data-centric storage is an effective and important technique in the wireless sensor networks. It stores the sensing data according to their values by mapping them to some point in the network in order to avoid routing...
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ISBN:
(纸本)9781595939111
data-centric storage is an effective and important technique in the wireless sensor networks. It stores the sensing data according to their values by mapping them to some point in the network in order to avoid routing all the values outside the network and flooding the queries. However, in most data-centric storage schemes, there is a "hotspot" problem due to the skewness of data and randomness of the mapping functions. Large number of sensor readings (events) may be routed to the same point by the predefined hashed function. In this paper, we propose a new Dynamic BAlanced data-centric Storage (DBAS) scheme, a cooperative strategy between the base station and the in-network processing in wireless sensor network. Our scheme, which utilizes the rich resources in the base station and is aware of the data distributions of the network, dynamically adjusts the mappings from readings to the storage points to balance the storage and workload in the network, as well as to reduce the cost of storing these readings. Moreover, it takes advantage of perimeter routing algorithm of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that DBAS is more balanced and energy efficient than the traditional data-centric storage mechanism in wireless sensor network.
We study the problem of answering queries given a set of mappings between peer ontologies. In addition to the schema mapping between peer ontologies, there are axioms to give constraints to classes and properties. We ...
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In domain ontology, Semantic Association(SA) is used to depict the correlation between two concepts. In this paper, we define Semantic Association Degree(SAD) for measuring SA in the domain ontology. We first present ...
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Ontology matching determines the correspondences between concepts and relations of related ontologies. In this paper, we put forward an ontology hierarchies matching approach based on lattices alignment. The proposed ...
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Ontology matching determines the correspondences between concepts and relations of related ontologies. In this paper, we put forward an ontology hierarchies matching approach based on lattices alignment. The proposed lattice-based matching algorithm can be utilized not only in matching processes between two ontologies, but also in annotation processes between an ontology and its corresponding resources. Experiments on spatiotemporal ontology annotation have been carried out which shown the applicability of the approach.
Both WordNet and Chinese Classified Thesaurus(CCT) are widely used in information retrieval and management systems. In this paper we propose a novel approach for building bilingual ontologies based on these existing k...
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It widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p1, p2, , ...
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As the memory capacity increases and the hardware becomes cheaper, main memory databases (MMDB) have come true and been used in more and more applications, because they can provide better response time and throughputs...
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In domain ontologies, there is usually no weight assigned to the link between two concepts. This has been considered as one of main obstacles in using ontologies. Semantic Association (SA) is to depict the correlation...
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In domain ontologies, there is usually no weight assigned to the link between two concepts. This has been considered as one of main obstacles in using ontologies. Semantic Association (SA) is to depict the correlation of two concepts, and can be measured as the weight of the link. In this paper, we defined Degree of Association (DOA) to measure SA from a concept to its direct-related concept in domain ontology, and proposed a Language-Model-Based Method (LMBM) to compute DOA. Our idea comes from the intuition that the semantic relationship between two concepts implies certain semantic association of them. We took probabilistic model for computing DOA, and used Maximum Likelihood Estimation to estimate parameters. We tested the proposed method on two different domain ontologies, and applied it in experiments of semantic query expansion. Experimental results show the benefit of our approach and demonstrate the promising effectiveness over semantic query expansion.
In contextual information retrieval, the retrieval of information depends on the time and place of submitting query, history of interaction, task in hand, and many other factors that are not given explicitly but impli...
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In contextual information retrieval, the retrieval of information depends on the time and place of submitting query, history of interaction, task in hand, and many other factors that are not given explicitly but implicitly lie in the interaction and surroundings of searching, namely the context. User's cognition is one of important contextual factors for understanding his or her personal needs. We propose a model called DOSAM to get user's individual cognitive structure on domain knowledge. DOSAM is developed from the spreading-activation model of psychology and is established on the domain ontology. The cost analysis of algorithm shows that it is feasible to get cognitive structure by DOSAM. Personalized search experimental results on digital library indicate that DOSAM can help improve the search effectiveness and user's satisfaction.
Association rule mining is one of the most important and basic technique in data mining, which has been studied extensively and has a wide range of applications. However, as traditional data mining algorithms usually ...
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Association rule mining is one of the most important and basic technique in data mining, which has been studied extensively and has a wide range of applications. However, as traditional data mining algorithms usually only focus on analyzing data organized in single table, applying these algorithms in multi-relational data environment will result in many problems. This paper summarizes these problems, proposes a framework for the mining of multi-relational association rule, and gives a definition of the mining task. After classifying the existing work into two categories, it describes the main techniques used in several typical algorithms, and it also makes comparison and analysis among them. Finally, it points out some issues unsolved and some future further research work in this area.
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