Journal articles: 'Analytics Application'

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G, Aravind, Varun K, and Manjunath C. R. Soumya K. N. "Application of Big Data Analytics with Evidence Based Medicine." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 440–44. http://dx.doi.org/10.31142/ijtsrd12979.

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Nagaraj, Samala. "Marketing Analytics for Customer Engagement." International Journal of Information Systems and Social Change 11, no. 2 (April 2020): 41–55. http://dx.doi.org/10.4018/ijissc.2020040104.

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Customer engagement is the buzz word in marketing discipline today. Engaging customers has never been as effective before the emergence of marketing analytics and its application. Marketing analytics coupled with social media and brand communities has given rise to improved innovative ways to engage customers across various service industries. The integration of marketing analytics with artificial intelligence (AI) has enhanced marketers understanding of customer engagement. The present article is a viewpoint on the various applications of marketing analytics for customer engagement. The present article focuses on the evolution of marketing analytics, its various models and application in various forms of customer engagement. The article highlights the future applications of analytics and concludes with the importance of marketing analytics for marketers in increasing customer engagement.

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Agrawal, Deepak. "Analytics based decision making." Journal of Indian Business Research 6, no. 4 (November 11, 2014): 332–40. http://dx.doi.org/10.1108/jibr-09-2014-0062.

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Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

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Ruipérez-Valiente, José, Pedro Muñoz-Merino, Díaz Pijeira, Ruiz Santofimia, and Carlos Kloos. "Evaluation of a learning analytics application for open edX platform." Computer Science and Information Systems 14, no. 1 (2017): 51–73. http://dx.doi.org/10.2298/csis160331043r.

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Massive open online courses (MOOCs) have recently emerged as a revolution in education. Due to the huge amount of users, it is difficult for teachers to provide personalized instruction. Learning analytics computer applications have emerged as a solution. At present, MOOC platforms provide low support for learning analytics visualizations, and a challenge is to provide useful and effective visualization applications about the learning process. At this paper we review the learning analytics functionality of Open edX and make an overview of our learning analytics application ANALYSE. We present a usability and effectiveness evaluation of ANALYSE tool with 40 students taking a Design of Telematics Applications course. The survey obtained very positive results in a system usability scale (SUS) questionnaire (78.44/100) in terms of the usefulness of visualizations (3.68/5) and the effectiveness ratio (92/100) of the actions required for the respondents. Therefore, we can conclude that the implemented learning analytics application is usable and effective.

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Pike, William, Joe Bruce, Bob Baddeley, Daniel Best, Lyndsey Franklin, Richard May, Douglas Rice, Rick Riensche, and Katarina Younkin. "The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics." Information Visualization 8, no. 1 (January 2009): 71–84. http://dx.doi.org/10.1057/ivs.2008.33.

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A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ while interacting with analytic environments. We introduce an application suite called the scalable reasoning system (SRS), which provides web-based and mobile interfaces for visual analysis. The service-oriented analytic framework that underlies SRS provides a platform for deploying pervasive visual analytic environments across an enterprise. SRS represents a ‘lightweight’ approach to visual analytics whereby thin client analytic applications can be rapidly deployed in a platform-agnostic fashion. Client applications support multiple coordinated views while giving analysts the ability to record evidence, assumptions, hypotheses and other reasoning artifacts. We describe the capabilities of SRS in the context of a real-world deployment at a regional law enforcement organization.

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Ghosh, Siddhartha, Akshat Agrawal, B. Ramu, S. Kishore Kumar, and N. Tharun Reddy. "Application of C in Data Analytics." Journal of Engineering Education Transformations 33 (January 31, 2020): 600. http://dx.doi.org/10.16920/jeet/2020/v33i0/150128.

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Kim, Jeong-ryeol, and Je-Young Lee. "English Learning Analytics and its Application." Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology 6, no. 9 (September 30, 2016): 321–30. http://dx.doi.org/10.14257/ajmahs.2016.09.15.

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Goncalves, Carlos, Luis Assuncao, and Jose C. Cunha. "Flexible MapReduce Workflows for Cloud Data Analytics." International Journal of Grid and High Performance Computing 5, no. 4 (October 2013): 48–64. http://dx.doi.org/10.4018/ijghpc.2013100104.

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Data analytics applications handle large data sets subject to multiple processing phases, some of which can execute in parallel on clusters, grids or clouds. Such applications can benefit from using MapReduce model, only requiring the end-user to define the application algorithms for input data processing and the map and reduce functions, but this poses a need to install/configure specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. In order to provide more flexibility in defining and adjusting the application configurations, as well as in the specification of the composition of the application phases and their orchestration, the authors describe an approach for supporting MapReduce stages as sub-workflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). The authors discuss how a text mining application is represented as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. Access to intermediate data produced during the MapReduce computations is supported by a data sharing abstraction. The authors describe two implementations of this abstraction, one based on a shared tuple space and another based on an in-memory distributed key/value store. The authors describe the implementation of the framework, a set of developed tools, and our experimentation with the execution of the text mining algorithm over multiple Amazon EC2 (Elastic Compute Cloud) instances, and report on the speed-up and size-up results obtained up to 20 EC2 instances and for different corpus sizes, up to 97 million words.

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KIM, JENNIFER, DAVID A. OSTROWSKI, HIROSHI YAMAGUCHI, and PHILLIP C. Y. SHEU. "SEMANTIC COMPUTING AND BUSINESS INTELLIGENCE." International Journal of Semantic Computing 07, no. 01 (March 2013): 87–117. http://dx.doi.org/10.1142/s1793351x13500013.

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With rapidly expanding data collections becoming increasingly available, the application of Semantic Computing has become imperative to leverage this resource for industrial applications. This paper presents a survey of Semantic Computing in the area of Business Intelligence. We examine semantic analytical techniques and tools as applied for prediction analysis and decision support. We also define the role of Semantic Computing as applied in the context of Data Mining, Text Mining and Big Data Analytics. Additionally, we describe how business data is queried with Structured Natural Language as well as the use of On-Line Analytic Processing.

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Wise, Alyssa, Yuting Zhao, and Simone Hausknecht. "Learning Analytics for Online Discussions: Embedded and Extracted Approaches." Journal of Learning Analytics 1, no. 2 (August 7, 2014): 48–71. http://dx.doi.org/10.18608/jla.2014.12.4.

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This paper describes an application of learning analytics that builds on an existing research program investigating how students contribute and attend to the messages of others in asynchronous online discussions. We first overview the E-Listening research program and then explain how this work was translated into analytics that students and instructors could use to reflect on their discussion participation. Two kinds of analytics were designed: some embedded in the learning environment to provide students with real-time information on their activity in-progress; and some extracted from the learning environment and presented to students in a separate digital space for reflection. In addition, we describe the design of an intervention though which use of the analytics can be introduced as an integral course activity. Findings from an initial implementation of the application indicated that the learning analytics intervention supported changes in students’ discussion participation. Five issues for future work on learning analytics in online discussions are presented. One, unintentional versus purposeful change; two, differing changes prompted by the same analytic; three, importance of theoretical buy-in and calculation transparency for perceived analytic value; four, affective components of students’ reactions; and five, support for students in the process of enacting analytics-driven changes.

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Edwards, James. "Application Review of Genogram Analytics, Demo Version." Journal of Technology in Human Services 27, no. 3 (August 4, 2009): 235–40. http://dx.doi.org/10.1080/15228830903093262.

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Kim, Tai Ki. "Application of learning analytics in classroom practice." Asian Journal of Education 21, no. 3 (September 30, 2020): 907–51. http://dx.doi.org/10.15753/aje.2020.09.21.3.907.

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Serrano-Laguna, Ángel, Javier Torrente, Pablo Moreno-Ger, and Baltasar Fernández-Manjón. "Application of Learning Analytics in educational videogames." Entertainment Computing 5, no. 4 (December 2014): 313–22. http://dx.doi.org/10.1016/j.entcom.2014.02.003.

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Sharma, Manu, and Sudhanshu Joshi. "Online Advertisement Using Web Analytics Software." International Journal of Business Analytics 7, no. 2 (April 2020): 13–33. http://dx.doi.org/10.4018/ijban.2020040102.

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This article describes a analytic-hierarchy-process (AHP) application to identify and evaluate the best online advertising analytics software. This technique is multi-criteria and used in this study by comparing the top four web advertising analytics software. AHP uses pair-wise comparison of matrices. There are six criteria identified for evaluation: Ad scheduling, ad targeting, creative banner rotation, features, performance, cost and for each criterion, a matrix of pair-wise comparison with web-analytics software i.e. Google analytics, Accenture Analytics, Funnel and, Moat Analytics were evaluated. AHP is an effective method for multi-objective decision-making, and optimization. Thus, it helps web advertisers to evaluate the existing web advertising analytics software for posting their web advertisements.

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Rodríguez-Pupo, Luis, Carlos Granell, and Sven Casteleyn. "An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics." ISPRS International Journal of Geo-Information 8, no. 2 (January 26, 2019): 54. http://dx.doi.org/10.3390/ijgi8020054.

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In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health.

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Orlando, Blair, and Yeqiang Lin. "Application of Data Analytics in the Events Industry." Events and Tourism Review 4, no. 1 (June 29, 2021): 1–13. http://dx.doi.org/10.18060/23958.

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This research examined the data analytics practices used within the events industry and the value of such applications. This study consisted of an interpretive case review of current companies within the events industry. The interview process explained the current practices being used to collect and analyze data. The common themes revealed data analytics are being used to evaluate, redesign, and enhance company performance, marketing strategy, decision guidelines, and economics. The study shows data collection and analysis is mostly focused on determining what consumers want and are looking for within the industry. The findings of this study support the importance of applying data analytics within industry-related companies to be financially successful and maintain market-share. Both the results from this study and the literature used indicate the significance of data analytics and the tremendous amount of opportunity buried beneath the application of data, although there is still room for growth.

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Kurniawan, Candra. "A Survey on Big Data Analytics Model." ITEJ (Information Technology Engineering Journals) 4, no. 1 (July 22, 2019): 1–13. http://dx.doi.org/10.24235/itej.v4i1.46.

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Topic about big data analytics have received a lot of attention and interest at this time. There are many topics can be discussed related to the analytical model, tools, and technology used. Big data analytics model involves many processes with various technologies used. Skills in handling big data, extracting mining, and developing insight are needed in applying big data analytics. Suitable analytical hardware and software also needed in decision making. Big data analytics is a key to a business strategy, but only a small portion of big data is currently used to support their business strategy. Big data analitycs can answer many questions about how to manage costs, time, and development or optimization strategies, and other decision making choices. However, there are many challenges in big data analytics technology. This survey paper addresses topics related to the analytical model, tools, and technology used. This paper also discusses the application of big data analytics in various fields.

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Srivastav, Abhilash, and Alok Chauhan. "SOCIAL NETWORK DATA RETRIEVAL USING SEMANTIC TECHNOLOGY." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 31. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19541.

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Social network data analysis is an important problem due to proliferation of social network applications, amount of data these applications generate and potential of insight based on this big data. The objective of present work is to propose architecture for a semantic web application to facilitate meaningful social network data analytics as well as answering query about concerned ontology. Proposed technique links, on one hand, tools based on semantic technology provided by social network applications with data analytics tools and on the other hand extends this link to ontology authoring tools for further inference. Results obtained from data analytics tool, results of query on generated ontology and benchmarking of the performance of data analytics tool are shown. It has been observed that a semantic web application utilizing above mentioned tools and technologies is more versatile and flexible and further improvements are possible by applying generic data mining algorithms to the above scenario.

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Kogan, Alexander, Brian W. Mayhew, and Miklos A. Vasarhelyi. "Audit Data Analytics Research—An Application of Design Science Methodology." Accounting Horizons 33, no. 3 (June 1, 2019): 69–73. http://dx.doi.org/10.2308/acch-52459.

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SYNOPSIS This introduction to Audit Data Analytics Research overviews the forum's five articles that showcase recent advances in audit data analytics technology and methodology. The articles are discussed through the prism of design science research that originates in engineering and computer science. In contrast with natural and social sciences that aim to develop and test theories about the world, the objective of design science is to create new artifacts that are useful for solving important practical problems. In audit research, design science methodology was originally used implicitly in early studies devoted to developing and evaluating audit analytical procedures and audit sampling techniques. The recent advances in information technology necessitate renewed attention to this research methodology especially given the profound changes in accounting, auditing, and business processes currently underway.

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A.S, Prakaash, and Sivakumar K. "Data Analytics and Predictive Modelling In the Application of Big Data: A Systematic Review." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (February 20, 2019): 395–99. http://dx.doi.org/10.5373/jardcs/v11sp11/20193047.

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Raghupathi, Viju, Yilu Zhou, and Wullianallur Raghupathi. "Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis." International Journal of Healthcare Information Systems and Informatics 14, no. 4 (October 2019): 1–20. http://dx.doi.org/10.4018/ijhisi.2019100101.

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In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

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Upendran, Deepthi, Shiffon Chatterjee, S. Sindhumol, and Kamal Bijlani. "Application of Predictive Analytics in Intelligent Course Recommendation." Procedia Computer Science 93 (2016): 917–23. http://dx.doi.org/10.1016/j.procs.2016.07.267.

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Clarke, Roger. "Guidelines for the responsible application of data analytics." Computer Law & Security Review 34, no. 3 (June 2018): 467–76. http://dx.doi.org/10.1016/j.clsr.2017.11.002.

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Andrienko, Gennady, Natalia Andrienko, and Ulrich Bartling. "Visual Analytics Approach to User-Controlled Evacuation Scheduling." Information Visualization 7, no. 1 (February 28, 2008): 89–103. http://dx.doi.org/10.1057/palgrave.ivs.9500174.

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Application of the ideas of visual analytics is a promising approach to supporting decision making, in particular, where the problems have geographic (or spatial) and temporal aspects. Visual analytics may be especially helpful in time-critical applications, which pose hard challenges to decision support. We have designed a suite of tools to support transportation-planning tasks such as emergency evacuation of people from a disaster-affected area. The suite combines a tool for automated scheduling based on a genetic algorithm with visual analytics techniques allowing the user to evaluate tool results and direct its work. A transportation schedule, which is generated by the tool, is a complex construct involving geographical space, time, and heterogeneous objects (people and vehicles) with states and positions varying in time. We apply task-analytical approach to design techniques that could effectively support a human planner in the analysis of this complex information.

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Akinnagbe, Akindele, K. Dharini Amitha Peiris, and Oluyemi Akinloye. "Prospects of Big Data Analytics in Africa Healthcare System." Global Journal of Health Science 10, no. 6 (May 8, 2018): 114. http://dx.doi.org/10.5539/gjhs.v10n6p114.

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Big data is having a positive impact in almost every sphere of life, such as in military intelligence, space science, aviation, banking, and health. Big data is a growing force in healthcare. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa. Healthcare systems in Africa are, in relative terms, behind the rest of the world. Platforms and technologies used to amass big data such as the Internet and mobile phones are already in use in Africa, thereby making big data applications to be emerging. Hence, the key research question we address is whether big data applications can improve healthcare in Africa especially during epidemics and through the public health system. In this study, a literature review is carried out, firstly to present cases of big data applications in healthcare in Africa, and secondly, to explore potential ethical challenges of such applications. This review will provide an update on the application of big data in the health sector in Africa that can be useful for future researchers and health care practitioners in Africa.

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Yin, Jiarui, and Vicenc Fernandez. "A systematic review on business analytics." Journal of Industrial Engineering and Management 13, no. 2 (May 18, 2020): 283. http://dx.doi.org/10.3926/jiem.3030.

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Purpose: Business analytics, a buzzword of the recent decade, has been applied by thousands of enterprises to help generate more values and enhance their business performance. However, many aspects of business analytics remain unclear. This study clarifies the definition of business analytics combined with its functionality and the relation between business analytics and business intelligence. Moreover, we illustrate the applications of business analytics in both business areas and industry sectors and shed light on the education in business analytics. Ultimately, to facilitate future research, we summarize several research techniques used in the literature reviewed.Design/methodology/approach: We set well-established selection criteria to select relevant literature from two widely recognized databases: Scopus and Web of Science. Afterward, we reviewed the literature and coded relevant sections in an inductive way using MAXQDA. Then we compared and synthesized the coded information.Findings: There are mainly four findings. Firstly, according to the bibliometric analysis, literature about business analytics is growing exponentially. Secondly, business analytics is a system that enabled by machine learning techniques aiming at promoting the efficiency and performance of an organization by supporting the decision-making process. Thirdly, the application of business analytics is comprehensive, not only in specific areas of a company but also in different industry sectors. Finally, business analytics is interdisciplinary, and the successful training should involve technical, analytical, and business skills.Originality/value: This systematic review, as a synthesis of the current research on business analytics, can serve as a quick guide for new researchers and practitioners in the field, while experienced scholars can also benefit from this work, taking it as a practical reference.

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Pham, Linh Manh, Truong-Thang Nguyen, and Tien-Quang Hoang. "Towards an Elastic Fog-Computing Framework for IoT Big Data Analytics Applications." Wireless Communications and Mobile Computing 2021 (August 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/3833644.

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IoT applications have been being moved to the cloud during the last decade in order to reduce operating costs and provide more scalable services to users. However, IoT latency-sensitive big data streaming systems (e.g., smart home application) is not suitable with the cloud and needs another model to fit in. Fog computing, aiming at bringing computation, communication, and storage resources from “cloud to ground” closest to smart end-devices, seems to be a complementary appropriate proposal for such type of application. Although there are various research efforts and solutions for deploying and conducting elasticity of IoT big data analytics applications on the cloud, similar work on fog computing is not many. This article firstly introduces AutoFog, a fog-computing framework, which provides holistic deployment and an elasticity solution for fog-based IoT big data analytics applications including a novel mechanism for elasticity provision. Secondly, the article also points out requirements that a framework of IoT big data analytics application on fog environment should support. Finally, through a realistic smart home use case, extensive experiments were conducted to validate typical aspects of our proposed framework.

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Kobayashi, Vladimer Birondo, Stefan Mol, and Gabor Kismihok. "Labour Market Driven Learning Analytics." Journal of Learning Analytics 1, no. 3 (December 2, 2014): 207–10. http://dx.doi.org/10.18608/jla.2014.13.24.

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This paper briefly outlines a project about integrating labour market information in a goal-setting learning analytics application that provides guidance to students in their transition from education to employment.

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Vaidya, Pranav Vilas, Janaki Meena M, and Syed Ibrahim Sp. "CLOUD-BASED DATA ANALYTICS FRAMEWORK FOR MOBILE APP EVENT ANALYSIS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 207. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19639.

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Mobile analytics studies the behavior of end users of mobile applications and the mobile application itself. These mobile applications, being an important part of the various businesses products, need to be monitored and the usage patterns are to be analyzed. The data collected from these apps can help to drive important business strategies by identifying the usage patterns. Enriching the data with information available from other sources, like sales/service information, provides holistic view about the solution. Thus, here we aim at exploring some set of tools that give capabilities as event trailing with higher extraction of its linguistics. If the application is used worldwide, the data generated out of it is Big Data, which traditional systems cannot handle. We therefore propose a special framework for efficient data collection, storage and processing at Big Data scale on cloud platform.

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Teixeira, João Marcelo Xavier Natário, Lucas Figueiredo, Lucas Maggi, Veronica Teichrieb, Marcel Santos, and Cristiano Araújo. "An Analytics Framework for Augmented Reality Applications." Journal on Interactive Systems 9, no. 2 (August 29, 2018): 1. http://dx.doi.org/10.5753/jis.2018.699.

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Analytics is a well-known form of capturing information about the user behavior of an application. Augmented reality applications deal with specific data such as the camera pose, not being supported by popular analytics frameworks. To fill such gap, this work proposes an analytics framework solution for augmented reality applications. It supports both markerbased and markerless augmented reality scenarios, collecting data related to camera pose and time spent by the user on each position. Besides the multiplatform capture tool, the framework provides a data analysis visualization tool capable of highlighting the most visited 3D positions, users main areas of interest over the marker plane, the 3D path performed by the camera and also a recovery of the content viewed by the user based on the collected camera pose information. Tests were performed using as case study a promotional campaign scenario and user behavior information was extracted using the proposed visualization tools.

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Levenson, Alec, and Alexis Fink. "Human capital analytics: too much data and analysis, not enough models and business insights." Journal of Organizational Effectiveness: People and Performance 4, no. 2 (June 5, 2017): 145–56. http://dx.doi.org/10.1108/joepp-03-2017-0029.

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Purpose The purpose of this paper is to address the barriers to the rapid development of effective HR analytics capabilities in organizations. Design/methodology/approach Literature and conceptual review of the current state of HR analytics. Findings “HR analytics” is used to refer to a too-wide array of measurement and analytical approaches, making strategic focus difficult. There is a misconception that doing more measurement of HR activities and human capital will necessarily lead to actionable insights. There is too much focus on incremental improvement of existing HR processes, detracting from diagnosing the problems with business performance. Too much time is spent on mining existing data, to the detriment of model building and testing, including collecting new more appropriate data. Too much energy is consumed with basic tasks of data management. Stakeholders avoid action by nitpicking the details of the data. Practical implications Practitioners who follow the guidance provided should find that their application of HR analytics leads to more relevant and actionable insights. Social implications More effective application of HR analytics should lead to better decision making in organizations and more effective resource allocation. Originality/value A new look at the field of HR analytics that synthesizes the research literature and current practice in organizations.

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Lao, L. J., and B. J. Harder. "GATEWAY: A GEOSPATIAL ANALYTICS SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 283–88. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-283-2019.

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Abstract. This paper presents an overview of the Gateway web platform, a proprietary geospatial analytics system developed by Cobena Business Analytics and Strategy, Inc.1 The application is intended to serve as a user-friendly and easily-accessible tool for spatial data analysis and visualization geared toward non-technical specialists. Gateway’s core functionalities hinge on mapping and data visualization (choropleths and points) alongside traditional scoring methods and built-in machine learning algorithms for area prioritization and site selection. Gateway provides an interactive, cloud-based environment that abstracts and simplifies common location-based analyses. A core strength of the platform is also its heavy localization to the Philippine context through a curated database of market information — with future plans to create local counterparts across SEA — which reduces the need for extensive external market data collection and reconciliation. The paper gives a brief review of the system design and key features of the platform. It also highlights some key applications across industries such as real estate, consumer goods, and retail in informing expansion and distribution strategies, prioritizing resource allocation, and analyzing historical performance against market factors.

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Mohamad Samuri, Suzani, Dorroity Anak Emang, Rahmadi Agus, Bahbibi Rahmatullah, Nurul Salini Mohamed Salleh, and Mazlina Che Mustafa. "LACLOD: Learning Analytics for Children’s Logic Development." International journal of Multimedia & Its Applications 13, no. 2 (April 30, 2021): 1–14. http://dx.doi.org/10.5121/ijma.2021.13201.

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Learning Analytics for Children's Logic Development (LACLOD) is a web-based and mobile friendly learning analytic platform for assessing the logic development of children age 3 to 4 years old in TASKA PERMATA UPSI Malaysia. The platform is developed using Unity and connected through Google Analytics (GA) plugin where it tracked the user interaction for the application. LACLOD is designed only for mobile or tablet which is using Android. In this paper, the development of this learning analytic platform is presented. For evaluation of this system, observation and survey have been used, to get the feedback from 2 teachers (female) and 3 children (2 female and 1 male). Based on the evaluation, it can be seen that there are still rooms for improvement. Female children found it quit hard to understand the game but the male children looked satisfy because he knew on how to navigate the app and he actively played the app by himself. As for teachers, the acceptance to this kind of assessment is moderate, however they agree that this application can better improve the children’s learning especially in logic development.

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Sangwan, Neeti, and Vishal Bhatnagar. "Comprehensive Contemplation of Probabilistic Aspects in Intelligent Analytics." International Journal of Service Science, Management, Engineering, and Technology 11, no. 1 (January 2020): 116–41. http://dx.doi.org/10.4018/ijssmet.2020010108.

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In Big Data analysis, the application of machine learning has proven to be a revolutionary. The systematic review of literature shows that research has been carried out on the domain of big data analytics particularly text analytics with the inclusion of machine learning approaches. This extensive survey deals with the data at hand that provides different ways and issues while combining the machine learning approaches with the text. During the course of the survey, various publications in the field of synchronous application of machine learning in text analytics were searched and studied. Classification framework is proposed as the contribution of machine learning in text analytics. A classification framework represented the various application areas to motivate researchers for future research on the application of two emerging technologies.

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Juola, Patrick. "Authorship Studies and the Dark Side of Social Media Analytics." JUCS - Journal of Universal Computer Science 26, no. 1 (January 28, 2020): 156–70. http://dx.doi.org/10.3897/jucs.2020.009.

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The computational analysis of documents to learn about their authorship (also known as authorship attribution and/or authorship profiling) is an increasingly important area of research and application of technology. This paper discusses the technology, focusing on its application to social media in a variety of disciplines. It includes a brief survey of the history as well as three tutorial case studies, and discusses several significant applications and societal benefits that authorship analysis has brought about. It further argues, though, that while the benefits of this technology have been great, it has created serious risks to society that have not been sufficiently considered, addressed, or mitigated.

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Alavi, Shirin, and Vandana Ahuja. "Digital Marketing Analytics." International Journal of Innovation in the Digital Economy 5, no. 4 (October 2014): 50–65. http://dx.doi.org/10.4018/ijide.2014100104.

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Technological advances and the speed with which new technologies are being embraced by organizations, along with the rising power of the consumers and their ability to get what they want, when they want it, from whomever they want, have opened up new challenges for customer relationship management and marketing. Thus the need for understanding the digital world and its application becomes one of the greatest competitive aspects for a business's survival. The exhortation of globalization holds no meaning without the concept of what is being termed as ‘Digitization'. Blackberry has started a long and hard climb to regain its lost glory. Supporting its product improvement and repositioning strategies are a set of well-defined digital marketing strategies. This manuscript explores the dynamics of Inside Blackberry-an online endeavour of Blackberry to trace the E-Marketing objectives of the Blog and its ability to leverage the behavioral internet theory for online branding, building usability and reciprocity, strengthening credibility and consumer persuasion.

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Panda, Bijaya Kumar. "Application of business model innovation for new enterprises." Journal of Management Development 39, no. 4 (November 7, 2019): 517–24. http://dx.doi.org/10.1108/jmd-11-2018-0314.

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Purpose The purpose of this paper is to study the details of new age digital business using a freemium business model. Design/methodology/approach Study of the various prospects of various digital business firms like revenues, customer base, share price, ranks. Uses of freemium business model to hold on to existing customers and attract new customers. Findings Innovative service or product offerings and growth strategy is the base of this business model. So businesses must assess innovation strategy before deciding whether to opt the freemium business model or not. Retaining the existing user and constant addition of new users are the founding stone of the freemium business model. So, the value offerings have to be well perceived by the customer so that switching costs will be increased for them and the customer will remain loyal. Originality/value Analyzing consumer behavior with recent analytical tools and techniques such as web analytics, bigdata analytics are required in order to get deeper market knowledge. It is crucial to get the knowledge of recent trends of markets, the perception of customer and customer’s journey mapping in order to run a business with freemium model.

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Hassani, Hossein, Christina Beneki, Stephan Unger, Maedeh Taj Mazinani, and Mohammad Reza Yeganegi. "Text Mining in Big Data Analytics." Big Data and Cognitive Computing 4, no. 1 (January 16, 2020): 1. http://dx.doi.org/10.3390/bdcc4010001.

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Chaparro-Peláez, Julián, Santiago Iglesias-Pradas, Francisco J. Rodríguez-Sedano, and Emiliano Acquila-Natale. "Extraction, Processing and Visualization of Peer Assessment Data in Moodle." Applied Sciences 10, no. 1 (December 24, 2019): 163. http://dx.doi.org/10.3390/app10010163.

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Situated in the intersection of two emerging trends, online self- and peer assessment modes and learning analytics, this study explores the current landscape of software applications to support peer assessment activities and their necessary requirements to complete the learning analytics cycle upon the information collected from those applications. More particularly, the study focuses on the specific case of Moodle Workshops, and proposes the design and implementation of an application, the Moodle Workshop Data EXtractor (MWDEX) to overcome the data analysis and visualization shortcomings of the Moodle Workshop module. This research paper details the architecture design, configuration, and use of the application, and proposes an initial validation of the tool based on the current peer assessment practices of a group of learning analytics experts. The results of the small-scale survey suggest that the use of software tools to support peer assessment is not so extended as it would initially seem, but also highlight the potential of MWDEX to take full advantage of Moodle Workshops.

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Denning, Peter J. "Working Set Analytics." ACM Computing Surveys 53, no. 6 (February 2021): 1–36. http://dx.doi.org/10.1145/3399709.

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The working set model for program behavior was invented in 1965. It has stood the test of time in virtual memory management for over 50 years. It is considered the ideal for managing memory in operating systems and caches. Its superior performance was based on the principle of locality, which was discovered at the same time; locality is the observed tendency of programs to use distinct subsets of their pages over extended periods of time. This tutorial traces the development of working set theory from its origins to the present day. We will discuss the principle of locality and its experimental verification. We will show why working set memory management resists thrashing and generates near-optimal system throughput. We will present the powerful, linear-time algorithms for computing working set statistics and applying them to the design of memory systems. We will debunk several myths about locality and the performance of memory systems. We will conclude with a discussion of the application of the working set model in parallel systems, modern shared CPU caches, network edge caches, and inventory and logistics management.

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Alhazmi, Eman A., Walaa A. Bajunaid, and Fahd S. Alotaibi. "Real-Time Big Data Analytics: Investigating Different Application Domains." IARJSET 4, no. 7 (July 20, 2017): 100–108. http://dx.doi.org/10.17148/iarjset.2017.4716.

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Dremliuga, Roman I., and Vadim V. Reshetnikov. "LEGAL ASPECTS OF PREDICTIVE ANALYTICS APPLICATION IN LAW ENFORCEMENT." Азиатско-Тихоокеанский регион: экономика, политика, право, no. 3 (2018): 133–44. http://dx.doi.org/10.24866/1813-3274/2018-3/133-144.

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Iqbal, Rahat, Faiyaz Doctor, Brian More, Shahid Mahmud, and Usman Yousuf. "Big data analytics: Computational intelligence techniques and application areas." Technological Forecasting and Social Change 153 (April 2020): 119253. http://dx.doi.org/10.1016/j.techfore.2018.03.024.

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Park, Deukhee, Woo Gon Kim, and Soojin Choi. "Application of social media analytics in tourism crisis communication." Current Issues in Tourism 22, no. 15 (July 29, 2018): 1810–24. http://dx.doi.org/10.1080/13683500.2018.1504900.

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Wojtkowiak, Krzysztof Juliusz. "DATA MINING ANALYTICS FUNDAMENTALS AND THEIR APPLICATION IN LOGISTICS." Acta Universitatis Nicolai Copernici. Zarządzanie 47, no. 1 (September 30, 2020): 47. http://dx.doi.org/10.12775/aunc_zarz.2020.1.005.

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MORRISON, Shaunna M., Anirudh PRABHU, Ahmed ELEISH, Feifei PAN, Hao ZHONG, Fang HUANG, Peter FOX, et al. "Application of Advanced Analytics and Visualization in Mineral Systems." Acta Geologica Sinica - English Edition 93, S3 (May 2019): 55. http://dx.doi.org/10.1111/1755-6724.14243.

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Flood, Mark D., Victoria L. Lemieux, Margaret Varga, and B. L. William Wong. "The application of visual analytics to financial stability monitoring." Journal of Financial Stability 27 (December 2016): 180–97. http://dx.doi.org/10.1016/j.jfs.2016.01.006.

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Angadi, Ravi V., P. S. Venkataramu, and Suresh Babu Daram. "Role of Big Data Analytics in Power System Application." E3S Web of Conferences 184 (2020): 01017. http://dx.doi.org/10.1051/e3sconf/202018401017.

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Power system sector is the back bone for any country economic growth. In current years, electric power systems have experienced various challenges and technological innovations and have become digitalized with the introduction concept of smart grids. Power systems are being operated in a stressed condition mainly due to the ever increasing load demand, depleting energy resources and environmental constraints on Transmission line expansion. This article focus mainly role of Big Data in various industrialization in brief and specifically applied in the power system studies along with other sectors. Also focuses on using very large data collections, which are difficult to access in standard database systems and also refers to as big data, to manage and monitor the power system. System stability is an significant goal for power engineers to use this huge amount of data to run the system in their rated capacity, power sector can beneficial of various potential solicitation of power system by the use of large-scale data analysis that can help improve the optimization process and helps for the power system to operate in the effective manner.

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Strehlitz, Beate, Christine Reinemann, Soeren Linkorn, and Regina Stoltenburg. "Aptamers for pharmaceuticals and their application in environmental analytics." Bioanalytical Reviews 4, no. 1 (December 17, 2011): 1–30. http://dx.doi.org/10.1007/s12566-011-0026-1.

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Bayrak, Tuncay. "Identifying Technical Requirements for a Mobile Business Analytics Application." International Journal of Business Analytics 8, no. 4 (October 2021): 91–103. http://dx.doi.org/10.4018/ijban.2021100106.

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This study identifies various technical requirements for business analytics applications designed and optimized for mobile devices. Such applications would enable the mobile workforce to gain business insights regardless of their physical locations. In order for the mobile workforce to be efficient and effective, they need to be able to use the browsers and applications designed specifically for the mobile devices to access data, similar to desktop computers. Based on the research, such factors as online and off-line data analysis capabilities, data visualization capabilities, and security issues appear to be important factors to be addressed carefully by developers.

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Journal articles on the topic 'Analytics Application'

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