Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific sk...
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Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific sk...
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ISBN:
(纸本)9783885791881
Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies, briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references, and discuss first steps towards emergent semantics.
The trends for pushing more operational intelligence towards network elements to achieve more context-aware and self-managing behavior often requires elements to gather network knowledge without necessarily binding ex...
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The trends for pushing more operational intelligence towards network elements to achieve more context-aware and self-managing behavior often requires elements to gather network knowledge without necessarily binding explicitly to all of the potential sources of that knowledge. Though event-based publish-subscribe models allow efficient distribution of knowledge where the event types are known globally, dynamic service chains, ad hoc networks and pervasive computing application all introduce a more fluid and heterogeneous range of context knowledge. This requires some runtime translation of knowledge between sources and sinks of network context. This paper builds on existing mapping techniques that use ontological forms of existing management information models to examine the extent to which these can be employed for runtime semantic interoperability for network knowledge. It presents results in developing a management knowledge delivery framework based on existing models and platforms, but which offers a more decentralized knowledge exchange mechanism
In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for fo...
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In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006 [3].
We can see Multi-Agent Systems (MAS) like a society of agents that cooperates to work in the best way possible. With this we gain the ability of solve complex problems like dynamic and distributed scheduling. MAS cons...
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We can see Multi-Agent Systems (MAS) like a society of agents that cooperates to work in the best way possible. With this we gain the ability of solve complex problems like dynamic and distributed scheduling. MAS consists of several autonomous entities, called agents, that interact with each other to either further their own interests, trough competition or in pursuit a common objective trough cooperation. Social aspects are considered when a community of autonomous agents cooperate to reach a common goal. Agents negotiate in a cooperative way, in order to find a consistent overall plan, while avoiding significant changes onto their current best possible local plans. In this work we consider that a good global solution for a scheduling problem may emerge from a community of machine agents solving locally their schedules and cooperating with other machine agents. A cooperative negotiation mechanism is proposed for solving the schedules coordination process in the Multi-Agent System for Dynamic Scheduling in Manufacturing with Genetic Algorithms and Tabu Search (MASDScheGATS).
The idea of building query-oriented routing indices has changed the way of improving routing efficiency from the basis as it can learn the content distribution during the query routing process. It gradually improves r...
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The idea of building query-oriented routing indices has changed the way of improving routing efficiency from the basis as it can learn the content distribution during the query routing process. It gradually improves routing efficiency with no excessive network overhead of the routing index construction and maintenance. However, the previously proposed mechanism is not practically effective due to the slow improvement of routing efficiency. In this paper, we propose a novel mechanism for query-oriented routing indices which quickly achieves high routing efficiency at low cost. The maintenance method employs reinforcement learning to utilize mass peer behaviors to construct and maintain routing indices. It explicitly uses the expected value of returned content number to depict the content distribution, which helps quickly approximate the real distribution. Meanwhile, the routing method is to retrieve as many contents as possible. It also helps speed up the learning process further. The experimental evaluation shows that the mechanism has high routing efficiency, quick learning ability and satisfactory performance under churn
Many previous works of data mining user queries in peer-to-peer systems focused their attention on the distribution of query contents. However, few has been done towards a better understanding of the time series distr...
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Many previous works of data mining user queries in peer-to-peer systems focused their attention on the distribution of query contents. However, few has been done towards a better understanding of the time series distribution of these queries, which is vital for system performance. To remedy this situation, this paper mines query steams by using automatic time series analysis to evaluate different linear models (Box-Jenkins models and some simple windowed-mean models) for predicting the number of duplicated queries from 10 minutes to 2 hours into the future. Both the predictive power and the computational costs of these models are evaluated over 318,942,450 real world Gnutella queries collected over 3 months. We find the number of duplicated queries is consistently predictable. Simple, practical models like AR perform well on prediction
Locality sensitive hash (LSH) is widely used in peer-to-peer (P2P) systems. Although it can support range or similarity queries, it breaks the load balance mechanism of traditional distributed hash table (DHT) based s...
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Locality sensitive hash (LSH) is widely used in peer-to-peer (P2P) systems. Although it can support range or similarity queries, it breaks the load balance mechanism of traditional distributed hash table (DHT) based system by replacing consistent hash with LSH. To solve the imbalance problem, current systems either weaken the locality preserve ability from similarity preserved to order preserved or adopt load aware peer join mechanism. The first method does not support similarity query as it loses the similarity information and the second method is greatly affected by the dynamic nature of P2P networks. In this paper, we propose a novel system, cuckoo ring, which can preserve similarity information while load balanced. It does not guide the newly joining peer to the hot areas but move the items in the hot areas to cold areas so that the short life time peers are distributed uniformly across the network instead of being guided to the hot areas. Compared to traditional DHT systems, cuckoo ring only maintains a little more information about the global light load peers and the moved indexed items
The purpose of this paper is to present a system for dynamic scheduling of manufacturing orders using a product oriented approach, to be used in an integrated manner for dynamic, inter-active and iterative scheduling ...
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The purpose of this paper is to present a system for dynamic scheduling of manufacturing orders using a product oriented approach, to be used in an integrated manner for dynamic, inter-active and iterative scheduling under a scheduling decision support system. Here is described a due date based scheduling method, where al the operations of one task are scheduled before the next task is considered, a task is considered to be the set of all the operations needed to produce one product. The referred method is to be applied to solve real world dynamic scheduling problems in a multi-order multi-resource environment, where the products to be processed have release times and due dates, and the resources are available in a limited amount. Some realistic constraints are considered, such as multilevel tasks, shared resources, alternative resources and temporal constraints. The objective is to meet the deadlines for all the tasks. Yet there is one limitation, pre-emption is not allowed.
The detection of the types of local surface form deviations is a major step in the automated quality assessment of car body parts during the manufacturing process. In previous studies we compared the performance of di...
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The detection of the types of local surface form deviations is a major step in the automated quality assessment of car body parts during the manufacturing process. In previous studies we compared the performance of different soft computing techniques for this purpose. We achieved promising results with regard to classification accuracy and interpretability of rule bases, even though the dataset was rather small, high dimensional and unbalanced. In this paper we reconsider the collection of training examples and their assignment to defect types by the quality experts. We attempt to minimize the uncertainty of the quality experts' subjective and error-prone labelling in order to achieve a higher reliability of the defect detection. We show that refined and more accurate classification models can be built on the basis of a preprocessed training set that is more consistent. Using a partially supervised learning strategy we can report improvements in classification accuracy.
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