Supply chain management (SCM) environments are often dynamic markets providing a plethora of information, either complete or incomplete. It is, therefore, evident that such environments demand intelligent solutions, w...
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Supply chain management (SCM) environments are often dynamic markets providing a plethora of information, either complete or incomplete. It is, therefore, evident that such environments demand intelligent solutions, which can perceive variations and act in order to achieve maximum revenue. To do so, they must also provide some sophisticated mechanism for exploiting the full potential of the environments they inhabit. Advancing on the way autonomous solutions usually deal with the SCM process, we have built a robust and highly-adaptable mechanism for efficiently dealing with all SCM facets, while at the same time incorporating a module that exploits data mining technology in order to forecast the price of the winning bid in a given order and, thus, adjust its bidding strategy. The paper presents our agent, Mertacor, and focuses on the forecasting mechanism it incorporates, aiming to optimal agent efficiency
Autonomic computersystems adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and s...
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Autonomic computersystems adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system. The self adaptation can be achieved by tuning the software, balancing the load or through hardware provisioning. This paper investigates a feed-forward adaptation scheme in which tuning and provisioning decisions are based on a dynamic predictive performance model of the system and the software. The model consists of a layered queuing network whose parameters are tuned by tracking the system with an Extended Kalman Filter. An optimization algorithm searches the system configuration space by using the predictive performance model to evaluate every configuration.
We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed items...
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We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed itemset pattern mining. Based on this method, we show that (1) the number of complete closed multidimensional sequential patterns is not larger than the number of complete multidimensional sequential patterns (2) the set of complete closed multidimensional sequential patterns covers the complete resulting set of multidimensional sequential patterns. In addition, mining using closed itemset pattern mining on multidimensional information would mine only multidimensional information associated with mined closed sequential patterns, and mining using closed sequential pattern mining on sequences would mine only sequences associated with mined closed itemset patterns
Data replication is one of the key components in data grid architecture as it enhances data access and reliability and minimises the cost of data transmission. In this paper, we address the problem of reducing the ove...
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ISBN:
(纸本)9781424430116
Data replication is one of the key components in data grid architecture as it enhances data access and reliability and minimises the cost of data transmission. In this paper, we address the problem of reducing the overheads of the replication mechanisms that drive the data management components of a data grid. We propose an approach that extends the resource broker with policies that factor in user quality of service as well as service costs when replicating and transferring data. A realistic model of the data grid was created to simulate and explore the performance of the proposed policy. The policy displayed an effective means of improving the performance of the grid network traffic and is indicated by the improvement of speed and cost of transfers by brokers.
Logistic planning and programming of construction machinery involves complex sets of objectives and constraints; therefore traditional approaches typically result in a large monolithic model that is difficult to solve...
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Logistic planning and programming of construction machinery involves complex sets of objectives and constraints; therefore traditional approaches typically result in a large monolithic model that is difficult to solve, understand, and maintain. In order to tackle large, particularly combinatorial, problems in logistic support for construction machinery, we design a constraint programming system, namely DISPDESK, which comprises a specification generator and its underlying domain-specific architecture, an algorithm library and its selector, a pre-defined solution library and a problem solver. The system features separation of concerns in specifications, little requirements for programming skills, domain-specific optimization, and semi-automatic generation of high-performance and reliable problem solvers
In this article, we describe a grid of sensors to collect patients' vital data and to allow real time monitoring of patients in heath-care centres. We analyse the problem scenario and identify the components invol...
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In this article, we describe a grid of sensors to collect patients' vital data and to allow real time monitoring of patients in heath-care centres. We analyse the problem scenario and identify the components involved towards the construction of an integrated and homogeneous management system. Finally, we present a case study to demonstrate the applicability of our approach
The growing computational and storage needs of several scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM's BlueGene/L which can accommodate as many as 128 K processors....
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The growing computational and storage needs of several scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM's BlueGene/L which can accommodate as many as 128 K processors. One of the challenges when designing and deploying these systems in a production setting is the need to take failure occurrences, whether it be in the hardware or in the software, into account. Earlier work has shown that conventional runtime fault-tolerant techniques such as periodic checkpointing are not effective to the emerging systems. Instead, the ability to predict failure occurrences can help develop more effective checkpointing strategies. Failure prediction has long been regarded as a challenging research problem, mainly due to the lack of realistic failure data from actual production systems. In this study, we have collected RAS event logs from BlueGene/L over a period of more than 100 days. We have investigated the characteristics of fatal failure events, as well as the correlation between fatal events and non-fatal events. Based on the observations, we have developed three simple yet effective failure prediction methods, which can predict around 80% of the memory and network failures, and 47% of the application I/O failures
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