The Experts below are selected from a list of 279339 Experts worldwide ranked by ideXlab platform

Lois Delcambre - One of the best experts on this subject based on the ideXlab platform.

  • Using the uni-Level Description (ULD) to support data-model interoperability
    Data & Knowledge Engineering, 2006
    Co-Authors: Shawn Bowers, Lois Delcambre
    Abstract:

    We describe a framework called the Uni-Level Description (ULD) for accurately representing information from a broad range of data models. The ULD extends previous meta-data-model approaches by: (a) providing uniform representation and access to data model, schema, and data, and (b) supporting data models with non-traditional schema arrangements, including those that allow optional and multiple Levels of schema. Because the ULD is a flat, first-order representation, we show how Datalog over the ULD can provide a flexible mechanism to query, extract, and transform reformation from data sources that exhibit various types of structural heterogeneity.

  • The uni-Level Description: a uniform framework for managing structural heterogeneity
    2004
    Co-Authors: Shawn Bowers, Lois Delcambre
    Abstract:

    Information management systems (such as database and knowledge-based systems) are based on data models, which provide basic structures for organizing and storing data. A number of data models are in use, and each provides a slightly different set of structures. For instance, information is represented as tables in the relational data model, as ordered trees in semi-structured data models (such as XML), and as directed graphs in semantic networks (such as RDF). With several distinct data models available, developers can select the most convenient representation for their particular need. However, the use of multiple data models introduces structural heterogeneity, making it difficult to combine information and exploit generic tools (for example, for querying or browsing). This dissertation describes a new framework called the Uni-Level Description (ULD) that can accurately store and accommodate information from a broad range of data models. The ULD consists of a meta-data-model to describe the basic data structures used by a data model, and a uniform representation to store information within a source, including the data-model structures, schema (if present), and instance data. The ULD extends existing meta-data-models by allowing uniform access to schema and data, by permitting data models with non-traditional schema arrangements, and by providing a language for representing data-model constraints. The two primary motivations for our work are (1)to study the basic structural capabilities offered by data models and (2)to define languages for enabling interoperability among sources with structural heterogeneity. We use the ULD to describe a wide range of data models, including those that allow optional and partial schema. We present a query and transformation language (based on Datalog) for accessing and converting information among heterogeneous sources. We demonstrate the flexibility of the transformation language by defining a number of structural mappings. And finally, we use the ULD as the basis for generic navigation, where a single set of operators can be used to discover and browse information in structurally heterogeneous sources.

  • ER - The Uni-Level Description: A Uniform Framework for Representing Information in Multiple Data Models
    Conceptual Modeling - ER 2003, 2003
    Co-Authors: Shawn Bowers, Lois Delcambre
    Abstract:

    One advantage of having several different representation schemes and data models is that users can select the right representation and associated tools for their particular need. However, multiple representations introduce structural, model-based heterogeneity, making it difficult to combine information from different sources and exploit information using generic tools (e.g., for querying or browsing). In this work, we define a uniform representation based on a meta-data-model called the Uni-Level Description (ULD) that can accommodate and accurately store information in a broad range of data models. The ULD defines three distinct instance-of relationships plus a relationship for modeling conformance, which is used to connect (data) constructs to other (schema) constructs and can be constrained to reflect the requirements of the data model. The ULD has been shown to enable powerful, generic transformation rules and simple generic browsing capability over information represented in diverse data models and representation schemes.

Shawn Bowers - One of the best experts on this subject based on the ideXlab platform.

  • Using the uni-Level Description (ULD) to support data-model interoperability
    Data & Knowledge Engineering, 2006
    Co-Authors: Shawn Bowers, Lois Delcambre
    Abstract:

    We describe a framework called the Uni-Level Description (ULD) for accurately representing information from a broad range of data models. The ULD extends previous meta-data-model approaches by: (a) providing uniform representation and access to data model, schema, and data, and (b) supporting data models with non-traditional schema arrangements, including those that allow optional and multiple Levels of schema. Because the ULD is a flat, first-order representation, we show how Datalog over the ULD can provide a flexible mechanism to query, extract, and transform reformation from data sources that exhibit various types of structural heterogeneity.

  • The uni-Level Description: a uniform framework for managing structural heterogeneity
    2004
    Co-Authors: Shawn Bowers, Lois Delcambre
    Abstract:

    Information management systems (such as database and knowledge-based systems) are based on data models, which provide basic structures for organizing and storing data. A number of data models are in use, and each provides a slightly different set of structures. For instance, information is represented as tables in the relational data model, as ordered trees in semi-structured data models (such as XML), and as directed graphs in semantic networks (such as RDF). With several distinct data models available, developers can select the most convenient representation for their particular need. However, the use of multiple data models introduces structural heterogeneity, making it difficult to combine information and exploit generic tools (for example, for querying or browsing). This dissertation describes a new framework called the Uni-Level Description (ULD) that can accurately store and accommodate information from a broad range of data models. The ULD consists of a meta-data-model to describe the basic data structures used by a data model, and a uniform representation to store information within a source, including the data-model structures, schema (if present), and instance data. The ULD extends existing meta-data-models by allowing uniform access to schema and data, by permitting data models with non-traditional schema arrangements, and by providing a language for representing data-model constraints. The two primary motivations for our work are (1)to study the basic structural capabilities offered by data models and (2)to define languages for enabling interoperability among sources with structural heterogeneity. We use the ULD to describe a wide range of data models, including those that allow optional and partial schema. We present a query and transformation language (based on Datalog) for accessing and converting information among heterogeneous sources. We demonstrate the flexibility of the transformation language by defining a number of structural mappings. And finally, we use the ULD as the basis for generic navigation, where a single set of operators can be used to discover and browse information in structurally heterogeneous sources.

  • ER - The Uni-Level Description: A Uniform Framework for Representing Information in Multiple Data Models
    Conceptual Modeling - ER 2003, 2003
    Co-Authors: Shawn Bowers, Lois Delcambre
    Abstract:

    One advantage of having several different representation schemes and data models is that users can select the right representation and associated tools for their particular need. However, multiple representations introduce structural, model-based heterogeneity, making it difficult to combine information from different sources and exploit information using generic tools (e.g., for querying or browsing). In this work, we define a uniform representation based on a meta-data-model called the Uni-Level Description (ULD) that can accommodate and accurately store information in a broad range of data models. The ULD defines three distinct instance-of relationships plus a relationship for modeling conformance, which is used to connect (data) constructs to other (schema) constructs and can be constrained to reflect the requirements of the data model. The ULD has been shown to enable powerful, generic transformation rules and simple generic browsing capability over information represented in diverse data models and representation schemes.

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

  • Unbalanced region matching based on two-Level Description for image retrieval
    Pattern Recognition Letters, 2005
    Co-Authors: Shengyang Dai, Yu-jin Zhang
    Abstract:

    Research on integrating spatial information into content-based image retrieval (CBIR) is aimed at solving the problem caused by global feature based algorithm. Most systems derive the spatial information from image segmentation. However, the Description of images based on one-Level segmentation (OLD) and the inevitable inaccuracy of segmentation results seriously limit the performance. A two-Level Description (TLD) describes images by a rough Description and a detailed Description to avoid improper spatial constraint caused by OLD is proposed. Similarity measurement based on unbalanced region matching (URM) is introduced to take advantage of TLD to reduce the influence of segmentation. A novel spatial descriptor integrating shape, size, and density as well as position and spatial layout information together is also proposed. The performance of the integrated system is illustrated by experimental results with 1000 query images randomly selected from a database of 10,000 general-purpose images.

Shengyang Dai - One of the best experts on this subject based on the ideXlab platform.

  • Unbalanced region matching based on two-Level Description for image retrieval
    Pattern Recognition Letters, 2005
    Co-Authors: Shengyang Dai, Yu-jin Zhang
    Abstract:

    Research on integrating spatial information into content-based image retrieval (CBIR) is aimed at solving the problem caused by global feature based algorithm. Most systems derive the spatial information from image segmentation. However, the Description of images based on one-Level segmentation (OLD) and the inevitable inaccuracy of segmentation results seriously limit the performance. A two-Level Description (TLD) describes images by a rough Description and a detailed Description to avoid improper spatial constraint caused by OLD is proposed. Similarity measurement based on unbalanced region matching (URM) is introduced to take advantage of TLD to reduce the influence of segmentation. A novel spatial descriptor integrating shape, size, and density as well as position and spatial layout information together is also proposed. The performance of the integrated system is illustrated by experimental results with 1000 query images randomly selected from a database of 10,000 general-purpose images.

Shuqiang Jiang - One of the best experts on this subject based on the ideXlab platform.

  • exciting event detection in broadcast soccer video with mid Level Description and incremental learning
    ACM Multimedia, 2005
    Co-Authors: Qingming Huang, Wen Gao, Shuqiang Jiang
    Abstract:

    In this paper, we propose a method for exciting event detection in broadcast soccer video with mid-Level Description and SVM-based incremental learning. In the method, video frames are firstly classified and grouped into views in terms of low-Level playfield features. Mid-Level Description including view label, motion descriptor and shot descriptor are then extracted to present the characteristics of a view. By using the fixed temporal structure of views, SVM classification models are constructed to detected exciting events in a soccer match. In the view classification and event detection procedures, SVM-based incremental learning method is explored to improve the extensibility of view classification and event detection. Experiments on real soccer video programs demonstrate encouraging results.

  • ACM Multimedia - Exciting event detection in broadcast soccer video with mid-Level Description and incremental learning
    Proceedings of the 13th annual ACM international conference on Multimedia - MULTIMEDIA '05, 2005
    Co-Authors: Qingming Huang, Wen Gao, Shuqiang Jiang
    Abstract:

    In this paper, we propose a method for exciting event detection in broadcast soccer video with mid-Level Description and SVM-based incremental learning. In the method, video frames are firstly classified and grouped into views in terms of low-Level playfield features. Mid-Level Description including view label, motion descriptor and shot descriptor are then extracted to present the characteristics of a view. By using the fixed temporal structure of views, SVM classification models are constructed to detected exciting events in a soccer match. In the view classification and event detection procedures, SVM-based incremental learning method is explored to improve the extensibility of view classification and event detection. Experiments on real soccer video programs demonstrate encouraging results.