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Search Results: 1 - 10 of 52080 matches for " data analysis "
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Research of Big Data Based on the Views of Technology and Application  [PDF]
Zan Mo, Yanfei Li
American Journal of Industrial and Business Management (AJIBM) , 2015, DOI: 10.4236/ajibm.2015.54021
Abstract: In the era of big data, large amounts of data affect our work, life and study, even national economic development. It provides a new way of thinking and approaches to analyze and solve problems, which gradually becomes a hot research. Based on describing the concept and characteristics of big data, this paper describes the development of technologies in big data analysis and storage and analyses the trends and different values in commercial applications, manufacturing, biomedical science and other applications. At last, the authors sum up the existent challenges of big data applications and put forward the view that we should deal with big data challenges correctly.
A Hierarchical Methodology for Performance Evaluation Based on Data Envelopment Analysis: The Case of Companies’ Competitiveness in an Economy  [PDF]
Mohamed Dia, Fouad Ben Abdelaziz
American Journal of Operations Research (AJOR) , 2011, DOI: 10.4236/ajor.2011.13015
Abstract: In this research, we present a hierarchical Data Envelopment Analysis (DEA) methodology for competitiveness analysis. This methodology takes into account the heterogeneity of the decision making units (DMUs) as well as the diversity of the comparison criteria. We propose to homogenize the DMUs by grouping them hierarchically, which permits a better identification and definition of the criteria in each specific grouping. The methodology proceeds first by the determination of the performances or relative efficiencies, which are in turn aggregated into competitiveness indices in each grouping by the superiority index of [1]; then, the overall competitiveness indices are determined additively along the hierarchical levels. We illustrate the methodology by a competitiveness analysis of several companies belonging to different sectors of activity in an economy, where are suggested ways of improvement for the non-competitive companies within their sectors and within the economy.
A Ranking Method of Extreme Efficient DMUs Using Super-Efficiency Model  [PDF]
Dariush Akbarian
Journal of Applied Mathematics and Physics (JAMP) , 2013, DOI: 10.4236/jamp.2013.11001
Abstract: In this paper, we present a method for ranking extreme efficient decision making units (DMUs) in data envelopment analysis (DEA) models based on measuring distance between them and new PPS (after omission extreme efficient DMUs) along the input-axis or output axis.
The Application of Mixed Method in Developing a Cyber Terrorism Framework  [PDF]
Rabiah Ahmad, Zahri Yunos
Journal of Information Security (JIS) , 2012, DOI: 10.4236/jis.2012.33026
Abstract: Mixed method research has becoming an increasingly popular approach in the discipline of sociology, psychology, education, health science and social science. The purpose of this paper is to describe the application of mixed method in developing a cyber terrorism framework. This project has two primary goals: firstly is to discover the theory and then develop a conceptual framework that describes the phenomena, and secondly is to verify the conceptual framework that describes the phenomena. In order to achieve conclusive findings of the study, a mixed method research is recommended: qualitative data and quantitative data are collected and analyzed respectively in a separate phase. The mixed method approach improves the rigor and explanation of the research results, thus bring conclusive findings to the study outcome. By utilizing qualitative and quantitative techniques within the same study, we are able to incorporate the strength of both methodologies and fit together the insights into a workable solution.
Reliability Estimators for the Components of Series and Parallel Systems: The Weibull Model  [PDF]
Felipe L. Bhering, Carlos A. de B. Pereira, Adriano Polpo
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.511157

This paper presents a hierarchical Bayesian approach to the estimation of components’ reliability (survival) using a Weibull model for each of them. The proposed method can be used to estimation with general survival censored data, because the estimation of a component’s reliability in a series (parallel) system is equivalent to the estimation of its survival function with right- (left-) censored data. Besides the Weibull parametric model for reliability data, independent gamma distributions are considered at the first hierarchical level for the Weibull parameters and independent uniform distributions over the real line as priors for the parameters of the gammas. In order to evaluate the model, an example and a simulation study are discussed.

Inferring Locations of Mobile Devices from Wi-Fi Data  [PDF]
Leon Wu, Ying Zhu
Intelligent Information Management (IIM) , 2015, DOI: 10.4236/iim.2015.72006
Abstract: Mobile phones are becoming a primary platform for information access. A major aspect of ubiquitous computing is context-aware applications which collect information about the environment that the user is in and use this information to provide better service and improve user experience. Location awareness makes certain applications possible, e.g., recommending nearby businesses and tracking estimated routes. An Android application is able to collect useful Wi-Fi information without registering a location listener with a network-based provider. We passively collected the data of the IDs of Wi-Fi access points and the received signal strengths. We developed and implemented an algorithm to analyse the data; and designed heuristics to infer the location of the device over time—all without ever connecting to the network thus maximally preserving the privacy of the user.
Big Data Stream Analytics for Near Real-Time Sentiment Analysis  [PDF]
Otto K. M. Cheng, Raymond Lau
Journal of Computer and Communications (JCC) , 2015, DOI: 10.4236/jcc.2015.35024

In the era of big data, huge volumes of data are generated from online social networks, sensor networks, mobile devices, and organizations’ enterprise systems. This phenomenon provides organizations with unprecedented opportunities to tap into big data to mine valuable business intelligence. However, traditional business analytics methods may not be able to cope with the flood of big data. The main contribution of this paper is the illustration of the development of a novel big data stream analytics framework named BDSASA that leverages a probabilistic language model to analyze the consumer sentiments embedded in hundreds of millions of online consumer reviews. In particular, an inference model is embedded into the classical language modeling framework to enhance the prediction of consumer sentiments. The practical implication of our research work is that organizations can apply our big data stream analytics framework to analyze consumers’ product preferences, and hence develop more effective marketing and production strategies.

Evaluate the Investment Efficiency by Using Data Envelopment Analysis: The Case of China  [PDF]
Hualun Zhang, Wei Song, Xiaobao Peng, Xiaoyan Song
American Journal of Operations Research (AJOR) , 2012, DOI: 10.4236/ajor.2012.22020
Abstract: Although investment is regarded as a key force of China’s economic growth, little study has been done to measure China’s investment efficiency. The present paper applies the data envelopment analysis (DEA) to Chinese provincial panel data from the year 2003 to 2008 for measuring the investment efficiencies and identifying their trends of Chinese 30 provinces and autonomous regions. A cross-efficient DEA model with considering benevolent formulation is used for providing accurate efficiency scores and completely ranking. The empirical results suggest that the differences of investment efficiency in different regions are distinct but tending to diminish year by year, and the investment efficiencies in some provinces are significantly correlated to their investment rates to the national total investment.
Correlation between Mortality of Prehospital Trauma Patients and Their Heart Rate Complexity  [PDF]
Gholamhussian Erjaee, Ali Foroutan, Sara Keshtkar, Pegah ShojaMozafar, Alham Benabas
International Journal of Clinical Medicine (IJCM) , 2012, DOI: 10.4236/ijcm.2012.37103
Abstract: Recently, nonlinear analysis of R-to-R interval (RRI) in heart rate has brought research attention in medicine to improve predictive accuracy of medication in severely injured patients. It seems conventional vital signs information such as heart rate and blood pressure to identify critically injured patients eventually replaced by heartrate complexity (HRC) analysis to the electrocardiogram (ECG) of patients in trauma centers. In this respect, different nonlinear analysis tools such as; power spectra, entropy, fractal dimension, auto-correlation function and auto-correlation have been adapted for this complexity analysis of ECG signal. Reidbord and Redington [1] were one of the early reports on applications of nonlinear analysis of the heart physiology. Moody and his colleagues could confidently predicted survival in heart failure cases by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics [2]. Further studies were reported in cases of arrhythmia or general anesthesia by Pomfrett [3], Fortrat [4], Lass [5] and references therein. Recently, noteworthy works of Batchinsky and coworkers have shown that prehospital loss of RRI complexity is associated with mortality in trauma patients [6-8]. They have also shown that prediction of trauma survival by analysis of heart rate complexity is even possible by reducing data set size from 800-beat to 200 or lower beat data sets. In this article, we will use different data nonlinear analysis tools such as; power spectrum, entropy, Lyapunov exponent, capacity dimension and correlation function to analyze HRC as a sensitive indictor of physiologic deterioration. In these analyses, we will use real data of 270-beat sections of ECG from 45 emergency patients brought to Shiraz Rejaee Hospetal trauma center prior to any medication. As we can see, using some manipulation on raw data will provide more informative vital signs in our nonlinear analyses.
Seismic Signal and Data Analysis of Rock Media with Vertical Anisotropy  [PDF]
Yuan Zhao, Nan Zhao, Lin Fa, Meishan Zhao
Journal of Modern Physics (JMP) , 2013, DOI: 10.4236/jmp.2013.41003

This paper is concerned with anisotropic effects on seismic data and signal analysis for transversely isotropic rock media with vertical anisotropy. It is understood that these effects are significant in many practical applications, e.g. earthquake forecasting, materials exploration inside the Earth’s crust, as well as various practical works in oil industry. Under the framework of the most accepted anisotropic media model (i.e. VTI media, transverse isotropy with a vertical axis symmetry), with applications of a set of available anisotropic rock parameters for sandstone and shale, we have performed numerical calculations of the anisotropic effects. We show that for rocks with strong anisotropy, the induced relative depth error can be significantly large. Nevertheless, with an improved understanding of the seismic-signal propagation and proper data processing, the error can be reduced, which in turn may enhance the probability of forecasting accurately the various wave propagations inside the Earth’s crust, e.g. correctly forecasting the incoming earthquakes from the center of the Earth.

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