Machine Learning Algorithms for Big Data Applications With Policy Implementation

This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation.

A Dynamic Scaling Approach in Hadoop YARN

In Cloud based Big Data applications, Hadoop has been widely adopted for distributed processing large scale data sets. However, the wastage of energy consumption of data centers still constitutes an important axis of research due to overuse of resources and extra overhead costs. As a solution to overcome this challenge, a dynamic scaling of resources in Hadoop YARN Cluster is a practical solution. This paper proposes a dynamic scaling approach in Hadoop YARN (DSHYARN) to add or remove nodes automatically based on workload. It is based on two algorithms (scaling up/down) which are implemented to automate the scaling process in the cluster. This article aims to assure energy efficiency and performance of Hadoop YARN’ clusters. To validate the effectiveness of DSHYARN, a case study with sentiment analysis on tweets about covid-19 vaccine is provided. the goal is to analyze tweets of the people posted on Twitter application. The results showed improvement in CPU utilization, RAM utilization and Job Completion time. In addition, the energy has been reduced of 16% under average workload.

Reflections on the Innovation of University Scientific Research Management in the Era of Big Data

In China, universities are important centers for SR (scientific research) and innovation, and the quality of SR management has a significant impact on university innovation. The informatization of SR management is a critical component of university development in the big data environment. As a result, it is crucial to figure out how to improve SR management. As a result, this paper builds a four-tier B/W/D/C (Browser/Web/Database/Client) university SR management innovation information system based on big data technology and thoroughly examines the system’s hardware and software configuration. The SVM-WNB (Support Vector Machine-Weighted NB) classification algorithm is proposed, and the improved algorithm runs in parallel on the Hadoop cloud computing platform, allowing the algorithm to process large amounts of data efficiently. The optimization strategy proposed in this paper can effectively optimize the execution of scientific big data applications according to a large number of simulation experiments and real-world multidata center environment experiments.

Satellite Image Processing Systems: An Architectural Perspective

Satellite image processing systems include satellite image classification, long ranged data processing, yield prediction systems, etc. All of these systems require a large quantity of images for effective processing, and thus they are directed towards big-data applications. All these applications require a series of highly complex image processing and signal processing steps, which include but are not limited to image acquisition, image pre-processing, segmentation, feature extraction & selection, classification and post processing. Numerous researchers globally have proposed a large variety of algorithms, protocols and techniques in order to effectively process satellite images. This makes it very difficult for any satellite image system designer to develop a highly effective and application-oriented processing system. In this paper, we aim to categorize these large number of researches w.r.t. their effectiveness and further perform statistical analysis on the same. This study will assist researchers in selecting the best and most optimally performing algorithmic combinations in order to design a highly accurate satellite image processing system.

Preliminary Benefits of Big Data in the Construction Industry: A Case Study

Big data applications consist of i) data collection using big data sources, ii) storing and processing the data, and iii) analysing data to gain insights for creating organisational benefit. The influx of digital technologies and digitization in the construction process includes big data as one newly emerging digital technology adopted in the construction industry. Big data application is in a nascent stage in construction, and there is a need to understand the tangible benefit(s) that big data can offer the construction industry. This study explores the benefits of big data in the construction industry. Using a qualitative case study design, construction professionals in an Australian Construction firm were interviewed. The research highlights that the benefits of big data include reduction of litigation amongst projects stakeholders, enablement of near to real-time communication, and facilitation of effective subcontractor selection. By implication, on a broader scale, these benefits can improve contract management, procurement, and management of construction projects. This study contributes to an ongoing discourse on big data application, and more generally, digitization in the construction industry.

Importance of Big Data and Hadoop in E-Servicing

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

Modified Dominance-based soft set Approach for Feature Selection

Big data analysis applications in the field of medical image processing have recently increased rapidly. Feature reduction plays a significant role in eliminating irrelevant features and creating a successful research model for Big Data applications. Fuzzy clustering is used for the segment of the nucleus. Various features, including shape, texture, and color-based features, have been used to address the segmented nucleus. The Modified Dominance Soft Set Feature Selection Algorithm (MDSSA) is intended in this paper to determine the most important features for the classification of leukaemia images. The results of the MDSSA are evaluated using the variance analysis called ANOVA. In the dataset extracted function, the MDSSA selected 17 percent of the features that were more promising than the existing reduction algorithms. The proposed approach also reduces the time needed for further analysis of Big Data. The experimental findings confirm that the performance of the proposed reduction approach is higher than other approaches.

Big Data Analytics Strategy Framework: A Case of Crowd Management During the Hajj Pilgrimage, Mecca, Saudi Arabia

The objective of the present study was an investigation of applications of big data analytics in Hajj and Umrah for pilgrims, who come to Saudi Arabia every year for tourism and observation of religious rites as per the sacred beliefs of Islam. It has now become a necessity to see more applications of big data analytics in these pilgrimages because of the growing number of people every year. Therefore, crowd control, crowd management and conflict management are essential for reduction of stress, troubles, fatalities, accidents, theft and possible deaths during Hajj and Umrah events. Developing a predictive data analytic model for Hajj and Umrah will improve the efficiency, gross domestic product (GDP), surveillance, revenue generation, opportunities and satisfaction for the pilgrimages. In this paper, review of big data tools was presented along with their use in the decision support system and how it can be used for surveillance and crowd management. A robust big data framework applicable for Hajj and Umrah events was also presented in this paper. This was meant to aid seamless adoption and implementation of big data applications across sectors and government parastatals involved in Hajj and Umrah. The presented framework was also included all the relevant use cases related to these pilgrimages.