computingmodel of nodes has an important effect on the network performance and the mobile agent computingmodel for the next generation network has attracted more and more attentions. The background of mobile agent a...
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ISBN:
(纸本)9781424413119
computingmodel of nodes has an important effect on the network performance and the mobile agent computingmodel for the next generation network has attracted more and more attentions. The background of mobile agent applied In the wireless sensor network is first analyzed. Then a framework based on mobile agent is proposed from two levels, namely network and nodes. After introducing the basic elements, functional definitions and interfaces of the framework, an implementation mechanism based on directed diffusion for mobile agent is presented in detail.
Due to the advent of multicore machines, shared memory distributed computing models taking into account asynchrony and process crashes are becoming more and more important. This paper visits models for these systems a...
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ISBN:
(数字)9783642222122
ISBN:
(纸本)9783642222115;9783642222122
Due to the advent of multicore machines, shared memory distributed computing models taking into account asynchrony and process crashes are becoming more and more important. This paper visits models for these systems and analyses their properties from a computability point of view. Among them, the base snapshot model and the iterated model are particularly investigated. The paper visits also several approaches that have been proposed to model failures (mainly the wait-free model and the adversary model) and gives also a look at the BG simulation. The aim of this survey is to help the reader to better understand the power and limits of distributedcomputing shared memory models.
In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mi...
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ISBN:
(纸本)9781479942930
In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided taxonomies. Among the generalized itemsets, the most peculiar high-level patterns are those with many contrasting correlations among items at different abstraction levels. They represent misleading situations that are worth analyzing separately by experts during manual inspection. This paper proposes a novel cloud-based service, named MGI-CLOUD, to efficiently mine misleading multiple-level patterns, i.e., theMisleading Generalized Itemsets, on a distributedcomputing environment. MGI-CLOUD consists of a set of distributed MapReduce jobs running in the cloud. As a case study, the system has been contextualized in a real-life scenario, i.e., the analysis of traffic law infractions committed in a smart city environment. The experiments, performed on real datasets, demonstrate the efficiency and effectiveness of MGI-CLOUD.
Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data ...
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ISBN:
(纸本)9780769550220
Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a novel cloud-based service, named SEARUM, to efficiently mine association rules on a distributed computing model. SEARUM consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process. As a case study, the proposed approach has been applied to the network data scenario. The experimental validation, performed on two real network datasets, shows the effectiveness and the efficiency of SEARUM in mining association rules on a distributed computing model.
We present an approach to data mining on arbitrary graph data that uses a cloud-based distributed computing model for dynamic provisioning of computing resources as the graph model grows or shrinks. Further, we introd...
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ISBN:
(纸本)9781479904051
We present an approach to data mining on arbitrary graph data that uses a cloud-based distributed computing model for dynamic provisioning of computing resources as the graph model grows or shrinks. Further, we introduce the concept of logging graph changes as a basis for calculating properties of dynamic graphs. We briefly describe queries that leverage the dynamic graph model, for instance, by using a snapshot of the original graph while an algorithm executes or adapting query results as the graph changes. To demonstrate the feasibility of our approach, we conducted an initial evaluation, which shows that our parallel computingmodel can dramatically improve load times. Raw data imported into our system is processed faster on larger compute clusters.
作者：
Yoshitomi MorisawaKoji ToriiNihon Unisys
Ltd. 1-1-1 Toyosu Koto-ku Tokyo 135-8560 Japan and Graduate School of Information Science Nara Institute of Science and Technology 8916-5 Takayama Ikoma-shi nara 630-0101 Janan Graduate School of Information Science
Nara Institute of Science and Technology 8916-5 Takayama Ikoma-shi nara 630-0101 Janan
When implementing an application system in a distributedcomputing environment, several architectural questions arise, such as how and where computing resources are arranged, and how the communication among computing ...
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ISBN:
(纸本)9781581133905
When implementing an application system in a distributedcomputing environment, several architectural questions arise, such as how and where computing resources are arranged, and how the communication among computing resources are implemented. To simplify the process of making these choices, we have developed an architectural style for distributed processing system. The style classifies product lines for distributed processing systems into nine categories based on the location of data storage and the style of processing between client and server. This paper describes our architectural style and proposes a simple but practical method to select an appropriate architectural style for developing an application system. We apply this selection method in concrete real application systems.
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