customer Data as an example, the paper presents an approach to enhance the Management of Enterprise Data by using Semantic Web technologies. Customer Data is the most important kind of core business entity a company uses repeatedly across many business processes and systems, and customer Data Management (CDM) is becoming critical for Enterprises because it keeps a single, complete and accurate record of customers across the Enterprise. Existing CDM systems focus on integrating customer Data from all customer-facing channels and front and back office systems through multiple interfaces, as well as publishing customer Data to different applications. To make the effective use of the CDM system, this paper investigates semantic query and analysis over the integrated and centralized customer Data, enabling automatic classification and relationship discovery. We have implemented these features over IBM Websphere Customer Center, and shown the prototype to our clients. We believe that our study and experiences are valuable for both Semantic Web community and Data Management community.

Enhong Chen - One of the best experts on this subject based on the ideXlab platform.

  • executing sql queries over encrypted character strings in the Database as service model
    Knowledge Based Systems, 2012
    Co-Authors: Enhong Chen, Yanchun Zhang
    Abstract:

    Rapid advances in the networking technologies have prompted the emergence of the ''software as service'' model for Enterprise computing, moreover, which is becoming one of the key industries quickly. ''Database as service'' model provides users power to store, modify and retrieve Data from anywhere in the world, as long as they have access to the Internet, thus, being increasingly popular in current Enterprise Data Management systems. However, this model introduces several challenges, an essential issue being how to implement SQL queries over encrypted Data efficiently. To ensure Data security, this model generally encrypts sensitive Data at the trusted client's site, before storing them into the non-trusted Database service provider's site, which, unfortunately, results in that SQL queries cannot be executed over the encrypted Data immediately at the Database service provider. In this paper we only focus on how to query encrypted character strings efficiently. Our strategy is that when storing character strings to the Database service provider, we not only store the encrypted character strings themselves, but also generate some characteristic index values for these character strings, and store them in an additional field; and when querying the encrypted character strings, we first execute a coarse query over the characteristic index fields at the Database service provider, in order to filter out most of tuples not related to the querying conditions, and then, we decrypt the rest tuples and execute a refined query over them again at the client site. In our strategy, we define an n-phase reachability matrix for a character string and use it as the characteristic index values, and based on such a definition, we present some theorems to split a SQL query into its server-side representation and client-side representation for partitioning the computation of a query across the client and the server and thus improving query performance. Finally, experimental results validate the functionality and effectiveness of our strategy.

Hongming Cai - One of the best experts on this subject based on the ideXlab platform.

  • instance driven ontology evolution mechanism towards Enterprise Data Management
    International Conference on e-Business Engineering, 2011
    Co-Authors: Cheng Xie, Lihong Jiang, Hongming Cai
    Abstract:

    with the wide application of information systems, more and more Enterprises adopt ontology as conceptual backbone for business Data Management to take advantage of knowledge base and semantic web technology. However, dynamic business motivates modifying existed ontology according to the continuous changes, which is hard to be done manually due to the growing size of ontology. Thus automatic method is needed for ontology evolution. In this paper, an instance-driven ontology evolution approach is proposed to cope with the dynamic changes in ontology evolution. The proposed approach suggests changing directions to users to control the process of evolution. Then the changes of ontology are formalized with elementary and composite changes which are considered as the basis of evolution. Moreover, the identification of changes is realized by instances analyzing. Finally, the approach is tested in a hospital Database. The result shows instance-driven ontology evolution is an efficient method to cope with Data conception changes in Enterprise Data Management.

Xingzhi Sun - One of the best experts on this subject based on the ideXlab platform.

  • semantic enhancement for Enterprise Data Management
    International Semantic Web Conference, 2009
    Co-Authors: Xingzhi Sun, Feng Cao, Chen Wang, Xiaoyuan Wang, Nick Kanellos, Dan Wolfson, Yue Pan
    Abstract:

    Taking customer Data as an example, the paper presents an approach to enhance the Management of Enterprise Data by using Semantic Web technologies. Customer Data is the most important kind of core business entity a company uses repeatedly across many business processes and systems, and customer Data Management (CDM) is becoming critical for Enterprises because it keeps a single, complete and accurate record of customers across the Enterprise. Existing CDM systems focus on integrating customer Data from all customer-facing channels and front and back office systems through multiple interfaces, as well as publishing customer Data to different applications. To make the effective use of the CDM system, this paper investigates semantic query and analysis over the integrated and centralized customer Data, enabling automatic classification and relationship discovery. We have implemented these features over IBM Websphere Customer Center, and shown the prototype to our clients. We believe that our study and experiences are valuable for both Semantic Web community and Data Management community.