A Brief Review on Leading Big Data Models

Sharma, Shweta; Tim, U. Sunday; Wong, Johnny; Gadia, Shashi K.; Sharma, S. C.

doi:10.2481/dsj.14-041

Cited by 72 publications

(29 citation statements)

References 10 publications

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“…Weibo is reported to have over 766 million active users per day in 2014 (Weibo, 2015). The increasing use of social media such as YouTube, Twitter, and Weibo has contributed nearly 90% of the total data available today (Sharma et al, 2014). These unprecedented large and complex data have given birth to the concept of "Big Data" (Sharma et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China

Ram

Zhang

Koronios

2016

Procedia Computer Science

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Social media has brought about a revolution and dictated a paradigm shift in the operational strategies of firms globally. It has resulted in collection of massive data from a variety of social media channels, necessitating use of this data for business intelligence purposes. Despite its importance, little research exists on the implications of the use of Big Data analytics for business intelligence purposes. This study fills this gap in knowledge by examining the role and implication of Big Data analytics on business intelligence for the data collected from Social media channels in China. Given the exploratory nature of the research, the study takes a qualitative approach to data collection and analysis. Based on an extensive literature review, the study has developed a robust semi-structure questionnaire. We plan to conduct approximately 35-40 interviews with respondents such as IT managers, IT consultants, and Senior Business managers among others from a wide range of industries including retail and manufacturing settings. The data will be analysed using Nvivo to identify issues that are critical for creating value through Big Data analytics for business intelligence purposes. The results have significant impact for both theory and practice to devise plans and strategies to optimise the benefits of social-media channels for business value.

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Section: Introductionmentioning

confidence: 99%

The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China

Ram

Zhang

Koronios

2016

Procedia Computer Science

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“…With the pervasiveness of the Internet of Things the amount of available data is getting much larger [20]. Deep learning is a useful tool for analyzing and learning from massive amounts of data (also known as Big Data) that may be unlabeled and unstructured [47,44,36]. Deep learning algorithms can be found in many modern applications [54,50,19,56,48,17,21,59], such as, voice recognition, face recognition, autonomous cars, classification of liver diseases and breast cancer, computer vision, or social media.…”

Section: Introductionmentioning

confidence: 99%

CHAOS: a parallelization scheme for training convolutional neural networks on Intel Xeon Phi

et al. 2017

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Deep learning is an important component of Big Data analytic tools and intelligent applications, such as self-driving cars, computer vision, speech recognition, or precision medicine. However, the training process is computationally intensive and often requires a large amount of time if performed sequentially. Modern parallel computing systems provide the capability to reduce the required training time of deep neural networks. In this paper, we present our parallelization scheme for training convolutional neural networks (CNN) named Controlled Hogwild with Arbitrary Order of Synchronization (CHAOS). Major features of CHAOS include the support for thread and vector parallelism, non-instant updates of weight parameters during back-propagation without a significant delay, and implicit synchronization in arbitrary order. CHAOS is tailored for parallel computing systems that are accelerated with the Intel Xeon Phi. We evaluate our parallelization approach empirically using measurement techniques and performance modeling for various numbers of threads and CNN architectures. Experimental results for the MNIST dataset of handwritten digits using the total number of threads on the Xeon Phi show speedups of up to 103× compared to the execution on one thread of the Xeon Phi, 14× compared to the sequential execution on Intel Xeon E5, and 58× compared to the sequential execution on Intel Core i5.

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“…However, for rate limit to work properly, we must be able to measure the connection failure rates of individual hosts accurately and efficiently. This paper points out that using Internet Control Message Protocol (ICMP) messages for this purpose [21] is flawed as they are widely blocked on today's Internet, and the total number of message packets in this big data [22] [23] [30] [31] and cloud computing [36 -39] era is enormous. In this paper, we first design a novel measurement method that solves the problem with a highly efficient data structure based on bitmaps [41], which keeps record of connection attempts and results (success or fail) in bits, from which we can recover the connection failure rates, while removing the duplicate connection failures (which may cause bias against normal hosts).…”

Section: Introductionmentioning

confidence: 99%

Limiting Self-Propagating Malware Based On Connection Failure Behavior Through Hyper-Compact Estimators

Zhou¹,

Zhou²,

Chen³

et al. 2016

IJNSA

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A Brief Review on Leading Big Data Models

Cited by 72 publications

References 10 publications

The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China

The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China

CHAOS: a parallelization scheme for training convolutional neural networks on Intel Xeon Phi

Limiting Self-Propagating Malware Based On Connection Failure Behavior Through Hyper-Compact Estimators

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