PERFORMANCE IMPACTS OF BIG DATA ANALYTICS

Big Data Analytics has been a ‘hot topic’ for industry and academic during the past few years. This paper examines what constitutes Big Data Analytics (BDA) and how it relates to organizational performance. It also investigates what other factors influence this relationship, whether BDA leads to more data-driven decision-making (DDDM) and whether the latter is really superior to less informed decision-making. The study first operationalizes Big Data Analytics, and then develops a research model which manifests the direct and indirect relationships between analytic capability, DDDM, and organizational performance

Do supernovae favor tachyonic Big Brake instead de Sitter?

We investigate whether a tachyonic scalar field, encompassing both dark energy and dark matter-like features will drive our universe towards a Big Brake singularity or a de Sitter expansion. In doing this it is crucial to establish the parameter domain of the model, which is compatible with type Ia supernovae data. We find the 1-sigma contours and evolve the tachyonic sytem into the future. We conclude, that both future evolutions are allowed by observations, Big Brake becoming increasingly likely with the increase of the positive model parameter k.Comment: 8 pages, 6 figures, to be published in the Proceedings of the Invisible Universe International Conference, Paris, 2009, Ed. J. M. Alimi; v2: reference

Big Data Opportunities for Global Infectious Disease Surveillance

Simon Hay and colleagues discuss the potential and challenges of producing continually updated infectious disease risk maps using diverse and large volume data sources such as social media

Big data analytics in computational biology and bioinformatics

Big data analytics in computational biology and bioinformatics refers to an array of operations including biological pattern discovery, classification, prediction, inference, clustering as well as data mining in the cloud, among others. This dissertation addresses big data analytics by investigating two important operations, namely pattern discovery and network inference. The dissertation starts by focusing on biological pattern discovery at a genomic scale. Research reveals that the secondary structure in non-coding RNA (ncRNA) is more conserved during evolution than its primary nucleotide sequence. Using a covariance model approach, the stems and loops of an ncRNA secondary structure are represented as a statistical image against which an entire genome can be efficiently scanned for matching patterns. The covariance model approach is then further extended, in combination with a structural clustering algorithm and a random forests classifier, to perform genome-wide search for similarities in ncRNA tertiary structures. The dissertation then presents methods for gene network inference. Vast bodies of genomic data containing gene and protein expression patterns are now available for analysis. One challenge is to apply efficient methodologies to uncover more knowledge about the cellular functions. Very little is known concerning how genes regulate cellular activities. A gene regulatory network (GRN) can be represented by a directed graph in which each node is a gene and each edge or link is a regulatory effect that one gene has on another gene. By evaluating gene expression patterns, researchers perform in silico data analyses in systems biology, in particular GRN inference, where the “reverse engineering” is involved in predicting how a system works by looking at the system output alone. Many algorithmic and statistical approaches have been developed to computationally reverse engineer biological systems. However, there are no known bioin-formatics tools capable of performing perfect GRN inference. Here, extensive experiments are conducted to evaluate and compare recent bioinformatics tools for inferring GRNs from time-series gene expression data. Standard performance metrics for these tools based on both simulated and real data sets are generally low, suggesting that further efforts are needed to develop more reliable GRN inference tools. It is also observed that using multiple tools together can help identify true regulatory interactions between genes, a finding consistent with those reported in the literature. Finally, the dissertation discusses and presents a framework for parallelizing GRN inference methods using Apache Hadoop in a cloud environment

The Magnitude Of Big Data 5VS In Business Macroclimate

This paper discusses and features the age of Big Data in business macroclimate. The systematic review of influences the rapid changing business world today. This research explores Big Data and today’s business literature is used in this research to further understand the concepts of Big Data and how Big Data processes, models and the Internet way of utilizing data for breakthrough innovation to acclimate for the age of technology. This paper also examines the depth understanding Big Data and what will Big Data bring to our society and businesses is essential for managers and top management to fully utilize the Big Data for competitive advantage. Big Data 5V are discussed in context of business macroclimate and the Big Data influences towards business processes. The garnered interest of researchers on Big Data in business over the years is evaluated to understand the need to grasp an understanding of 5V and the conceptual framework of big data analytics in business decision making is formed. As a conclusion, the Big Data phenomenon is illustrated in business concepts and intelligence and utilizing Big Data for competitive advantages in the competitive business world with advance information age in this millennium

Update Tutorial: Big Data Analytics: Concepts, Technology, and Applications

In 2014, I wrote a paper on big data analytics that the Communications of the Association for Information Systems published (volume 34). Since then, we have seen significant advances in the technologies, applications, and impacts of big data analytics. While the original paper’s content remains accurate and relevant, with this new paper, I update readers on important, recent developments in the area

Impact of Big Data Analytics on Decision Making and Performance

‘Big Data’ has become a major topic of interest and discussion for both academics and professionals in the IT and business disciplines, and case evidence suggests that companies engaging in Big Data outperform others. It has to be noted though that ‘Bigger’ Data as such does not provide any benefits, but it is rather how organisations make sense of data and gain insights from analysing the data. Analytic capabilities and practices are required to convert Big Data (BD) into insights which arguably improve decision-making and thereby organisational performance. While protagonists of such Big Data Analytics (BDA) imply that those effects exist, so far they have not been confirmed by rigorous empirical research. Data was obtained using a cross-sectional online survey which targeted Chief Information Officers and senior IT managers of medium-to-large Australian for-profit organisations and yielded 163 complete responses, which met the standard criteria for measurement reliability and validity. PLS-SEM and multiple bootstrapping methods were used to test the hypotheses, while controlling for firm size. The present study empirically confirms claims made in the literature that BD and related analytics lead to better performance. It also reveals that such benefits are achieved primarily because BDA creates additional incentives for managers to base their decisions on analytics, and that more analytic-based decision making actually leads to superior performance. Finally, the results of our study suggest that managers in organisations which engage in BD are generally more analytics-minded in their decision making, even if the analytic tools and methods used in support of their decisions are not particularly sophisticated. The results provide evidence that neither Big Data nor Big Data Analytics are just ‘hypes’, but they do actually lead to superior performance, partly directly and partly indirectly by creating an incentive for managers to rely on analytics when making strategic or operational decisions. Interestingly, managers in smaller firms are more likely to base their decisions on analytics than larger firms, which suggests that they use analytics to compete against larger firms

The Magnitude Of Big Data 5VS In Business Macroclimate

This paper discusses and features the age of Big Data in business macroclimate. The systematic review of influences the rapid changing business world today. This research explores Big Data and today’s business literature is used in this research to further understand the concepts of Big Data and how Big Data processes, models and the Internet way of utilizing data for breakthrough innovation to acclimate for the age of technology. This paper also examines the depth understanding Big Data and what will Big Data bring to our society and businesses is essential for managers and top management to fully utilize the Big Data for competitive advantage. Big Data 5V are discussed in context of business macroclimate and the Big Data influences towards business processes. The garnered interest of researchers on Big Data in business over the years is evaluated to understand the need to grasp an understanding of 5V and the conceptual framework of big data analytics in business decision making is formed. As a conclusion, the Big Data phenomenon is illustrated in business concepts and intelligence and utilizing Big Data for competitive advantages in the competitive business world with advance information age in this millennium

Parallel classification and optimization of telco trouble ticket dataset

In the big data age, extracting applicable information using traditional machine learning methodology is very challenging. This problem emerges from the restricted design of existing traditional machine learning algorithms, which do not entirely support large datasets and distributed processing. The large volume of data nowadays demands an efficient method of building machine-learning classifiers to classify big data. New research is proposed to solve problems by converting traditional machine learning classification into a parallel capable. Apache Spark is recommended as the primary data processing framework for the research activities. The dataset used in this research is related to the telco trouble ticket, identified as one of the large volume datasets. The study aims to solve the data classification problem in a single machine using traditional classifiers such as W-J48. The proposed solution is to enable a conventional classifier to execute the classification method using big data platforms such as Hadoop. This study’s significant contribution is the output matrix evaluation, such as accuracy and computational time taken from both ways resulting from hyper-parameter tuning and improvement of W-J48 classification accuracy for the telco trouble ticket dataset. Additional optimization and estimation techniques have been incorporated into the study, such as grid search and cross-validation method, which significantly improves classification accuracy by 22.62% and reduces the classification time by 21.1% in parallel execution inside the big data environment