The Experts below are selected from a list of 55833 Experts worldwide ranked by ideXlab platform
Carlo Zaniolo - One of the best experts on this subject based on the ideXlab platform.
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Archis: An xml-based approach to transaction-time Temporal Database systems
VLDB Journal, 2008Co-Authors: Fusheng Wang, Carlo Zaniolo, Xin ZhouAbstract:Effective support for Temporal applications by Database systems represents an important technical objective that is difficult to achieve since it requires an integrated solution for several problems, including (i) expressive Temporal representations and data models, (ii) powerful languages for Temporal queries and snapshot queries, (iii) indexing, clustering and query optimization techniques for managing Temporal information efficiently, and (iv) architectures that bring together the different pieces of enabling technology into a robust system. In this paper, we present the ArchIS system that achieves these objectives by supporting a Temporally grouped data model on top of RDBMS. ArchIS' architecture uses (a) XML to support Temporally grouped (virtual) representations of the Database history, (b) XQuery to express powerful Temporal queries on such views, (c) Temporal clustering and indexing techniques for managing the actual historical data in a relational Database, and (d) SQL/XML for executing the queries on the XML views as equivalent queries on the relational Database. The performance studies presented in the paper show that ArchIS is quite effective at storing and retrieving under complex query conditions the transaction-time history of relational Databases, and can also assure excellent storage efficiency by providing compression as an option. This approach achieves full-functionality transaction-time Databases without requiring Temporal extensions in XML or Database standards, and provides critical support to emerging application areas such as RFID.
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using xml to build efficient transaction time Temporal Database systems on relational Databases
International Conference on Data Engineering, 2006Co-Authors: Fusheng Wang, Xin Zhou, Carlo ZanioloAbstract:In this paper, we present the ArchIS system that achieves full-functionality transaction-time Databases without requiring Temporal extensions in XML or Database standards. ArchIS architecture uses (a) XML to support Temporally grouped (virtual) representations of the Database history, (b) XQuery to express powerful Temporal queries on such views, (c) Temporal clustering and indexing techniques for managing the actual historical data in a relational Database, and (d) SQL/XML for executing the queries on the XML views as equivalent queries on the relational Database. The performance studies presented in the paper show that ArchIS is quite effective at storing and retrieving under complex query conditions the transaction-time history of relational Databases.
Wan Jin - One of the best experts on this subject based on the ideXlab platform.
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membership of mixed dependency set in strong partial ordered Temporal scheme
Journal of Computer Applications, 2015Co-Authors: Wan JinAbstract:The solution of membership problem is essential to design an available algorithm of scheme decomposition.Because of the partial order among Temporal types in strong partial ordered Temporal scheme, it is difficult to solve its membership problem. The concepts of mixed dependency base on given Temporal type, mixed dependency base in strong partial ordered scheme, mixed set closure of partial Temporal functional dependency and Temporal multi-valued dependency and mixed closure of strong partial ordered scheme were given. The algorithms of dependency base of attribution and closure of attribution sets were also given. On this basis, the algorithm of membership problem of mixed dependency set in strong partial ordered scheme was put forward. The proof for its termination, correction and time complexity were presented. Application examples show that the research on related theory and algorithm solves determination of the membership problem in strong partial ordered mixed dependencies, and provides a theoretical basis for solving the strong partial order Temporal scheme and the design of Temporal Database standardization.
V Janaki - One of the best experts on this subject based on the ideXlab platform.
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an approach for mining similar Temporal association patterns in single Database scan
2016Co-Authors: Vangipuram Radhakrishna, P V Kumar, V JanakiAbstract:Mining similar Temporal association patterns from a time stamped Temporal Database is an important research problem in Temporal data mining. The main objective and idea of this research is in finding similar Temporal patterns from a given time stamped Temporal Database of transactions by scanning the input Database only once. This objective to find Temporally similar patterns through single scan of Database coins out an important challenge to devise a single Database scan procedure which shall use only support values of items computed in the first Database scan, so as to discover all other Temporal patterns. In the current research, we come out with a novel procedure to discover similar Temporal patterns with respect to a reference sequence of support values for a given threshold limit. In this paper, we propose a novel approach to find similar Temporal patterns followed by a case study. The approach is efficient in terms of space and time as it eliminates repeated scan of Database by computing Temporal frequent patterns or Temporally similar patterns in only a single Database scan.
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a survey on Temporal Databases and data mining
Proceedings of the The International Conference on Engineering & MIS 2015, 2015Co-Authors: Vangipuram Radhakrishna, P V Kumar, V JanakiAbstract:Temporal Database is a Database which captures and maintains past, present and future data. Conventional Databases are not suitable for handling such time varying data. In this context Temporal Database has gained a significant importance in the field of Databases and data mining. The major objective of this research is to perform a detailed survey on Temporal Databases and the various Temporal data mining techniques and explore the various research issues in Temporal data mining. We also throw light on the Temporal association rules and Temporal clustering works carried in literature.
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an approach for mining similarity profiled Temporal association patterns using gaussian based dissimilarity measure
Proceedings of the The International Conference on Engineering & MIS 2015, 2015Co-Authors: Vangipuram Radhakrishna, P V Kumar, V JanakiAbstract:The problem of mining frequent patterns in non-Temporal Databases is studied extensively. Conventional frequent pattern algorithms are not applicable to find Temporal frequent items from the Temporal Databases. Given a reference support time sequence, the problem of mining similar Temporal association patterns has been the current interest among the researchers working in the area of Temporal Databases. The main objective of this research is to propose and validate the suitability of Gaussian distribution based dissimilarity measure to find similar and dissimilar Temporal association patterns of interest. The measure designed serves as similarity measure for finding the similar Temporal association patterns. Finally, in this research, we consider the problem of mining similarity profiled Temporal patterns from the set of time stamped transaction of Temporal Database using proposed measure. We show using a case study how the proposed dissimilarity measure may be used to find the Temporal frequent patterns and compare the same with the work carried in the literature. The proposed measure has the fixed lower bound and upper bound values as 0 and 1 respectively which is its advantage as compared to Euclidean distance measure which has no fixed upper bound.
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A novel approach to discover similar Temporal association patterns in a single Database scan
2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2015Co-Authors: Vangipuram Radhakrishna, P V Kumar, V JanakiAbstract:Mining similar Temporal association patterns from time stamped Temporal Databases is an important research problem focused much recently. The imperative objective of current research is in finding similar Temporal patterns from a given time stamped Temporal Database of transactions by scanning the input Database only once. The objective to find Temporally similar patterns through single scan of Database coins out an important challenge to devise a single Database scan procedure which shall use only support values of singletons computed in the first Database scan, so as to discover all other Temporal patterns. In the current research, we come out with a novel procedure to discover similar Temporal patterns with respect to a reference sequence of support values for a given threshold limit. In this paper, we come up with an innovative approach to find similar Temporal patterns followed by a case study. The approach is efficient in terms of space and time as it eliminates repeated scan of Database by computing Temporal frequent patterns or Temporally similar patterns in only a single Database scan.
Fusheng Wang - One of the best experts on this subject based on the ideXlab platform.
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Archis: An xml-based approach to transaction-time Temporal Database systems
VLDB Journal, 2008Co-Authors: Fusheng Wang, Carlo Zaniolo, Xin ZhouAbstract:Effective support for Temporal applications by Database systems represents an important technical objective that is difficult to achieve since it requires an integrated solution for several problems, including (i) expressive Temporal representations and data models, (ii) powerful languages for Temporal queries and snapshot queries, (iii) indexing, clustering and query optimization techniques for managing Temporal information efficiently, and (iv) architectures that bring together the different pieces of enabling technology into a robust system. In this paper, we present the ArchIS system that achieves these objectives by supporting a Temporally grouped data model on top of RDBMS. ArchIS' architecture uses (a) XML to support Temporally grouped (virtual) representations of the Database history, (b) XQuery to express powerful Temporal queries on such views, (c) Temporal clustering and indexing techniques for managing the actual historical data in a relational Database, and (d) SQL/XML for executing the queries on the XML views as equivalent queries on the relational Database. The performance studies presented in the paper show that ArchIS is quite effective at storing and retrieving under complex query conditions the transaction-time history of relational Databases, and can also assure excellent storage efficiency by providing compression as an option. This approach achieves full-functionality transaction-time Databases without requiring Temporal extensions in XML or Database standards, and provides critical support to emerging application areas such as RFID.
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using xml to build efficient transaction time Temporal Database systems on relational Databases
International Conference on Data Engineering, 2006Co-Authors: Fusheng Wang, Xin Zhou, Carlo ZanioloAbstract:In this paper, we present the ArchIS system that achieves full-functionality transaction-time Databases without requiring Temporal extensions in XML or Database standards. ArchIS architecture uses (a) XML to support Temporally grouped (virtual) representations of the Database history, (b) XQuery to express powerful Temporal queries on such views, (c) Temporal clustering and indexing techniques for managing the actual historical data in a relational Database, and (d) SQL/XML for executing the queries on the XML views as equivalent queries on the relational Database. The performance studies presented in the paper show that ArchIS is quite effective at storing and retrieving under complex query conditions the transaction-time history of relational Databases.
Xuetong Xie - One of the best experts on this subject based on the ideXlab platform.
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a problem oriented approach to data mining in distributed spatio Temporal Database
International Conference on Conceptual Structures, 2007Co-Authors: Zhou Huang, Xia Peng, Yu Fang, Bin Chen, Xuetong XieAbstract:Recently, a fast increment of spatio-Temporal data volume has been achieved and more importantly the data might distribute everywhere. So, there is a need for spatio-Temporal data mining systems that are able to support such distributed spatio-Temporal query and analysis operations. Distributed spatio-Temporal data mining technologies were discussed in this paper. After discussing the process of spatio-Temporal data mining in distributed environment, one actual DSTDMS (Distributed Spatio-Temporal Data Mining System) was designed and then implemented. The system is based on data model of sequent snapshot and accomplished through spatio-Temporal extension on PostgreSQL. Various spatio-Temporal analyses and mining queries could be carried out in the system through simple SQL statements. By using the system, effective mining of distributed spatio-Temporal data were achieved.
