In allusion to the problem about electricity behavior analysis in the low efficiency of dealing with huge amounts of data, we puts forward the Fuzzy c-means clustering(Fuzzy c-means clustering, FCM) parallel algorithm...
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In allusion to the problem about electricity behavior analysis in the low efficiency of dealing with huge amounts of data, we puts forward the Fuzzy c-means clustering(Fuzzy c-means clustering, FCM) parallel algorithm based on Mapreduce technology. By decomposing the iterative process of FCM algorithm into two steps of Map and Reduce, it can effectively improve the efficiency of similarity computing between the data objects and the clustering centers. On this basis, the four characteristics of resident electrical data are clustering analyzed by using the proposed FCM parallel algorithm. The experimental results show that the proposed algorithm can improve the efficiency of mass data clustering analysis and also proves the feasibility of the model.
With the use of internet more and more information can be searched from the web. Lots of 3D data is generated by satellite and in medical field due to advanced 3D camera capturing techniques. In last few decades 3D im...
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With the use of internet more and more information can be searched from the web. Lots of 3D data is generated by satellite and in medical field due to advanced 3D camera capturing techniques. In last few decades 3D image analysis became essential in the field of Computer-Aided Design (CAD), medical imaging and entertainment.3D models leads to the urgent requirement of effective and efficient 3D model analysis in real-time. This process demands high computation time, storage capacity and network bandwidth. The process of extracting features from large 3D/4D images and analyzing them with different machine learning algorithm is really a challenging task. In this paper, we are proposing use of Hadoop's Map-Reduce technique for analyzing large scale images. Map-Reduce is used in distributed processing for optimizing different tasks.
Summary form only given, as follows. internet has become the ubiquitous fabric that enabled the growth of infrastructures, applications, and technologies that significantly enhance global interactions and collaboratio...
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Summary form only given, as follows. internet has become the ubiquitous fabric that enabled the growth of infrastructures, applications, and technologies that significantly enhance global interactions and collaborations with significant and increasing impact on society. Unprecedented cyber-social and cyber-physical infrastructures, systems, and applications that span geographic boundaries are becoming reality. technology has evolved from standalone tools to open systems supporting collaboration in multi-organizational settings, and from general purpose tools to specialized collaboration platforms. Increasingly, individuals and organizations have relied on internet-enabled collaboration between distributed teams of humans, computer applications, or autonomous robots to achieve higher productivity and produce collaboratively developed products that would have been infeasible just a few years ago. This panel will explore and debate on the challenges and research directions related to Collaboration and internetcomputing areas. Some key issues that will be discussed in this panel are, but not limited to: (1) What are new key challenges in systems, applications and networking areas related to CIC? Are there specific limitations in these areas that need a fundamental redesign? (2) How are the global safety, security and privacy issues reshaping within the context of the CIC area? (3) What are potential transformative, killer applications that CIC can enable and what are the challenges towards achieving them? A record of the panel discussion was not made available for publication as part of the conference proceedings.
作者：
Feng, ShanLiu, RuifangWang, QinlongShi, RuishengBUPT
Sch Informat & Commun Engn Beijing 100876 Peoples R China BUPT
Key Lab Trustworthy Distributed Comp & Serv Educ Minist Beijing 100876 Peoples R China BUPT
Sch Humanities Beijing 100876 Peoples R China
The fast growth of internet web documents has posed new challenges on how to efficiently and accurately manage and retrieve the textual collections, text clustering plays a significant role. Traditional document clust...
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ISBN:
(纸本)9781479947195
The fast growth of internet web documents has posed new challenges on how to efficiently and accurately manage and retrieve the textual collections, text clustering plays a significant role. Traditional document clustering is an unsupervised categorization of a given document collection based on vector space model, which is a high sparse vector. In this paper, we propose a means to fight the existing shortcomings with a word vector in distributed representation which is obtained from a neural probabilistic language model. To improve the representation of document vector and enhance the accuracy of text clustering, we first computing semantic similarities between words using word embedded vector, and then expanding the keywords of each document. The experiment results show the method can improve the accuracy of clustering.
As the wealth of information available on the web keeps growing, being able to harvest massive amounts of data has become a major challenge. Web crawlers are the core components to retrieve such vast collections of pu...
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As the wealth of information available on the web keeps growing, being able to harvest massive amounts of data has become a major challenge. Web crawlers are the core components to retrieve such vast collections of publicly available data. The key limiting factor of any crawler architecture is however its large infrastructure cost. To reduce this cost, and in particular the high upfront investments, we present in this paper a geo-distributed crawler solution, UniCrawl. UniCrawl orchestrates several geographically distributed sites. Each site operates an independent crawler and relies on well-established techniques for fetching and parsing the content of the web. UniCrawl splits the crawled domain space across the sites and federates their storage and computing resources, while minimizing thee inter-site communication cost. To assess our design choices, we evaluate UniCrawl in a controlled environment using the ClueWeb12 dataset, and in the wild when deployed over several remote locations. We conducted several experiments over 3 sites spread across Germany. When compared to a centralized architecture with a crawler simply stretched over several locations, UniCrawl shows a performance improvement of 93.6% in terms of network bandwidth consumption, and a speedup factor of 1.75.
A scheme for WSNs(wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper. The sensing tetrahedron is divided into a number of small ...
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A scheme for WSNs(wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper. The sensing tetrahedron is divided into a number of small grids. All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron. In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials. All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials. At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron. Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.
Cloud computingtechnology is an innovative IT application mode, which integrates computing, storage, network, information services infrastructure, operating system, application platform, WEB services and software res...
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ISBN:
(纸本)9783037859032
Cloud computingtechnology is an innovative IT application mode, which integrates computing, storage, network, information services infrastructure, operating system, application platform, WEB services and software resources. By using cloud computingtechnology, users can use many different services though the internet. This technology is the result of a variety of technologies evolution and integration, including distributedcomputing, grid computing, utility computing, visualization's technology, and SOA, etc. This paper introduces cloud computing principles and analyzes the necessity, key points and difficulties of the simulation cloud computing project of power system.
In recent years, the scale of mobile internet is rapidly increasing because of the explosive growing of smartphone users and applications. The traffic analysis and anomaly detection become critical for mobile operator...
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ISBN:
(纸本)9781479947195
In recent years, the scale of mobile internet is rapidly increasing because of the explosive growing of smartphone users and applications. The traffic analysis and anomaly detection become critical for mobile operators. Up to now, there are a number of studies for detecting anomaly network traffic. However, the way of detecting anomalies on massive traffic data in real-time manner is not well studied. In this paper, we propose a real-time anomaly detection method based on dynamic k-NN cumulative-distance abnormal detection algorithm. We also present the design and implementation of the method by leveraging Strom, a distributed steam computingtechnology. Experimental results from evaluation by real-world dataset show that our system is a promised solution for real-time anomaly detection solution in high-speed network.
A massive growth of collaborative frameworks, where web services, internet of things, mobile applications and digitization of enterprise processes leads to generate massively heterogeneous data termed as Big Data. Han...
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A massive growth of collaborative frameworks, where web services, internet of things, mobile applications and digitization of enterprise processes leads to generate massively heterogeneous data termed as Big Data. Handling the heterogeneity of such data by a distributed file management system based database such as H-Base having limitation to handle only structured form of the data. This paper introduces an efficient algorithm for managing data heterogeneity in three basic types i.e. 1) centralized structured data to distributed structured data, 2) unstructured to a structured format and 3) semi-structured to a structured format. The performance of proposed method is evaluated in a real-time prototype experiment and in future we planned to compare to work on a data transform method in the specific context of health care data addressing performance metrics such as memory consumption and request per write. The experimental outcomes of the proposed system show how the processing time is reduced even when we process data of large size, thereby showing the effectiveness of presented approach.
HEVC has higher compression ratio and better compressed video quality,but encoding algorithm complexity increases rapidly by comparing with H.264 video coding *** order to reduce encoding algorithm complexity,intra/in...
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HEVC has higher compression ratio and better compressed video quality,but encoding algorithm complexity increases rapidly by comparing with H.264 video coding *** order to reduce encoding algorithm complexity,intra/inter prediction mode fast algorithm of decision that is based on pixel relevancy of coding blocks is proposed on the basis of analyzing HM intra/inter prediction mode decision *** dealing with pixel relevancy of coding blocks,cost of inter prediction mode is *** comparison between estimated cost and corresponding inter prediction mode cost can reduce intra prediction calculation required by B or P image compression process can be *** result shows that intra prediction mode coding time of the algorithm reduces by 1.9%,frequency calculation of intra prediction in B or P frame reduces by 73.1% and coding complexity is declined,given that code rate increases by 0.008% and PSNR reduces by 0.004% by comparing with HEVC's reference software HM12.0.
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