Journal articles: 'Cognitive analytics'

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Handfield, Robert, Seongkyoon Jeong, and Thomas Choi. "Emerging procurement technology: data analytics and cognitive analytics." International Journal of Physical Distribution & Logistics Management 49, no. 10 (December 10, 2019): 972–1002. http://dx.doi.org/10.1108/ijpdlm-11-2017-0348.

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Purpose The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics? Design/methodology/approach This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field. Findings The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture. Originality/value While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.

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Casimiro, Leni T. "Cognitive Engagement in Online Intercultural Interactions: Beyond Analytics." International Journal of Information and Education Technology 6, no. 6 (2016): 441–47. http://dx.doi.org/10.7763/ijiet.2016.v6.729.

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Narayana, T., Sohail Shaik, and S. Kaur. "Predictive Analytics – The Cognitive Analysis." Oriental journal of computer science and technology 10, no. 1 (March 23, 2017): 187–93. http://dx.doi.org/10.13005/ojcst/10.01.25.

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Predictive analytics plays an important role in the decision-making process and intuitive business decisions, by overthrowing the traditional instinct process. Predictive analytics utilizes data-mining techniques in order to predict the future outcomes with a high level of certainty. This advanced branch of data engineering is composed of various analytical and statistical methods which are used to develop models that predict the future occurrences. This paper examines the concepts of predictive analytics and various mining methods to achieve the prior. In conclusion, paper discusses process and issues involved in the knowledge discovery process.

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Earley, Seth. "Cognitive Computing, Analytics, and Personalization." IT Professional 17, no. 4 (July 2015): 12–18. http://dx.doi.org/10.1109/mitp.2015.55.

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Wang, Yingxu, and Jun Peng. "Big Data Analytics." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 2 (April 2017): 41–56. http://dx.doi.org/10.4018/ijcini.2017040103.

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Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.

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Ozdal, Muhammet Mustafa, Serif Yesil, Taemin Kim, Andrey Ayupov, John Greth, Steven Burns, and Ozcan Ozturk. "Graph Analytics Accelerators for Cognitive Systems." IEEE Micro 37, no. 1 (January 2017): 42–51. http://dx.doi.org/10.1109/mm.2017.7.

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Rika, Havana, Itzhak Aviv, and Roye Weitzfeld. "Unleashing the Potentials of Quantum Probability Theory for Customer Experience Analytics." Big Data and Cognitive Computing 6, no. 4 (November 10, 2022): 135. http://dx.doi.org/10.3390/bdcc6040135.

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In information systems research, the advantages of Customer Experience (CX) and its contribution to organizations are largely recognized. The CX analytics evaluate how customers perceive products, ranging from their functional usage to their cognitive states regarding the product, such as emotions, sentiment, and satisfaction. The most recent research in psychology reveals that cognition analytics research based on Classical Probability Theory (CPT) and statistical learning, which is used to evaluate people’s cognitive states, is limited due to their reliance on rational decision-making. However, the cognitive attitudes of customers are characterized by uncertainty and entanglement, resulting in irrational decision-making bias. What is captured by traditional CPT-based data science in the context of cognition aspects of CX analytics is only a small portion of what should be captured. Current CX analytics efforts fall far short of their full potential. In this paper, we set a novel research direction for CX analytics by Quantum Probability Theory (QPT). QPT-based analytics have been introduced recently in psychology research and reveal better cognition assessment under uncertainty, with a high level of irrational behavior. Adopting recent advances in the psychology domain, this paper develops a vision and sets a research agenda for expanding the application of CX analytics by QPT to overcome CPT shortcomings, identifies research areas that contribute to the vision, and proposes elements of a future research agenda. To stimulate debate and research QPT-CX analytics, we attempt a preliminary characterization of the novel method by introducing a QPT-based rich mathematical framework for CX cognitive modeling based on quantum superposition, Bloch sphere, and Hilbert space. We demonstrate the implementation of the QPT-CX model by the use case of customers’ emotional motivator assessments while implementing quantum vector space with a set of mathematical axioms for CX analytics. Finally, we outline the key advantages of quantum CX over classical by supporting theoretical proof for each key.

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Fisher, Brian, Tera Marie Green, and Richard Arias-Hernández. "Visual Analytics as a Translational Cognitive Science." Topics in Cognitive Science 3, no. 3 (April 11, 2011): 609–25. http://dx.doi.org/10.1111/j.1756-8765.2011.01148.x.

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Arias-Hernandez, Richard, Tera M. Green, and Brian Fisher. "From Cognitive Amplifiers to Cognitive Prostheses: Understandings of the Material Basis of Cognition in Visual Analytics." Interdisciplinary Science Reviews 37, no. 1 (March 2012): 4–18. http://dx.doi.org/10.1179/0308018812z.0000000001.

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Rostamzadeh, Neda, Sheikh S. Abdullah, and Kamran Sedig. "Visual Analytics for Electronic Health Records: A Review." Informatics 8, no. 1 (February 23, 2021): 12. http://dx.doi.org/10.3390/informatics8010012.

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The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.

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Cybulski, Jacob L., Susan Keller, and Dilal Saundage. "Interactive Exploration of Data with Visual Metaphors." International Journal of Software Engineering and Knowledge Engineering 25, no. 02 (March 2015): 231–52. http://dx.doi.org/10.1142/s0218194015400082.

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Visual Analytics (VA) is an approach to data analysis by means of visual manipulation of data representation, which relies on innate human abilities of perception and cognition. Even though current visual toolkits in the Business Analytics (BA) domain have improved the effectiveness of data exploration, analysis and reporting, their features are often not intuitive, and can be confusing and difficult to use. Moreover, visualizations generated from these toolkits are mostly accessible to specialist users. Thus, there is a need for analytic environments that support data exploration, interpretation and communication of insight that do not add to the cognitive load of the analyst and their non-technical clients. In this conceptual paper, we explore the potential of primary metaphors, which arise out of human lived and sensory-motor experiences, in the design of immersive visual analytics environments. Primary metaphors provide ideas for representation of time, space, quantity, similarity, actions and team work. Using examples developed in our own work, we also explain how to combine such metaphors to create complex and cognitively acceptable visual metaphors, such as 3D data terrains that approximate our intuition of reality and create opportunities for data to be viewed, navigated, explored, touched, changed, discussed, reported and described to others, individually or collaboratively.

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Kurzhals, Kuno, Brian Fisher, Michael Burch, and Daniel Weiskopf. "Eye tracking evaluation of visual analytics." Information Visualization 15, no. 4 (July 26, 2016): 340–58. http://dx.doi.org/10.1177/1473871615609787.

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The application of eye tracking for the evaluation of humans’ viewing behavior is a common approach in psychological research. So far, the use of this technique for the evaluation of visual analytics and visualization is less prominent. We investigate recent scientific publications from the main visualization and visual analytics conferences and journals, as well as related research fields that include an evaluation by eye tracking. Furthermore, we provide an overview of evaluation goals that can be achieved by eye tracking and state-of-the-art analysis techniques for eye tracking data. Ideally, visual analytics leads to a mixed-initiative cognitive system where the mechanism of distribution is the interaction of the user with the visualization environment. Therefore, we also include a discussion of cognitive approaches and models to include the user in the evaluation process. Based on our review of the current use of eye tracking evaluation in our field and the cognitive theory, we propose directions for future research on evaluation methodology, leading to the grand challenge of developing an evaluation approach to the mixed-initiative cognitive system of visual analytics.

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Ahmad, Faizan, Zeeshan Ahmed, and Sara Muneeb. "Effect of Gaming Mode Upon the Players' Cognitive Performance During Brain Games Play." International Journal of Game-Based Learning 11, no. 1 (January 2021): 67–76. http://dx.doi.org/10.4018/ijgbl.2021010105.

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An improvement in cognitive performance through brain games play is implicit yet progressive. It is necessary to explore factors that potentially accelerate this improvement process. Like various other significant yet unexplored aspects, it is equally essential to establish a performative (fusion of accuracy and efficiency) insight about players' cognition (memory, vision, and analytics) among the different modes of brain games. This paper presents empirical research that investigates the impact of different modes of brain games (single vs. multiplayer) upon the players' cognitive performance. An accumulated result of the research revealed that the cognitive performance in memory stimulating and visual activity-oriented brain games play significantly boosts during multiplayer mode. Similarly, cognitive accuracy in analytical brain gameplay also increases during the multiplayer mode; however, it's rather inefficient. In addition, both the components of cognitive performance in single-player mode are reported as negatively correlated, while in the multiplayer mode it's rather contrary.

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Bivens, Alan, and Hari Ramasamy. "Preface: Cognitive and Contextual Analytics for IT Services." IBM Journal of Research and Development 61, no. 1 (January 1, 2017): 0:1–0:4. http://dx.doi.org/10.1147/jrd.2017.2648458.

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Vuckovic, Milena, and Johanna Schmidt. "On Sense Making and the Generation of Knowledge in Visual Analytics." Analytics 1, no. 2 (October 2, 2022): 98–116. http://dx.doi.org/10.3390/analytics1020008.

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Interactive visual tools and related visualization technologies, built to support explorative data analysis, ultimately lead to sense making and knowledge discovery from large volumes of raw data. These processes namely rely on human visual perception and cognition, in which human analysts perceive external representations (system structure, dataset, integral data visualizations) and form respective internal representations (internal cognitive imprints of external systems) that enable deeper comprehension of the employed system and the underlying data features. These internal representations further evolve through continuous interaction with external representations. They also depend on the individual’s own cognitive pathways. Currently, there has been insufficient work on understanding how these internal cognitive mechanisms form and function. Hence, we aim to offer our own interpretations of such processes observed through our daily data exploration workflows. This is accomplished by following specific explorative data science tasks while working with diverse interactive visual systems and related notebook style environments that have different organizational structures and thus may entail different approaches to thinking and shaping sense making and knowledge generation. In this paper, we deliberate on the cognitive implications for human analysists when interacting with such a diverse organizational structure of tools and approaches when performing the essential steps of an explorative visual analysis.

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Yi, John, Christina Kang-Yi, Flavia Burton, and H. Chen. "Predictive Analytics Approach to Improve and Sustain College Students’ Non-Cognitive Skills and Their Educational Outcome." Sustainability 10, no. 11 (November 2, 2018): 4012. http://dx.doi.org/10.3390/su10114012.

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The application of predictive analytics in higher education has increasingly gained acceptance and interest over the years. In this study, a predictive model is developed to map students’ non-cognitive skills against their class performance. Our predictive analytics model identified the non-cognitive skills that predicted new students’ class performance based on the dataset collected early in the semester. Based on the predictive analytics results, tailored teaching to improve students’ non-cognitive skills was offered in a required class designed for undergraduate business students. The improvement in the average final semester grade for students in the tailored-taught classes based on our predicted analytics approach was 9%, which was higher than that of the class grade taught without the approach. The study finding also demonstrates a long-term, sustainable positive effect to the students with the predictive analytics approach.

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Markov, B. V. "COGNITIVE PRACTICES OF ANALYTICAL ANTHROPOLOGY." Humanities And Social Studies In The Far East 17, no. 3 (2020): 22–29. http://dx.doi.org/10.31079/1992-2868-2020-17-3-22-29.

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The article is devoted to the problem of comparing analytical practices of modern philosophy. Such a problem was put in the philosophy of cognition of L.A. Mikeshina. She thoroughly studied phenomenology, hermeneutics, analytical philosophy, structuralistict and post-structuralistic concepts. Her book identifies the boundaries and possibilities of modern philosophy. The originality of L.A. Mikeshina's program is to connect hermeneutics with cognitive theories. This program is continued in the article. It is supplemented by the study of methods of philosophical anthropology. Analytical practices of philosophy are formed on the basis of book culture and are designed to analyze texts. The development of a figurative, "screen" culture generates the need for analytics and conceptualization of visual discourse. The question of how we navigate images is solved by searching for words that express the meaning of the visible. The article reveals the specifics of the study of images in visual anthropology. The object of analysis is also music, design, architecture. A set of analytical techniques for analysis of medial culture is presented. A project to study the cultural space, in the context of which philosophical concepts are formed, has been proposed.

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Barua, Shaibal, Mobyen Uddin Ahmed, and Shahina Begum. "Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification." Brain Sciences 10, no. 8 (August 6, 2020): 526. http://dx.doi.org/10.3390/brainsci10080526.

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One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform ‘intelligent multivariate data analytics’ based on machine learning (ML). Here, the k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., sequential forward floating selection (SFFS) and random forest have been applied where out of 323 features, a subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source.

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Güçlü, Burçin, and Miguel-Ángel Canela. "The Destructive Effect of Complex Analytics on Innovativeness." International Journal of Applied Behavioral Economics 6, no. 4 (October 2017): 38–56. http://dx.doi.org/10.4018/ijabe.2017100103.

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Several studies have raised a common concern in the field of management, the lack of innovation. However, they either attribute this phenomenon to the inefficiency of marketing analytics, or to managerial despair in evaluating innovation projects. In this article, the authors propose and empirically test cognitive effort spent on marketing analytics which can lead to the lack of innovativeness, due to the negative impact of high cognitive effort on the managers' mood. In a longitudinal experiment, where manipulating the complexity of the decision context through marketing analytics, the authors demonstrate that managers employing simple marketing analytics expect their competitors to launch more products, compared to managers using complex marketing analytics. They also demonstrate that firms employing simple marketing analytics behave venturesome by embarking upon innovative activities. At the same time, firms using complex marketing analytics take more deliberative actions by innovating less and amplifying short-term gains with high priced products.

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Tripathy, Amiya K., Jyothi M. Rao, and Vijaya Bharathi Jagan. "Predictive Analytics of User Cognitive Styles in Online Shopping." International Journal of Computational Intelligence Studies 11, no. 2 (2022): 1. http://dx.doi.org/10.1504/ijcistudies.2022.10050222.

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Rousopoulou, Vaia, Thanasis Vafeiadis, Alexandros Nizamis, Ioannis Iakovidis, Leonidas Samaras, Alkis Kirtsoglou, Konstantinos Georgiadis, Dimosthenis Ioannidis, and Dimitrios Tzovaras. "Cognitive analytics platform with AI solutions for anomaly detection." Computers in Industry 134 (January 2022): 103555. http://dx.doi.org/10.1016/j.compind.2021.103555.

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Zhang, Yin, Huimin Lu, and Haider Abbas. "Mobile Intelligence Assisted by Data Analytics and Cognitive Computing." Wireless Communications and Mobile Computing 2018 (November 8, 2018): 1–2. http://dx.doi.org/10.1155/2018/4302012.

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Choi, Younyoung, and Jigeun Kim. "Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis." Sustainability 13, no. 18 (September 10, 2021): 10149. http://dx.doi.org/10.3390/su131810149.

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A learner’s cognitive load is highly associated with their academic achievement within learning systems. Diagnostic information about a learner’s cognitive load is useful for achieving optimal learning, by enabling the learner to manage and control their cognitive load in the e-learning environment. However, little empirical research has been conducted to obtain diagnostic information about the cognitive load in e-learning systems. The purpose of this study was to analyze a personalized diagnostic evaluation for a learner’s cognitive load in an e-learning system, using the Bayesian Network (BN) as a learning analytic method. Data from 700 learners were collected from Cyber University. A learner’s cognitive load level was measured in terms of three components: extraneous cognitive load, intrinsic cognitive load, and germane cognitive load. The BN was built by representing the relationship among the extraneous cognitive load, intrinsic cognitive load, germane cognitive load, and academic achievement. The conditional and marginal probabilities in the BN were estimated. This study found that the BN provided diagnostic information about a learner’s level of cognitive load in the e-learning system. In addition, the BN predicted the learner’s academic achievement in terms of their different cognitive load patterns. This study’s results imply that diagnostic information related to cognitive load helps learners to improve academic achievement by managing and controlling their cognitive loads in the e-learning environment. In addition, instructional designers are able to offer more appropriately customized instructional methods by considering learners’ cognitive loads in online learning.

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Syahreza, Dina Sarah. "Studi Literatur Analisis Sumber Daya Manusia: Sistematisasi Topik dan Arah Penelitian dari Literatur Terpilih." Reslaj : Religion Education Social Laa Roiba Journal 1, no. 2 (December 7, 2019): 253–64. http://dx.doi.org/10.47467/reslaj.v1i2.399.

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Literature studies related to human resources, but they have not been systemized in a focused topic. The way human resources are managed today is heavily influenced by the emergence of a global workforce and the increasing relevance of business analytics as an organizational strategic capability. While human resource analytics has been mostly discussed in the literature in the past decades, however, the systematic identification and classification of major topics has not been introduced. Where there is room for conceptual contributions that aim to provide a comprehensive definition of concepts and research areas related to HR analytics in the future. This study uses a systematic literature through a review process. Next, the researcher constructs the concept of human resource analytics as widely presented. There were 91 main research topics identified related to the three majors, namely HR analytics (technology and organization), application (descriptive) and diagnostic/prescriptive), and values (employee values and organizational values). We also speculate on an “exponential” view of HR analytics made possible by assertion of artificial intelligence and cognitive technology. This research provides a major systematization effort and future research directions to develop further studies in the field of HR analytics. Where this research offers insights to support the design of innovative analytic projects within organizations. Keywords: Human Resources, Systematization, Literature Study

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Chen, Bodong. "From Theory Use to Theory Building in Learning Analytics: A Commentary on “Learning Analytics to Support Teachers during Synchronous CSCL”." Journal of Learning Analytics 2, no. 2 (December 7, 2015): 163–68. http://dx.doi.org/10.18608/jla.2015.22.12.

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In this commentary on Van Leeuwen (this issue), I explore the relation between theory and practice in learning analytics. Specifically, I caution the issue of adhering to one specific theoretical doctrine while ignoring others, suggest deeper application of the cognitive load theory to understanding teaching with analytics tools, and comment on issues with theory building in the nascent field of learning analytics.

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Bashir, Ali Kashif, Shahid Mumtaz, Varun G. Menon, and Kim Fung Tsang. "Guest Editorial: Cognitive Analytics of Social Media for Industrial Manufacturing." IEEE Transactions on Industrial Informatics 17, no. 4 (April 2021): 2899–901. http://dx.doi.org/10.1109/tii.2020.3028762.

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Ravichandran, T., and Liang Zhao. "Cognitive Complexity, Digitized Analytics Capability, CEO Incentives and Competitive Aggressiveness." Academy of Management Proceedings 2021, no. 1 (August 2021): 14189. http://dx.doi.org/10.5465/ambpp.2021.14189abstract.

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Baciu, George, Yungzhe Wang, and Chenhui Li. "Cognitive Visual Analytics of Multi-Dimensional Cloud System Monitoring Data." International Journal of Software Science and Computational Intelligence 9, no. 1 (January 2017): 20–34. http://dx.doi.org/10.4018/ijssci.2017010102.

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Hardware virtualization has enabled large scale computational service delivery models with high cost leverage and improved resource utilization on cloud computing platforms. This has completely changed the landscape of computing in the last decade. It has also enabled large–scale data analytics through distributed high performance computing. Due to the infrastructure complexity, end–users and administrators of cloud platforms can rarely obtain a full picture of the state of cloud computing systems and data centers. Recent monitoring tools enable users to obtain large amounts of data with respect to many utilization parameters of cloud platforms. However, they fail to get the maximal overall insight into the resource utilization dynamics of cloud platforms. Furthermore, existing tools make it difficult to observe large-scale patterns, making it difficult to learn from the past behavior of cloud system dynamics. In this work, the authors describe a perceptual-based interactive visualization platform that gives users and administrators a cognitive view of cloud computing system dynamics.

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Lytras, Miltiadis, Anna Visvizi, Xi Zhang, and Naif Radi Aljohani. "Cognitive computing, Big Data Analytics and data driven industrial marketing." Industrial Marketing Management 90 (October 2020): 663–66. http://dx.doi.org/10.1016/j.indmarman.2020.03.024.

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V.V. Kishore, P., SK Azma, K. Gayathri, A. S.C.S. Sastry, E. Kiran Kumar, and D. Anil Kumar. "SWIFT cognitive behavioral assessment model built on cognitive analytics of empirical mode internet of things." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 377. http://dx.doi.org/10.14419/ijet.v7i1.1.9856.

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This paper introduces a study and analysis to predict the present human behaviour through his/her object interactions in the physical environment. The physical environment consists of a door, chair and telephone with accelerometer sensors attached to them and connected to computer using a raspberry pi IoT(Internet of Things) kit. Two other parameters used for assessment are human voice intensities and human motion analysis through a motion capture camera with inbuilt microphone and Wi-Fi module. The dataset is a collection of accelerometer data from chair and telephone, human interaction with door through camera and voice sample of a word ‘Hello’. These 4 parameter measurements are collected from 15 test subjects in the age group 19-21 without their knowledge. We used the dataset to train and test 3 predominant behaviours in the chosen age group namely, excitable, assertive and pleasant on an artificial neural network with backpropagation training algorithm. The overall recognition accuracy is 84.89% based on the physical assessment from a physiatrist of all the test subjects. This study can help individuals, doctors and machines to predict the current human emotional state and provide feedback to modify unpleasant current state of behaviour to a pleasant state to maximize human performance.

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Lazaroiu, George, Armenia Androniceanu, Iulia Grecu, Gheorghe Grecu, and Octav Neguriță. "Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing." Oeconomia Copernicana 13, no. 4 (December 30, 2022): 1047–80. http://dx.doi.org/10.24136/oc.2022.030.

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Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including ?cognitive Industrial Internet of Things?, ?cognitive automation?, ?cognitive manufacturing systems?, ?cognitively-enhanced machine?, ?cognitive technology-driven automation?, ?cognitive computing technologies,? and ?cognitive technologies.? The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

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Grübel, Jascha, Tyler Thrash, Leonel Aguilar, Michal Gath-Morad, Julia Chatain, Robert W. Sumner, Christoph Hölscher, and Victor R. Schinazi. "The Hitchhiker’s Guide to Fused Twins: A Review of Access to Digital Twins In Situ in Smart Cities." Remote Sensing 14, no. 13 (June 27, 2022): 3095. http://dx.doi.org/10.3390/rs14133095.

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Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.

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Pauleen, David J. "Dave Snowden on KM and big data/analytics: interview with David J. Pauleen." Journal of Knowledge Management 21, no. 1 (February 13, 2017): 12–17. http://dx.doi.org/10.1108/jkm-08-2016-0330.

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Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

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Siew, Cynthia. "Investigating Cognitive Network Models of Learners’ Knowledge Representations." Journal of Learning Analytics 9, no. 1 (March 11, 2022): 120–29. http://dx.doi.org/10.18608/jla.2022.7671.

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This commentary discusses how research approaches from Cognitive Network Science can be of relevance to research in the field of Learning Analytics, with a focus on modelling the knowledge representations of learners and students as a network of interrelated concepts. After providing a brief overview of research in Cognitive Network Science, I suggest that a focus on the cognitive processes that occur in the knowledge network, as well as the mechanisms that give rise to changes in the structure of knowledge networks, can lead to potentially informative insights into how learners navigate their knowledge representations to retrieve information and how the knowledge representations of learners develop and grow over the course of their educational careers. Learning Analytics can leverage these insights to design adaptive learning or online learning platforms that optimize learning, and inform pedagogical practice and assessment design that support the development of effective and robust knowledge structures.

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Boysen, Anne. "Mine the Gap: Augmenting Foresight Methodologies with Data Analytics." World Futures Review 12, no. 2 (February 24, 2020): 239–48. http://dx.doi.org/10.1177/1946756720905639.

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The explosion of Big Data and analytic tools in recent years has brought new opportunities to the field of foresight. Big Data and improved analytics capabilities can expand the knowledge base and act as a corrective to our cognitive biases. Moreover, several data mining and machine learning techniques that increase performance for businesses can be applied in foresight to help researchers discover patterns that may be early signals of change and correct our misperception of patterns where they don’t exist. This article discusses the opportunities and limitations of various data mining and machine learning techniques in foresight.

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Kamran, Maira, Marium Malik, Muhammad Waseem Iqbal, Muhammad Anwar, Muhammad Aqeel, and Sana Ahmad. "Web Simplification Prototype for Cognitive Disabled Users." Human Behavior and Emerging Technologies 2022 (March 9, 2022): 1–14. http://dx.doi.org/10.1155/2022/5817410.

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Information and communication technology (ICT) and World Wide Web (WWW) are increasingly being used in daily life and becoming important in community, business, personal performance, and improvement of livelihood. people with disabilities (PWDs) can easily perform many tasks using WWW which might be difficult or impossible for them. However, many websites applications such as e-learning, e-commerce, and e-government are not specifically designed keeping in view PWD users. Through the web accessibility guidelines, web developers can build a web program accessible to PWDs. In this paper, we have investigated the issues related to website design that make it unavailable for PWDs. Keeping in view these issues, we have built a framework to make the web easier for PWDs. In addition, these issues are assessed using the GTmetrix, Netcraft, and WAVE accessibility tools and the results are generated using Google Analytics. Based on these results, we have proposed a simplified web version to improve website access for people with disabilities. The proposed prototype is also implemented on a website called Easywebcare by incorporating our recommendations for resolving the investigated issues. Analytics shows that the proposed type surpasses all existing activities in improving website accessibility for people with disabilities.

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Ahmad, Fawad. "A systematic review of the role of Big Data Analytics in reducing the influence of cognitive errors on the audit judgement." Revista de Contabilidad 22, no. 2 (July 1, 2019): 187–202. http://dx.doi.org/10.6018/rcsar.382251.

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This systematic literature review provides the association between memory processes, auditors judgement and decision-making process under the influence of cognitive errors. Due to limited cognitive resources, auditors are unable to analyze the population of accounting transactions, therefore, they use sampling and heuristics for information processing. In the context of Big Data (BD), auditors may face a similar problem of information overload and exhibit cognitive errors, resulting in the selection and analysis of irrelevant information cues. But Big Data analytics (BDA) can facilitate information processing and analysis of complex diverse Big Data by reducing the influence of auditor’s cognitive errors. The current study adapts Ding et al., (2017) framework in the auditing context that identify causes of cognitive errors influencing auditor’s information processing. This review identified 75 auditing related studies to elaborate the role of BD and BDA in improving audit judgement. In addition, role of memory, cognitive errors, and judgement and decision-making are highlighted by using 61 studies. The analysis provides useful insight in different open areas by proposing research propositions and research questions that can be explored by future research to gain extensive understanding on the association between memory and audit judgement in the context of BD and BDA. La revisión sistemática de la literatura proporciona la asociación entre los procesos de la memoria, el juicio de los auditores y el proceso de toma de decisiones bajo la influencia de errores cognitivos. Debido a los limitados recursos cognitivos, los auditores no pueden analizar la población de transacciones contables; por lo tanto, utilizan el muestreo y la heurística para el procesamiento de la información. En el contexto de Big Data (BD), los auditores pueden enfrentarse a un problema similar de sobrecarga de información y exhibir errores cognitivos, lo que resulta en la selección y análisis de indicios de información irrelevantes. No obstante, la analítica de Big Data (BDA) puede facilitar el procesamiento de información y el análisis de datos complejos y diversos al reducir la influencia de los errores cognitivos del auditor. El presente estudio adapta el marco de trabajo de Ding et al (2017) en el contexto de la auditoría que identifica las causas de los errores cognitivos que influyen en el procesamiento de la información del auditor. Esta revisión identificó 75 estudios relacionados con la auditoría para elaborar el papel de BD y BDA en la mejora del juicio de auditoría. Además, el papel de la memoria, los errores cognitivos y el juicio y la toma de decisiones se destacan mediante el uso de 61 estudios. El análisis proporciona una visión útil de los diferentes aspectos abiertos de la cuestión proponiendo propuestas y preguntas de estudio que puedan ser exploradas por la investigación futura para obtener una comprensión amplia de la asociación entre la memoria y el juicio de auditoría en el contexto de BD y BDA.

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Ponraj, Jayaprakash, and A. Chandra Mohan. "Evolving Competitive Electricity Markets: Enablement through Advanced Analytics and Cognitive Solutions." Advances in Computational Sciences and Technology 10, no. 5 (May 30, 2017): 657. http://dx.doi.org/10.37622/acst/10.5.2017.657-669.

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Garrett, M. A. "Big Data analytics and cognitive computing – future opportunities for astronomical research." IOP Conference Series: Materials Science and Engineering 67 (October 29, 2014): 012017. http://dx.doi.org/10.1088/1757-899x/67/1/012017.

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Elhoseny, M., M. Kabir Hassan, and Mirjana Pejic-Bach. "Special Issue on “Cognitive Big Data Analytics for Intelligent Information Systems”." Information Systems and e-Business Management 18, no. 4 (November 6, 2020): 485–86. http://dx.doi.org/10.1007/s10257-020-00483-3.

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Larmuseau, Charlotte, Jan Cornelis, Luigi Lancieri, Piet Desmet, and Fien Depaepe. "Multimodal learning analytics to investigate cognitive load during online problem solving." British Journal of Educational Technology 51, no. 5 (May 22, 2020): 1548–62. http://dx.doi.org/10.1111/bjet.12958.

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Heer, Jeffrey, and Maneesh Agrawala. "Design Considerations for Collaborative Visual Analytics." Information Visualization 7, no. 1 (February 21, 2008): 49–62. http://dx.doi.org/10.1057/palgrave.ivs.9500167.

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Visualizations leverage the human visual system to support the process of sensemaking, in which information is collected, organized, and analyzed to generate knowledge and inform action. Although most research to date assumes a single-user focus on perceptual and cognitive processes, in practice, sensemaking is often a social process involving parallelization of effort, discussion, and consensus building. Thus, to fully support sensemaking, interactive visualization should also support social interaction. However, the most appropriate collaboration mechanisms for supporting this interaction are not immediately clear. In this article, we present design considerations for asynchronous collaboration in visual analysis environments, highlighting issues of work parallelization, communication, and social organization. These considerations provide a guide for the design and evaluation of collaborative visualization systems.

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Tarhini, Abbas, Puzant Balozain, and F. Jordan Srour. "Emergency management system design for accurate data: a cognitive analytics management approach." Journal of Enterprise Information Management 34, no. 2 (February 2, 2021): 697–717. http://dx.doi.org/10.1108/jeim-11-2019-0366.

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PurposeThis paper uses a cognitive analytics management approach to analyze, understand and solve the problems facing the implementation of information systems and help management do the needed changes to enhance such a critical process; the emergency management system in the health industry is analyzed as a case study.Design/methodology/approachCognitive analytics management (CAM) framework (Osman and Anouz, 2014) is used. Cognitive process: The right questions are asked to understand the behavior of every process and the flow of its corresponding data; critical data variables were identified, guidelines for identifying data sources were set. Analytics process: Techniques of data analytics were applied to the selected data sets, problems were identified in user–system interaction and in the system design. The analysis process helped the management in the management process to make right decisions for the right change.FindingsUsing the CAM framework, the analysis to the Lebanese Red Cross case study identified system user-behavior problems and also system design problems. It identified cases where distributed subsystems are vulnerable to time keeping errors and helped the management make knowledgeable decisions to overcome major obstacles by implementing several changes related to hardware design, software implementation, human resource training, operational and human-technology changes. CAM is a novel and feasible software engineering approach for handling system failures.Originality/valueThe paper uses CAM framework as an approach to overcome system failures and help management do the needed changes to enhance such a critical process. This work contributes to the software engineering literature by introducing CAM as a new agile methodology to be used when dealing with system failures. Furthermore, this study is an action research that validated the CAM theoretical framework in a health emergency context in Lebanon.

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Carvalho, Adriana Ventura, Amal Chouchene, Tânia M. Lima, and Fernando Charrua-Santos. "Cognitive Manufacturing in Industry 4.0 toward Cognitive Load Reduction: A Conceptual Framework." Applied System Innovation 3, no. 4 (December 3, 2020): 55. http://dx.doi.org/10.3390/asi3040055.

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Cognitive manufacturing utilizes cognitive computing, the industrial Internet of things (IoT), and advanced analytics to upgrade manufacturing processes in manners that were not previously conceivable. It enables associations to improve major business measurements, for example, productivity, product reliability, quality, and safety, while decreasing downtime and lowering costs. Considering all the facts that can prejudice the manufacturing performance in Industry 4.0, the cognitive load has received more attention, since it was previously neglected with respect to manufacturing industries. This paper aims to investigate what causes cognitive load reduction in manufacturing environments, i.e., human–computer interaction technologies that reduce the identified causes and the applications of cognitive manufacturing that use the referred technologies. Thus, a conceptual framework that links cognitive manufacturing to a reduction of the cognitive load was developed.

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Weiser, Orli, Yoram M. Kalman, Carmel Kent, and Gilad Ravid. "65 competencies." Communications of the ACM 65, no. 3 (March 2022): 58–66. http://dx.doi.org/10.1145/3467018.

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Perelmuter, Zeev. "Nous and Two Kinds of Epistêmê in Aristotle’s Posterior Analytics." Phronesis 55, no. 3 (2010): 228–54. http://dx.doi.org/10.1163/156852810x506003.

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AbstractAt the beginning of Posterior Analytics 2.19 Aristotle reminds us that we cannot claim demonstrative knowledge (epistêmê apodeiktikê) unless we know immediate premisses, the archai of demonstrations. By the end of the chapter he explains why the cognitive state whereby we get to know archai must be Nous. In between, however, Aristotle describes the process of the acquisition of concepts, not immediate premisses. How should we understand this? There is a general agreement that it is Nous by means of which we acquire both premisses and concepts. I argue that this cannot be the case. Since concepts are simples while premisses are composites (predications of concepts), the two cannot be objects of the same cognitive state. I further argue that, whereas Nous is responsible for our grasp of concepts, the state Aristotle elsewhere calls non-demonstrable knowledge is the one whereby we get to know the premisses of demonstrations.

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Mohd Ramli, Izzat Syahir, Siti Mistima Maat, and Fariza Khalid. "The design of game-based learning and learning analytics." Cypriot Journal of Educational Sciences 17, no. 5 (May 30, 2022): 1742–59. http://dx.doi.org/10.18844/cjes.v17i5.7326.

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Game-based learning has received increasing attention in recent years as it could help improve pupils’ motivation, self-efficacy, and achievement. Technological innovations like learning analytics (LA) and GBL offer pedagogical support for teachers. GBL could significantly support pupils’ learning as a learning approach compared to conventional approaches. Therefore, there is a need to elevate “ teachers’ level of knowledge on the impact of GBL. In the meantime, LA could be used to collect, analyze, and report data on the impact of GBL on pupils’ learning performance. In this light, GBL applications have been developed to facilitate the use of LA for teaching and learning. This paper describes the design of GBL with LA integration for teaching mathematics in primary schools. It documents the construction of the GBL and AL app, which is grounded on the Dick, Carey, and Carey Model and the theory of constructivism. In addition, the cognitive load theory was applied to ensure that the application accommodates pupils’ cognitive load. This study also validated the design of the GBL, and it was found to be relevant and engaging. Keywords: Game-based learning, mathematics, analytics, technology, education

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JONES, PHILIP, and JOHN CULLIS. "‘Individual Failure’ and the Analytics of Social Policy." Journal of Social Policy 29, no. 1 (January 2000): 73–93. http://dx.doi.org/10.1017/s0047279400005821.

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Social policy appraisal depends on assumptions that are made about individual motivation and individual behaviour. In recent years greater weight has been given to behavioural characteristics which are typical of homo-economicus. At the same time, a growing body of evidence from experimental economics and from cognitive psychology identifies a ‘richer’ characterisation of individual behaviour. Allowance must be made for ‘individual failure’ and for preference endogeneity when designing and implementing social policy. A ‘broader’ agenda is required when evaluating social policies. When designing social policy greater emphasis must be placed on the ‘demonstration effect’.

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Omer, Uzma, Muhammad Shoaib Farooq, and Adnan Abid. "Cognitive Learning Analytics Using Assessment Data and Concept Map: A Framework-Based Approach for Sustainability of Programming Courses." Sustainability 12, no. 17 (August 27, 2020): 6990. http://dx.doi.org/10.3390/su12176990.

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Students of initial level programming courses generally face difficulties while learning the programming concepts. The learning analytics studies, in these courses, are mostly anecdotal on the aspect of assessment as less or no attention is given to assess learning at various cognitive levels of specific concepts. Furthermore, the existing work reflects deficiencies in examining the effect of learners’ cognitive performance on subsequent stages of the course. This gap needs to be addressed by introducing more granular and methodical approaches of cognitive analysis for sustaining the programming courses effectively in computer science and associated disciplines. In this article, a framework-based approach is proposed for cognitive learning analytics on the concepts taught in initial level programming courses. The framework serves as a platform that provides structure to the concept data using the technique of concept mapping and examines learners’ cognitive propagation on related concepts using assessment data. Learners’ performance prediction has been examined on relatively higher-level programming concepts through the metrics established from the cognitive maps of learners, acquired by deploying the related layers of framework. Overall maximum prediction accuracy range obtained was 64.81% to 90.86%, which was better than the prediction accuracies presented in most of the related studies.

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Amin Husni, Nur Hanis, Nurul Farhana Jumaat, and Zaidatun Tasir. "Investigating Student’s Cognitive Engagement, Motivation and Cognitive Retention in Learning Management System." International Journal of Emerging Technologies in Learning (iJET) 17, no. 09 (May 10, 2022): 184–200. http://dx.doi.org/10.3991/ijet.v17i09.29727.

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Online learning has grown significantly in recent years, due to the proliferation of online courses offered by universities. As a result, pedagogical guidance is critical for improving teaching quality in an online learning environment. In this study, Inquiry-Based Learning (IBL) activities were developed with the aim of increasing students' cognitive engagement, cognitive retention, and motivation to learn. This study also investigated the effects of the IBL pedagogical method on student performance. A pre-experimental study was conducted with 16 undergraduate students enrolled on a Multimedia Design and Human Computer Interaction course. Learning Analytics (LA) was used to retrieve research data from a Learning Management System (LMS) online discussion forum, and the data was then analyzed using content analysis and data mining analysis. According to the findings, students perceived a higher rate of low-level cognitive engagement but, interestingly, a high level of cognitive retention and motivation. Meanwhile, a significant relationship was observed between students' cognitive engagement, motivation and cognitive retention.

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Journal articles on the topic 'Cognitive analytics'

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