Plenty of structured data existing on the grid need to be managed by databases, but there is little work available on grid-enabled databases. Two problems lie in current research: the first is the deficiency of a unif...
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ISBN:
(纸本)184000025
Plenty of structured data existing on the grid need to be managed by databases, but there is little work available on grid-enabled databases. Two problems lie in current research: the first is the deficiency of a uniform management and access method to various kinds of databases; the second is that query efficiency decreases rapidly with the increase of the amount of data records. Metadata plays a critical role in grid databases integration, so a layered metadata model is put forward which comprises information of data storage, data properties and relations between data objects. Through the metadata model, the distributed, heterogeneous data resources can be accessed uniformly and efficiently. At last an example of hospital information database system proves the validity and feasibility of our model.
Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose...
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Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose a new concept of probability information distance (PID) and prove that the PID satisfies four requests of axiomatization of the distance. So the PID is a kind of distance measure, which can be used to measure the degree of variation between two random variables. We make the PID be separability criterion of the classes for information feature extraction, and call it PID criterion (PIDC). Based on PIDC, we design a novel algorithm for information feature extraction. Compared with principal components analysis (PCA), correlation analysis etc., the algorithm put forward in this paper had regarded for the class information, and so it is a kind of supervised algorithm of feature extraction. The experimental results demonstrate that the algorithm is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition.
A formal representation of ontologies is proposed, based on F-logic and O-logic; and the works in the building of ontologies in NKI. An ontology includes class frames, slot frames, class-slot frames, object frames and...
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A formal representation of ontologies is proposed, based on F-logic and O-logic; and the works in the building of ontologies in NKI. An ontology includes class frames, slot frames, class-slot frames, object frames and axioms. The value restrictions of slots are defined in slot frames. For each slot and each class, there is a class-slot frame representing the specific value restrictions of the slot when defining the class; and the relations between class-slot frames and slot frames are discussed. For a slot in a class frame, its values are inherited to its subclasses without blocking; and its default values are inherited to its subclasses taking overriding, revising and conflict resolution into account. After giving the formal representation of ontologies, the semantics of ontologies are discussed, and main results are presented.
Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas con...
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Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas concerned with information function and information entropy of Shannon information theory, consider the inherent characteristic of eigenvalues of matrix, two new concepts of possibility information function (PIF) and possibility information entropy (PIE) are proposed firstly. On the basis of these, the formulae of information rate (IR) and accumulated information rate (AIR) are set up, by which the degree of information compression is measured. In the end, we improve the PCA algorithm called improved principal component analysis (PCA). Through simulated application in practice, the results show that the IPCA proposed here is efficient and satisfactory. It provides a new research approach of information feature compression for pattern recognition.
The key issue of peer data management systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers...
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The key issue of peer data management systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers to avoid network flooding. This paper proposes a semantic-based PDMS model, called R-Chord, by deploying the resource space model above the Chord overlay for uniformly, normally and effectively organizing and managing resources distributed in P2P networks. Experiments show that, compared to the Chord model, the R-Chord model is more flexible to support semantic-based queries and can significantly decrease the average visiting number of and visiting times on peers for answering queries.
A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the ...
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A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the operation of knowledge flow processes. This paper proposes the notion of knowledge energy as the driving cause of forming an autonomous knowledge flow network, and explores the behind principles. Knowing these principles can help team managers and the support systems improve cooperation by monitoring the knowledge energy of nodes, and by evaluating and adjusting knowledge flows. A knowledge flow network management system can be the high layer of the knowledge grid to help improve the efficiency of distributed knowledge-intensive teamwork.
An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for const...
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ISBN:
(纸本)158113844X
An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for constructing the semantic link network. The basic premise of this work is that the semantics of a web page can be reflected by a set of keywords, and the semantic relationship between two web pages can be determined by the semantic relationship between their keyword sets. The approach adopts the data mining algorithms to discover the semantic relationships between keyword sets, and then uses deductive and analogical reasoning to enrich the semantic relationships. The proposed algorithms have been implemented. Experiment shows that the approach is feasible.
The future Web cart be imagined as a life network consisting of resource nodes and semantic relationship links between them. Any node has a life span from birth - adding it to the network - to death - removing it from...
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ISBN:
(纸本)158113844X
The future Web cart be imagined as a life network consisting of resource nodes and semantic relationship links between them. Any node has a life span from birth - adding it to the network - to death - removing it from the network. Through establishing and investigating two types of models for such a network, we obtain the same scale free distribution of semantic links. Simulations and comparisons validate the rationality of the proposed models.
An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for const...
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ISBN:
(纸本)1581139128
An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for constructing the semantic link network. The basic premise of this work is that the semantics of a web page can be reflected by a set of keywords, and the semantic relationship between two web pages can be determined by the semantic relationship between their keyword sets. The approach adopts the data mining algorithms to discover the semantic relationships between keyword sets, and then uses deductive and analogical reasoning to enrich the semantic relationships. The proposed algorithms have been implemented. Experiment shows that the approach is feasible.
The future Web can be imagined as a life network consisting of resource nodes and semantic relationship links between them. Any node has a life span from birth -?adding it to the network -?to death -?removing it from ...
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ISBN:
(纸本)1581139128
The future Web can be imagined as a life network consisting of resource nodes and semantic relationship links between them. Any node has a life span from birth -?adding it to the network -?to death -?removing it from the network. Through establishing and investigating two types of models for such a network, we obtain the same scale free distribution of semantic links. Simulations and comparisons validate the rationality of the proposed models.
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