Topology control is one of the most elementary topics in wireless sensor networks. Typically, most of the research only considered the bidirectional communication and symmetric weighted communication model, while the ...
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ISBN:
(纸本)9781605581491
Topology control is one of the most elementary topics in wireless sensor networks. Typically, most of the research only considered the bidirectional communication and symmetric weighted communication model, while the real wireless world contains not only bidirectional but also unidirectional communication links and asymmetric weighted communication model. In this paper, we present two heuristics for the minimum power topology control problem on general model, i.e., given a set of sensors in the Euclidean plane and a transmission power threshold for each directed pair of sensors, to find a power assignment for each sensor to achieve a strong connectivity with minimum total transmission power. Extensive results in simulation evaluate the efficiency of the proposed algorithms. Copyright 2008 ACM.
Recently there have been growing interests in the applications of wireless sensor networks such as traffic tracking, environmental surveillance, and network monitoring. In these applications, the exploration of the re...
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ISBN:
(纸本)9781424432004;9780769531854
Recently there have been growing interests in the applications of wireless sensor networks such as traffic tracking, environmental surveillance, and network monitoring. In these applications, the exploration of the relationship and linkage of sensing data with other data sources can be naturally expressed by the external join, where the sensory tuples join with an external table at the base station. However, executing such kind of join queries in a highly distributed and resource-constraint sensor network is a challenging task. In this paper, we propose a partition-based algorithm called NEJA (in-network external join algorithm) for the external join processing in sensor networks. NEJA organizes the sensory data of the network through an optimized "value-to-storage" mapping, according to which each storage point stores the tuples that belong to the same subrange on the joint attribute. Then the subrange of each storage point is further partitioned into unit ranges, and tuples in the same unit range wisely choose their joining point that incurs the least communication cost based on a cost metric according to the latest historical statistics. Also, NEJA adopts some optimization techniques to handle the changes of sensory data and uses approximate approaches to cut down the maintenance cost of the mechanism. The experimental results indicate that our scheme is effective in reducing the amount of transmissions for the real time external join processing, especially when the external table has a relatively large size.
Frequent itemset mining is a very important problem in data mining. Closed frequent itemsets is the condensed representation of frequent itemsets thus spend less memory, so it is much suitable for stream mining. But o...
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ISBN:
(纸本)9780769532636
Frequent itemset mining is a very important problem in data mining. Closed frequent itemsets is the condensed representation of frequent itemsets thus spend less memory, so it is much suitable for stream mining. But on the other hand, when the minimum support is much lower, the size of closed frequent itemsets turns larger, which makes the performance reduced a lot. In this paper, we introduce a threshold to approximately mine closed frequent itemsets with a limited error tolerance. A new algorithm named ACFIM is proposed based on the introduction of the distance conception to mine the sliding window of stream, in which more data are pruned and more computation time are saved, so it much raise the performance in running time and memory comparing to the state-of-art closed frequent itemsets mining methods. Our experimental results over real-life datasets show that ACFIM is effective and efficient.
This paper focuses on the problem that how to select the optimal service among many Web services which all meet the functional needs,establishes an index system for Web services products selection from four aspects,na...
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This paper focuses on the problem that how to select the optimal service among many Web services which all meet the functional needs,establishes an index system for Web services products selection from four aspects,namely the supply side,the user,product and *** on this,we collect the views of 30 experts by Analytic Hierarchy Process (AHP) method and calculate the weight of each index at all levels based on the data collected from questionnaire *** the overall sample data analysis,we put two types of sample data namely business operation experts and academics for comparative *** Web services selection model proposed in this article can provide the reference to Web services managers when they selecting Web services,and also contributes to in-depth research on the adoption of Web services based information system.
Frequent itemsets mining is an important problem in data mining. Frequent closed itemsets mining provides complete and condensed information for frequent pattern analysis thus reduces the memory cost without accuracy ...
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ISBN:
(纸本)9780769532639
Frequent itemsets mining is an important problem in data mining. Frequent closed itemsets mining provides complete and condensed information for frequent pattern analysis thus reduces the memory cost without accuracy loss. More research focus on stream mining with the more application of stream. Stream is fast and unlimited thus data had to be stored in limited memory, how to save running time and memory usage is the most important target. In this paper, we propose an improved frequent closed itemsets mining method based on traditional stream mining algorithm CFI-stream with bitmap coding named CLIMB (closed itemset mining with bitmap) over stream's sliding window. The distinct items are maintained in memory in lexicographic order and each itemset is coded to bit-sequence with the order of items, moreover, the bit-sequence is split into sections to be recoded to reduce the memory cost. The experimental results on real-life show that CLIMB algorithm is effective and efficient.
In this paper, we present a system called CRO (Chinese Review Observer) for online product review structurization. By Structurization, we mean identifying, extracting and summarizing information from unstructured revi...
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ISBN:
(纸本)9781605581934
In this paper, we present a system called CRO (Chinese Review Observer) for online product review structurization. By Structurization, we mean identifying, extracting and summarizing information from unstructured review text to a structured table. The core tasks include review collection, product feature and user opinion extraction, and polarity analysis of opinions. Existing research in this area is mainly English text oriented. To deal with Chinese effectively, we propose several novel approaches for fulfilling the core tasks. Then we integrated these approaches and implement the whole procedure of review structurization in the system CRO. Running results for reviews of real products show its performance is satisfactory.
Sequential pattern mining is an important problem in continuous, fast, dynamic and unlimited stream mining. Recently approximate mining algorithms are proposed which spend too many system resources and can only obtain...
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Sequential pattern mining is an important problem in continuous, fast, dynamic and unlimited stream mining. Recently approximate mining algorithms are proposed which spend too many system resources and can only obtain the partial feature of stream. In this paper, a multi-level evolving sequential pattern mining model ESPMM is presented to address this problem thus the mostly entire stream feature is obtained. Furthermore, because of the smaller support of sequential patterns in each level, a mining method BMLA based on Levenshtein-Automata is proposed which builds state conversion model to compute sequences' similarity in linear time. The experiment results show this model is effective and efficient.
In the research field of supply chain coordination,many coordination contracts have been well *** chain members still feel confused about which contract should be chosen for their specific needs and *** paper starts f...
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In the research field of supply chain coordination,many coordination contracts have been well *** chain members still feel confused about which contract should be chosen for their specific needs and *** paper starts from the essential analysis of supply chain coordination,and summaries four important affecting factors as well as the attributes in coordination,including market demand,competitors' relationship,supply chain structure,and decisions *** importantly this paper studies the related products' characters,such as storage life,customer's loyalty,etc,which are seldom discussed in coordination before,and analyzes the influence in *** on these research,the chain members could analyze their specific product's characters and affecting factors,then choose the proper coordination contracts.
作者：
Li YuSchool of Information
Key Laboratory of Data Engineering and Knowledge Engineerin Renmin University of China Beijing China
Collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. It is not adapted to multi-interest or title recommendation for the 'general neighbourhood' problem w...
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ISBN:
(纸本)9781424432004;9780769531854
Collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. It is not adapted to multi-interest or title recommendation for the 'general neighbourhood' problem which is analyzed in this paper. Based on it, collaborative filtering recommendation based on community is presented by introducing the concept 'community neighbourhood' in the paper. Unfortunately, it results into severer sparsity problem which makes heavy effect on its performance. In order to overcome it, an ontological A-priori score is used to infer user preference and to pre-fill null rating first. After pre-filling using the ontology method, then collaborative filtering based on community is executed based on a dense rating matrix. The experiment shows that collaborative filtering based on community makes generally better performance than traditional method when data is not very sparse, and ontology method can truly enhance collaborative filtering based on community since the sparsity is overcame.
Most traditional mining approaches of frequent item sets consider mainly on databases and thus can use the second storage and need multiple scans which are not adapted to mining of stream. Some new algorithms over str...
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Most traditional mining approaches of frequent item sets consider mainly on databases and thus can use the second storage and need multiple scans which are not adapted to mining of stream. Some new algorithms over stream's sliding window are presented recently, which perform addition and deletion over stream independently, so the common deleting strategy which removes the earliest transaction is used when the window slides. This paper considers both operations together to reduce the computation cost, consequently, three deleting strategies are proposed to improve the performance with little precision loss. The experimental results show that these strategies over current method are effective and efficient.
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