The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Abdullah Uz Tansel - One of the best experts on this subject based on the ideXlab platform.

  • Nested bitemporal Relational Data Model
    2008
    Co-Authors: Abdullah Uz Tansel, Theodore Brown, Canan Eren
    Abstract:

    In this dissertation, we propose a nested bitemporal Relational Data Model, using nested relations, and we also develop algebra and calculus languages for this Model. The fundamental construct for representing temporal Data is a bitemporal atom that consists of five parts: a value, its validity period, and the time this Data is recorded in the Database. Bitemporal Data is attached to attributes, and arbitrary levels of nesting are allowed. The algebra includes operations to manipulate bitemporal Data, to restructure nested bitemporal relations, and to rollback Database to a designated state in the past. We have also defined the concept of 'context' for using bitemporal Data: bitemporal context, historical context, and current context. BtSQL, a preprocessor for the bitemporal SOL language, query syntax allows end users to incorporate bitemporal, current and historical context. We defined and implemented BtSQL for the specification of bitemporal queries in different context. It translates a bitemporal query specification to a standard SQL statement. BtSQL includes select, insert, delete, and update statements of SQL extended for bitemporal Relational Databases. A prototype implementation has been completed in an object-Relational Database system to demonstrate the feasibility of the Model defined in this thesis.

  • On handling time-varying Data in the Relational Data Model
    Information and Software Technology, 2004
    Co-Authors: Abdullah Uz Tansel
    Abstract:

    Abstract The article addresses the key issues in Modeling and querying temporal Data within the Relational framework. These issues include representation of temporal Data, temporal grouping (object) identifiers and primary keys of temporal relations, temporal integrity constraints, and fundamental operations of a temporal query language. The paper develops taxonomy for temporal Relational Databases and establishes criteria to evaluate various proposed extensions to the Relational Data Model. We expect that it will benefit the researchers and guide the practitioners in choosing the right approach for managing temporal Data in their applications.

  • Temporal Relational Data Model
    IEEE Transactions on Knowledge and Data Engineering, 1997
    Co-Authors: Abdullah Uz Tansel
    Abstract:

    This paper incorporates a temporal dimension to nested relations. It combines research in temporal Databases and nested relations for managing the temporal Data in nontraditional Database applications. A temporal Data value is represented as a temporal atom; a temporal atom consists of two parts: a temporal set and a value. The temporal atom asserts that the value is valid over the time duration represented by its temporal set. The Data Model allows relations with arbitrary levels of nesting and can represent the histories of objects and their relationships. Temporal Relational algebra and calculus languages are formulated and their equivalence is proved. Temporal Relational algebra includes operations to manipulate temporal Data and to restructure nested temporal relations. Additionally, we define operations to generate a power set of a relation, a set membership test, and a set inclusion test, which are all derived from the other operations of temporal Relational algebra. To obtain a concise representation of temporal Data (temporal reduction), collapsed versions of the set-theoretic operations are defined. Procedures to express collapsed operations by the regular operations of temporal Relational algebra are included. The paper also develops procedures to completely flatten a nested temporal relation into an equivalent 1 NF relation and back to its original form, thus providing a basis for the semantics of the collapsed operations by the traditional operations on 1 NF relations

Yike Guo - One of the best experts on this subject based on the ideXlab platform.

  • BigData Congress - BigData Oriented Open Scalable Relational Data Model
    2014 IEEE International Congress on Big Data, 2014
    Co-Authors: Zhiyun Zheng, Yike Guo
    Abstract:

    Data Model research is a fundamental part of the field of big Data, and strongly supports the higher levels of Database construction, Data access, Data analysis and Data mining. This paper considers the shortcomings of traditional Databases such as limited support for Data types, low concurrent query performance and lack of horizontal scalability. And based on this, it proposes an Open Scalable Relational Data Model (OSRDM), which provides open support for varieties of Data types and enables complete horizontal scalability on the basis of keeping and extending the Relational descriptive power of traditional Relational Data Models. It caters for the characteristics of big Data such as volume, variety and velocity. Finally, the paper analyzes and evaluates OSRDM by comparing it with popular Data Models from two perspectives: functional features and performance indicators. The results show the superiority of OSRDM.

  • BigData Oriented Open Scalable Relational Data Model
    2014 IEEE International Congress on Big Data, 2014
    Co-Authors: Zhiyun Zheng, Yike Guo
    Abstract:

    Data Model research is a fundamental part of the field of big Data, and strongly supports the higher levels of Database construction, Data access, Data analysis and Data mining. This paper considers the shortcomings of traditional Databases such as limited support for Data types, low concurrent query performance and lack of horizontal scalability. And based on this, it proposes an Open Scalable Relational Data Model (OSRDM), which provides open support for varieties of Data types and enables complete horizontal scalability on the basis of keeping and extending the Relational descriptive power of traditional Relational Data Models. It caters for the characteristics of big Data such as volume, variety and velocity. Finally, the paper analyzes and evaluates OSRDM by comparing it with popular Data Models from two perspectives: functional features and performance indicators. The results show the superiority of OSRDM.

  • Polymorphic type framework for scientific workflows with Relational Data Model
    International Journal of Business Process Integration and Management, 2010
    Co-Authors: Vasa Curcin, Moustafa Ghanem, Yike Guo
    Abstract:

    Scientific workflow systems provide languages for representing complex scientific processes as decompositions into lower level tasks, down to the level of atomic, executable units. To support Data analysis activities, a wide variety of such languages represent Data transformation and processing operations as task nodes within a workflow. Adding Data type information to the task inputs and outputs allows workflow authors to perform type checking at design time, search for compatible nodes in public component repositories and define specifications of abstract workflows. Introducing support for strict Data typing simplifies the implementation of a workflow system in addressing these issues, but at the expense of losing flexibility. We address this challenge by introducing workflow type signatures suitable for use in registries and for type matching, and developing a polymorphic type inference over compositions of such signatures. The focus is on the Relational Data Model, popular in Data analysis workflow systems, and the techniques introduced are validated by applying the inference engine prototype to an adverse drug reaction study implemented in the Relational algebra subset of the Discovery Net workflow system.

Wang Sheng - One of the best experts on this subject based on the ideXlab platform.

  • A TRANSLATOR TO CONVERT Relational Data Model TO XML DOCUMENTS
    Computer Engineering, 2001
    Co-Authors: Wang Sheng
    Abstract:

    The XML technology has very close relationship with Relational Data Models. It has realistic significance to convert the Relational Data formats to XML documents. This paper mainly discusses how to develop a translator with OmniMark programing language, and analyses the key technologies, including the relationship between relationship schemas, DTD and XML schemas, platform independence and SGML syntax checking.

Daryl Porter - One of the best experts on this subject based on the ideXlab platform.

  • A probabilistic Relational Data Model
    Lecture Notes in Computer Science, 1
    Co-Authors: Daniel Barbará, Hector Garcia-molina, Daryl Porter
    Abstract:

    It is often desirable to represent in a Database entities whose properties cannot be deterministically classified. We develop a new Data Model that includes probabilities associated with the values of the attributes. The notion of missing probabilities is introduced for partially specified probability distributions. This new Model offers a richer descriptive language allowing the Database to reflect more accurately the uncertain real world. Probabilistic analogs to the basic Relational operators are defined and their correctness is studied.

Joong-hee Park - One of the best experts on this subject based on the ideXlab platform.

  • NCM - Interoperability between a Relational Data Model and an RDF Data Model
    2010
    Co-Authors: Mi-young Choi, Chang-joo Moon, Doo-kwon Baik, Young-jun Wie, Joong-hee Park
    Abstract:

    The growing demand for a Semantic Web is focusing attention on Semantic Web trials that use a huge Relational Database (RDB), which is the backbone of IT. The resource description framework (RDF) is a language system that enables the information of resources on the Web to be written clearly and logically. Correct writing and use of the RDF are the core of the Semantic Web. It is possible to build a Semantic Web without RDB intervention. However, the use of an existing RDB Data requires automatic transformation of RDB Data into an RDF statement. This paper proposes an interoperable Data Model called a Relational between RDF interoperable Data Model (R2iDM), which can generate an RDF statement with table Data and Structured Query Language without having to read the meta-information of an RDB table. With the R2iDM, there is a reduction in the peripheral attributes (but not the primary attributes) as well as the necessary attributes for RDF transformation. In short, the R2iDM is a very simplified Data Model for interoperability between a Relational Data Model and the RDF. The paper also shows how R2iDM is mapped to the RDF. The mapping method is more advanced than existing simple methods of mapping between an RDB and the RDF.

  • Interoperability between a Relational Data Model and an RDF Data Model
    The 6th International Conference on Networked Computing and Advanced Information Management, 2010
    Co-Authors: Mi-young Choi, Chang-joo Moon, Doo-kwon Baik, Young-jun Wie, Joong-hee Park
    Abstract:

    The growing demand for a Semantic Web is focusing attention on Semantic Web trials that use a huge Relational Database (RDB), which is the backbone of IT. The resource description framework (RDF) is a language system that enables the information of resources on the Web to be written clearly and logically. Correct writing and use of the RDF are the core of the Semantic Web. It is possible to build a Semantic Web without RDB intervention. However, the use of an existing RDB Data requires automatic transformation of RDB Data into an RDF statement. This paper proposes an interoperable Data Model called a Relational between RDF interoperable Data Model (R2iDM), which can generate an RDF statement with table Data and Structured Query Language without having to read the meta-information of an RDB table. With the R2iDM, there is a reduction in the peripheral attributes (but not the primary attributes) as well as the necessary attributes for RDF transformation. In short, the R2iDM is a very simplified Data Model for interoperability between a Relational Data Model and the RDF. The paper also shows how R2iDM is mapped to the RDF. The mapping method is more advanced than existing simple methods of mapping between an RDB and the RDF.

  • The RDFS mapping for recursive relationship of Relational Data Model
    2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2010
    Co-Authors: Mi-young Choi, Chang-joo Moon, Doo-kwon Baik, Young-jun Wie, Joong-hee Park
    Abstract:

    The growing demand for a Semantic Web is focusing attention on Semantic Web trials that use a huge amount of Data from a Relational Database (RDB). Hence, the literature on the transformation of RDB Data to resource description framework (RDF) statements has consequently become very important. The Relational Data Model (RDM), the base of RDB, represents real world Data using relationships [1]. In existing research, the mapping of RDB Data to RDF only generates an RDF triple statement from an RDB table structure in a flat level; existing research does not consider the mapping of RDM which describes the business rules. In RDM, the relationship describes real world business rules by connecting the two entities. If one entity relates to itself, then it has a recursive relationship which helps to explain the logical Data Model and extend its flexibility. The recursive relationship is a necessary relation type of most Data Models to describe more complex business rules. This paper suggests a mapping method to map the two types of recursive relationships to the resource description framework schema (RDFS). One type of recursive relationship is generated during the integration process with different entities at different levels in a hierarchical structure while the other is generated at the hierarchical structure between the instances of an attribute in an entity. The transformed RDFS, including the class, subclasses, and subproperties minimizes the loss of Data meaning and enables the process of the inference function to be used with RDB Data. The method of transforming RDB Data to inferable, transitive RDF/RDFS with SQL implies that the most difficult step of writing a Semantic Web can be automated. In short, the proposed method enables a Semantic Web to be built with greater efficiency.

  • SOCA - The RDFS mapping for recursive relationship of Relational Data Model
    2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2010
    Co-Authors: Mi-young Choi, Chang-joo Moon, Doo-kwon Baik, Young-jun Wie, Joong-hee Park
    Abstract:

    The growing demand for a Semantic Web is focusing attention on Semantic Web trials that use a huge amount of Data from a Relational Database (RDB). Hence, the literature on the transformation of RDB Data to resource description framework (RDF) statements has consequently become very important. The Relational Data Model (RDM), the base of RDB, represents real world Data using relationships [1]. In existing research, the mapping of RDB Data to RDF only generates an RDF triple statement from an RDB table structure in a flat level; existing research does not consider the mapping of RDM which describes the business rules. In RDM, the relationship describes real world business rules by connecting the two entities. If one entity relates to itself, then it has a recursive relationship which helps to explain the logical Data Model and extend its flexibility. The recursive relationship is a necessary relation type of most Data Models to describe more complex business rules. This paper suggests a mapping method to map the two types of recursive relationships to the resource description framework schema (RDFS). One type of recursive relationship is generated during the integration process with different entities at different levels in a hierarchical structure while the other is generated at the hierarchical structure between the instances of an attribute in an entity. The transformed RDFS, including the class, subclasses, and subproperties minimizes the loss of Data meaning and enables the process of the inference function to be used with RDB Data. The method of transforming RDB Data to inferable, transitive RDF/RDFS with SQL implies that the most difficult step of writing a Semantic Web can be automated. In short, the proposed method enables a Semantic Web to be built with greater efficiency.