In this paper, we employ a novel approach to metarule-guided, multi-dimensional association rule mining which explores a data cube structure. We propose algorithms for metarule-guided mining: given a metarule containi...
详细信息
A mediator join index (MJI) is proposed to speed up N-way inter-database joins by reducing the amount of data transfer during evaluation. A family of algorithms, the query scrubbing algorithms (QSA), are developed to ...
详细信息
A mediator join index (MJI) is proposed to speed up N-way inter-database joins by reducing the amount of data transfer during evaluation. A family of algorithms, the query scrubbing algorithms (QSA), are developed to maintain MJI and to evaluate queries using MJI. QSA algorithms use query scrubbing to cope with update and query anomalies related to materialized views in the mediator context. Compared with existing algorithms, QSA algorithms incur less overhead in handling the anomalies and makes MJI a promising technique for efficient mediator query processing.
The AURORA mediator system employs a novel 2-tier, plug-and-play mediation model that is designed to facilitate access to a large number of heterogeneous data sources. The paper describes AURORA's mediation model ...
详细信息
The AURORA mediator system employs a novel 2-tier, plug-and-play mediation model that is designed to facilitate access to a large number of heterogeneous data sources. The paper describes AURORA's mediation model and a suite of techniques used by a specific AURORA mediator, AURORA-RH. This suite includes a mediation methodology provided via an interactive mediator author's toolkit (MAT), a mediation enabling algebra, a query rewriting algorithm, and transformation rules that facilitate query optimization.
Most databaseresearch on modeling time has concentrated on the definition of a particular temporal model and its incorporation into a (relational or object) database management system. This has resulted in quite a la...
详细信息
Most databaseresearch on modeling time has concentrated on the definition of a particular temporal model and its incorporation into a (relational or object) database management system. This has resulted in quite a large number of different temporal models, each providing a specific set of temporal features. This paper presents an object-oriented framework for temporal models which supports multiple notions of time. The framework can be used to accommodate the temporal needs of different applications and to derive existing temporal models by making a series of design decisions through subclass specialization. It can also be used to derive a series of new, more general temporal models that meet the needs of a growing number of emerging applications.
An important feature to be considered in the design of a multimedia database system (MMDBS) is content based retrieval of images. Spatial features represent the spatial relationships among objects in an image. The sal...
详细信息
An important feature to be considered in the design of a multimedia database system (MMDBS) is content based retrieval of images. Spatial features represent the spatial relationships among objects in an image. The salient objects (interesting objects) can be organized in an object hierarchy, based on object oriented concepts. The paper proposes an indexing scheme, called 2D-h trees, for content based retrieval of images. This scheme organizes the representations of the spatial relationships among objects in images and the hierarchical relationships among objects efficiently for query optimization. Our performance analysis indicates that the 2D-h-tree is an efficient index scheme for content based retrieval of images.
Data mining is a promising field in which research and development activities are fiourishing. It is also a young field with vast, unexplored territories. How can we contribute significantly to this fast expanding, mu...
详细信息
ISBN:
(纸本)0818678070
Data mining is a promising field in which research and development activities are fiourishing. It is also a young field with vast, unexplored territories. How can we contribute significantly to this fast expanding, multi-disciplinary field? This panel will bring databaseresearchers together to share different views and insights on the issues in the field.
Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induc...
详细信息
Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. The paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient, high quality, multiple level classification of large amounts of data, the relaxation of the requirement of perfect training sets, and the elegant handling of continuous and noisy data.
We develop a 2-tier, plug-and-play mediation model for accessing a large number of heterogeneous data sources. This model defines a divide-and-conquer approach towards information integration. It is more suitable for ...
We develop a 2-tier, plug-and-play mediation model for accessing a large number of heterogeneous data sources. This model defines a divide-and-conquer approach towards information integration. It is more suitable for applications such as electronic commerce than existing models. We also develop algebras that manipulate heterogeneous data, the mediation enabling algebras, that provide new techniques for efficient query processing in large-scale middleware. This paper presents the mediation model, architecture and techniques studied in the AURORA project.
Spatial data mining is to mine high-level spatial information and knowledge from large spatial databases. A spatial data mining system prototype, GeoMiner, has been designed and developed based on our years of experie...
ISBN:
(纸本)9780897919111
Spatial data mining is to mine high-level spatial information and knowledge from large spatial databases. A spatial data mining system prototype, GeoMiner, has been designed and developed based on our years of experience in the research and development of relational data mining system, DBMiner, and our research into spatial data mining. The data mining power of GeoMiner includes mining three kinds of rules: characteristic rules, comparison rules, and association rules, in geo-spatial databases, with a planned extension to include mining classification rules and clustering rules. The SAND (Spatial And Nonspatial Data) architecture is applied in the modeling of spatial databases, whereas GeoMiner includes the spatial data cube construction module, spatial on-line analytical processing (OLAP) module, and spatial data mining modules. A spatial data mining language, GMQL (Geo-Mining Query Language), is designed and implemented as an extension to Spatial SQL [3], for spatial data mining. Moreover, an interactive, user-friendly data mining interface is constructed and tools are implemented for visualization of discovered spatial knowledge.
暂无评论