Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering ana...
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ISBN:
(纸本)9783540717027
Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering analysis is becoming one of the most important requirements. In this paper, we present the problem of clustering moving objects in spatial networks and propose a unified framework to address this problem. Due to the innate feature of continuously changing positions of moving objects, the clustering results dynamically change. By exploiting the unique features of road networks, our framework first introduces a notion of cluster block (CB) as the underlying clustering unit. We then divide the clustering process into the continuous maintenance of CBs and periodical construction of clusters with different criteria based on CBs. The algorithms for efficiently maintaining and organizing the CBs to construct clusters are proposed. Extensive experimental results show that our clustering framework achieves high efficiency for clustering moving objects in real road networks.
Protection of users' privacy has been a central issue for location-based services (LBSs). In this paper, we classify two kinds of privacy protection requirements in LBS: location anonymity and identifier anonymity...
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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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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.
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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Sequential pattern mining is an important method in data mining. Traditional mining algorithms are not adapted to the fast, unlimited, continuous and dynamic data stream because they are multiple pass in scanning data...
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Sequential pattern mining is an important method in data mining. Traditional mining algorithms are not adapted to the fast, unlimited, continuous and dynamic data stream because they are multiple pass in scanning database. Some approximate sequential pattern mining algorithms are proposed recently which cost too many system resources in sequence compare process. A sequential compare method based on Levenshtein-Automata is proposed in this paper. This method build state conversion model with pretreatment which can finish computing the sequences' similarity in linear time. A combination of Levenshtein-Automata computation and common computation of edit distance is presented in allusion to the Levenshtein-Automata's problem of using too much memory, so a tradeoff between time cost and space cost is implemented. The experiment result shows this method is effective and efficient.
The skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset It is one of the most important query methods for database, datastream, P2P networks. ...
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The skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset It is one of the most important query methods for database, datastream, P2P networks. However, it has not been implemented in sensor networks due to limited energy of the sensor nodes. This paper presents an energy-efficient approximate skyline query scheme for sensor networks. According to the experiments, this scheme can greatly improve the lifetime of sensor networks compared to the naive skyline query.
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.
Load shedding has been widely used in data stream management systems (DSMSs) to keep DSMSs running steadily. One key problem in load shedding is determining how much system load to shed. Existing works tend to adapt c...
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Load shedding has been widely used in data stream management systems (DSMSs) to keep DSMSs running steadily. One key problem in load shedding is determining how much system load to shed. Existing works tend to adapt coarse algorithm (CA) to solve this problem. In this paper, we present an adaptive PI controller-based load shedding framework for data stream. The main contribution of this paper is our use of feedback control theory to design the load shedding scheme. In contrast to the existing approaches, we firstly apply system identification to establish a dynamic model to describe DSMS, which enables us analyze DSMS quantitatively. Then, based on the model, we use the Root Locus method to design the PI controller with proven performance guarantees. The adaptive framework has been implemented by modifying Borealis system. Theoretic analysis and experimental results demonstrate that our approach is robust even when system load changes frequently. Comparing to existing strategies, our approach also achieves significantly better performance.
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