/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

Feature selection methods for big data bioinformatics: A survey from the search perspective.

Pythagorean fuzzy set, generalized by Yager, is a new tool to deal with vagueness considering the membership grade $$\mu $$
 and non-membership $$\nu $$
 satisfying the condition $$\mu ^2+\nu ^2\le 1$$
 . It can be used to characterize the uncertain information more sufficiently and accurately than intuitionistic fuzzy set. Pythagorean fuzzy set has attracted great attention of many scholars that have been extended to new types and these extensions have been used in many areas such as decision making, aggregation operators, and information measures. Because of such a growth, we present an overview on Pythagorean fuzzy set with aim of offering a clear perspective on the different concepts, tools and trends related to their extension. In particular, we provide two novel algorithms in decision making problems under Pythagorean fuzzy environment. It may be served as a foundation for developing more algorithms in decision making.

Pythagorean fuzzy set: state of the art and future directions

We present a descriptive literature review and classification scheme for cloud computing research. This includes 205 refereed journal articles published since the inception of cloud computing research. The articles are classified based on a scheme that consists of four main categories: technological issues, business issues, domains and applications, and conceptualising cloud computing. The results show that although current research is still skewed towards technological issues, new research themes regarding social and organisational implications are emerging. This review provides a reference source and classification scheme for IS researchers interested in cloud computing, and to indicate under-researched areas as well as future directions.

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A Descriptive Literature Review and Classification of Cloud Computing Research

Recently MapReduce programming model is becoming popular for large scale data intensive distributed applications due to its efficiency, simplicity and ease of use. The Hadoop implementation of MapReduce is one of the most popular tools for many programmers due its ability to hide details of parallel programming from the users. However work on simulating the Hadoop environment is still in its infancy. Although there are a large number of simulating tools available to simulate distributed environments. However there are only a few simulators available which specifically targets the MapReduce environment. Based on testing we performed; the usability of these simulators is not satisfactory due to the simplified design which limits simulating jobs with variance configurations. We have designed and implemented a MapReduce simulator based on discrete event simulation called MRSim which accurately simulate the Hadoop environment. The simulator on one hand allows us to measure scalability of MapReduce based applications easily and quickly, on the other hand captures the effects of different configurations of Hadoop setup on MapReduce based applications behavior in terms of Hadoop job completion times and hardware utilization.

MRSim: A discrete event based MapReduce simulator

Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

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A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

HSim: A MapReduce simulator in enabling Cloud Computing

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) have been used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents MRSMO, a MapReduce based distributed SVM algorithm for automatic image annotation. The performance of the MRSMO algorithm is evaluated in an experimental environment. By partitioning the training dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computers, the MRSMO algorithm reduces the training time significantly while maintaining a high level of accuracy in both binary and multiclass classifications.

A MapReduce-based distributed SVM algorithm for automatic image annotation

https://bura.brunel.ac.uk/bitstream/2438/8787/2/Fulltext.pdf

Yang Liu

Papers

MRSim: A discrete event based MapReduce simulator

A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

HSim: A MapReduce simulator in enabling Cloud Computing

A MapReduce-based distributed SVM algorithm for automatic image annotation

An ontology enhanced parallel SVM for scalable spam filter training