Biometrics are a nowadays trend in developing secure systems. Biometric authentication systems must provide a higher processing capacity, as biometric verifications are complex. distributedcomputing relieves the comp...
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Crawling web applications is important for indexing, accessibility and security assessment. Crawling traditional web applications is an old problem, as old as the web itself. Crawling Rich Internet applications (RIA) ...
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ISBN:
(纸本)9780769550947
Crawling web applications is important for indexing, accessibility and security assessment. Crawling traditional web applications is an old problem, as old as the web itself. Crawling Rich Internet applications (RIA) quickly and efficiently, however, is an open problem. technologies such as AJAX and partial Document Object Model (DOM) updates only makes the problem of crawling RIA more time consuming to the web crawler. To reduce the time to crawl a RIA, this paper presents a new distributed algorithm to crawl a RIA in parallel with multiple computers, called Dist-RIA Crawler. Dist-RIA Crawler uses the JavaScript (R) events in the DOM structure to partition the search space. this paper illustrates a prototype implementation of Dist-RIA Crawler and inspect empirical performance measurements.
the future power grid expected to accommodate and integrates all new kinds of distributed renewable energy generations, intelligent energy management systems to accommodate the ever growing energy demand. this fact le...
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ISBN:
(纸本)9781479932542
the future power grid expected to accommodate and integrates all new kinds of distributed renewable energy generations, intelligent energy management systems to accommodate the ever growing energy demand. this fact leads the trend of power grid to shift to the Smart Grid that uses advanced communication, smart meters, sensors and information technologies to create an automated, intelligent and widely distributed energy delivery network. A huge amount of row data is collected by smart meters and sensors from the end user and different part of the network to the computation system. Subsequently, this considerably big amount of data must be processed, analyzed and stored in a cost effective ways. In this manner, an enormous pool of computing resources and storage must be provided to compute this vast amount of data. Researchers have been suggesting different solutions. A distributed and parallelcomputing techniques for future power system, Grid computing [1], fast parallel processing of the mass data in cloud computing [2], and smart grid control software in cloud computing architecture [3]. this paper discussed the feasibility study of the handling of monitoring of renewable energy in smart grid on cloud computing framework retaining smart grid security, analysis of the availability of Energy management software tools in smart grid using cloud computing, design of a demonstrator and realization of demonstration for further education purpose to utilize the advantage of cloud computingdistributed and scalable nature.
Much CSCW research predominantly focuses on investigating how distributed, mediated interactions are different from collocated interactions, but rarely looks at how the use of technologies affect collocated people. We...
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the proceedings contain 73 papers. the topics discussed include: guaranteed scheduling for (m,k)-firm deadline-constrained real-time tasks on multiprocessors;a distributed task migration scheme for mesh-based chip-mul...
ISBN:
(纸本)9780769545646
the proceedings contain 73 papers. the topics discussed include: guaranteed scheduling for (m,k)-firm deadline-constrained real-time tasks on multiprocessors;a distributed task migration scheme for mesh-based chip-multiprocessors;jMigBSP: object migration and asynchronous one-sided communication for BSP applications;a social network-based information dissemination scheme;XunleiProbe: a sensitive and accurate probing on a large-scale P2SP system;data flow error recovery with checkpointing and instruction-level fault tolerance;an experimental study on memory allocators in multicore and multithreaded applications;efficient hierarchical agglomerative clustering algorithms on GPU using data partitioning;the multidimensional scaling and barycentric coordinates based distributed localization in wireless sensor networks;fast estimation of Gaussian mixture model parameters on GPU using CUDA;and optimizing web browser on many-core architectures.
Attribute reduction for big data is viewed as an important preprocessing step in the areas of pattern recognition, machine learning and data mining. In this paper, a novel parallel method based on MapReduce for large-...
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ISBN:
(纸本)9781479924189
Attribute reduction for big data is viewed as an important preprocessing step in the areas of pattern recognition, machine learning and data mining. In this paper, a novel parallel method based on MapReduce for large-scale attribute reduction is proposed. By using this method, several representative heuristic attribute reduction algorithms in rough set theory have been parallelized. Further, each of the improved parallel algorithms can select the same attribute reduct as its sequential version, therefore, owns the same classification accuracy. An extensive experimental evaluation shows that these parallel algorithms are effective for big data.
this paper describes in detail, the workings of a prototype version of a parallel processor for high-performance computing of large-scale tasks for identification (classification). the use of such processors will prov...
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ISBN:
(纸本)9781479909247;9781479909230
this paper describes in detail, the workings of a prototype version of a parallel processor for high-performance computing of large-scale tasks for identification (classification). the use of such processors will provide the required computing power with a guaranteed quality of decision making. Program settings and architecture cascading, as well as the knowledge of the dependence of performance of hardware resources, will reduce the time frame for the development of intelligent systems for pattern recognition as integrated software and hardware, presented in real-time.
We focus on the parallelization of two-dimensional square packing problem. In square packing problem, a list of square items need to be packed into a minimum number of unit square bins. All square items have side leng...
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ISBN:
(纸本)9781479924189
We focus on the parallelization of two-dimensional square packing problem. In square packing problem, a list of square items need to be packed into a minimum number of unit square bins. All square items have side length smaller than or equal to 1 which is also the side length of each unit square bin. the total area of items that has been packed into one bin cannot exceed 1. Using the idea of harmonic, some squares can be put into the same bin without exceeding the bin limitation of side length 1. We try to concurrently pack all the corresponding squares into one bin by a parallel systerm of computation processing. A 9/4-worst case asymptotic error bound algorithm with time complexity theta(n) is showed. Let OPT(I) and A(I) denote, respectively, the cost of an optimal solution and the cost produced by an approximation algorithm A for an instance I of the square packing problem. the best upper bound of on-line square packing to date is 2.1439 proved by Han et al. [23] by using complexity weighting functions. However the upper bound of our parallel algorithm is a litter worse than Han's algorithm, the analysis of our algorithm is more simple and the time complexity is improved. Han's algorithm needs O(nlogn) time, while our method only needs theta(n) time.
Nowadays, the volume of multimedia and unstructured data has grown rapidly. More and more three-dimensional (3D) models are created for ever increasing applications. New storage and processing technologies are needed ...
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ISBN:
(纸本)9781467347143
Nowadays, the volume of multimedia and unstructured data has grown rapidly. More and more three-dimensional (3D) models are created for ever increasing applications. New storage and processing technologies are needed to keep pace withthe continuous growth of big data. Hadoop is an attractive and open-source platform for large-scale data storage and analytics. Our previous research work has applied Hadoop distributed file system to efficiently manage 3D data for a 3D model retrieval system. To take better advantages of Hadoop, in this paper we propose two parallel strategies to improve the storing and accessing performance of 3D models. the MapReduce paradigm is adopted to provide a coarse grained parallelism for data loading, and a lightweight multithreaded algorithm is presented for data accesses. We conduct an extensive performance study on a cluster and the results show that significant performance increase can be gained for the parallel techniques.
the proceedings contain 87 papers. the topics discussed include: dynamic transactional workflows in service-oriented environments;watermarking images in the frequency domain by exploiting self-inverting permutations;a...
ISBN:
(纸本)9789898565549
the proceedings contain 87 papers. the topics discussed include: dynamic transactional workflows in service-oriented environments;watermarking images in the frequency domain by exploiting self-inverting permutations;a dynamic load balancing strategy for a distributed biometric authentication system;towards a model-driven development of web applications;designing a click fraud detection algorithm - exposing suspect networks;model-based performance testing of web services using probabilistic timed automata;web forums change analysis;empowering collaborative business intelligence by the use of online social networks;semantic matching to achieve software component discovery and composition;cost-effective web-based media synchronization schemes for real-time distributed groupware;and making data citable - a web-based system for the registration of social and economics science data.
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