Analytics Technologies Research Articles

KEY INFORMATION TECHNOLOGIES OF INDUSTRY 4.0

In the last ten years, three new information technologies have emerged, representing the realization of old (by the time of emergence) theoretical concepts, but new in creativity and innovation, as well as incredible potential for the realization of revolutionary ideas and changes in all areas of human activity, which by one name they call 4th Industrial Revolution or Industry 4.0. They are: Big Data Analytics technology, Cloud Computing and Blockchain technology. Each of them individually represents the realization of new paradigms of computer data processing, which could not be practically implemented until the development of information and communication technologies, especially the Internet, Web applications, new operating systems, network technologies, mobile telephony, technologies of advancement of hardware performances, internet of things, etc. The primary aim of this paper is to get acquainted with the characteristics of these technologies and their potentials, their advantages and disadvantages in relation to traditional technologies, identification of areas and ways of their both current and future applications. The ultimate goal is to explain the incredible potential of these technologies, creating the foundation for their combined application in the creation of new Industry 4.0 products, which should result in a synergistic effect.

Salespeople Performance Evaluation with Predictive Analytics in B2B

Performance Evaluation is a process that occurs multiple times per year on a company. During this process, the manager and the salesperson evaluate how the salesperson performed on numerous Key Performance Indicators (KPIs). To prepare the evaluation meeting, managers have to gather data from Customer Relationship Management System, Financial Systems, Excel files, among others, leading to a very time-consuming process. The result of the Performance Evaluation is a classification followed by actions to improve the performance where it is needed. Nowadays, through predictive analytics technologies, it is possible to make classifications based on data. In this work, the authors applied a Naive Bayes model over a dataset that is composed by sales from 594 salespeople along 3 years from a global freight forwarding company, to classify salespeople into pre-defined categories provided by the business. The classification is done in 3 classes, being: Not Performing, Good, and Outstanding. The classification was achieved based on KPI’s like growth volume and percentage, sales variability along the year, opportunities created, customer base line, target achievement among others. The authors assessed the performance of the model with a confusion matrix and other techniques like True Positives, True Negatives, and F1 score. The results showed an accuracy of 92.50% for the whole model.

Data set of balance of Russia's regional economy in 2005-2024 based on the methodology of calculation of "underdevelopment whirlpools".

The article shows the possibilities of Data Set “Interactive Statistics and Intelligent Analytics of the Balanced State of the Regional Economy of Russia in Terms of Big Data and Blockchain – 2020”. For creation of the data set, we formed time rows of the values of the selected indicators, which characterize the balance of Russia's regional economy. The indicators are systematized and classified into two categories. The first category includes the indicators of the level of socio-economic development: GDP per capita and “underdevelopment whirlpools”, balanced financial result of companies’ activities, population's employment level, and balance of the regional budget, calculated by finding the difference between revenues and expenditures. The second category includes the indicators of potential of socio-economic development: investments into fixed capital per capita, share of innovations-active organizations, share of innovative products, and digitization level. The data are collected in a table, based on which – with the help of programming and web-design – a data set is created – an interactive platform for working with data. The data set is available in Russian and English at the official web-site of the Institute of Scientific Communications [12]. The data set unifies and transfers into the digital form the data on the level and potential of Russia's regions’ potential of socio-economic development. The data set allows for flexible setting of data for any research. The data set allows – based on the statistics of a region's socio-economic development – determining its position in the Russian national ranking, category by the “TRMS” matrix, calculating the integral index for each region, and comparing its values. Using the proprietary methodology of calculation of “underdevelopment whirlpools” of Prof. Popkova, the data set reflects the balance of Russia's regional economy in 2005–2024. The data set allows for automatic visualization of data by creating a blockchain polygon of region's socio-economic development, which includes the chronogram of the region's development and the process of region's pulling into an “underdevelopment whirlpool”. The data set's data are presented in the form of an interactive map of Russia's regional economy. Map's color shows categories that are assigned to regions and the borders of regions, as well as information on each region's position in the 2020 rating. The data set allows for large-scale studies of Russia's regional economy with application of technologies of Big Data processing, machine learning, and intellectual analytics.

Craniometric and Volumetric Analyses of Cranial Base and Cranial Vault Differences in Patients With Nonsyndromic Single-Suture Sagittal Craniosynostosis.

How different from "normal" are the cranial base and vault of infants with nonsyndromic, single-suture sagittal synostosis (NSSS)? This study quantitatively addresses this question utilizing computed tomography (CT) analytic technology. Head CT scans of infants with NSSS and normocephalic controls were analyzed using Mimics to calculate craniometric angles, distances, and segmented volumes. Craniometric measurements and asymmetry indices were compared between NSSS and control groups using linear regressions controlling for age. Ratios of anterior-, middle-, and posterior-to-total cranial vault volume were compared between groups using beta regressions controlling for age. Seventeen patients with NSSS and 19 controls were identified. Cranial index and interoccipital angle were significantly smaller in NSSS compared with controls (P = 0.003 and <0.001, respectively). Right-but not left-external acoustic meatus angle and internal acoustic meatus-to-midline distance were significantly greater in NSSS than in controls (P = 0.021 and 0.016, respectively). NSSS patients and controls did not significantly differ in any asymmetry indices, except for the articular fossa angle asymmetry index (P = 0.016). Anterior vault volume proportion was greater in NSSS relative to controls (proportion ratio = 1.63, P < 0.001). NSSS trended toward a smaller posterior vault volume proportion (P = 0.068) yet did not differ in middle vault volume proportion compared with controls. In this small study, patients with nonsyndromic, single-suture sagittal craniosynostosis had relatively similar cranial base measurements, and larger anterior vault volumes, when compared with controls. Further work is needed to confirm the possibility of rightward asymmetry of the anterior cranial base.

Big Data Analytics Technology Capability and Data-Driven Decision Making in Malaysian Higher Education Institutions: A Conceptual Framework

In the era of big data, many organizations are aiming to become more data-driven and increase their decision-making efficiency. Nevertheless, there is an insufficient investigation that explores the antecedents of data-driven decision making (DDDM) in the context of Malaysian Higher Education Institutions (HEIs). Therefore, the study examines existing literature and utilises the Big Data Analytics Technology Capability (BDATC) dimensions such as connectivity, compatibility and modularity to conceptualize a DDDM framework. The study utilises Resource-Based Theory (RBT) to highlight the key dimensions for BDATC and how these dimensions are being integrate in order to establish effective DDDM for excellent performance. The antecedents of DDDM is a relatively new approach that is documented in literature, where literature seems to be diversified in terms of offering theoretical and conceptual frameworks, together with a model that Higher Education Institutions (HEIs) can utilize. The study chooses HEIs that undergo Malaysian Research Assessment Instrument (MyRA) as the focus of investigation. The data was collected from the key informants of Malaysian HEIs. In conclusion, the contribution of the study is to highlight the influence of BDATC for DDDM to attain better performance of HEIs in Malaysia.

Guest Editorial: Industry Ingenuity and Care in a Crisis

Just a few months into a new decade and the impact of the COVID-19 pandemic, combined with oil market turmoil, is causing undeniable concern for the wellbeing of the workforce and future oil and gas operations. Now, more than ever, safely managing the role of digitalization and demand for decarbonization, while efficiently balancing the books, will be critical for companies to survive and thrive. At the core of such an evolution are the people who drive the industry forward to deliver safer, cleaner, and more efficient energy to the world. The industry’s economics have shifted significantly, and we will be grappling with challenges for some time. New ways of working that leverage the right mix of talent and technologies will be more important than ever as we look toward the future. Traditionally, many companies have worked in silo on their own solutions, services, technologies, and capabilities. Such a mentality can have an impact on operations and employee morale. A new mindset is required for greater collaboration and transparency if the sector is to play a decisive role in enabling the energy transition. Fragmentation to Fusion A deeper and broader “ecosystem” approach can help establish strategic partnerships across the value chain. Together, this will not only develop new technologies and commercial models, but also unify the industry as it looks beyond the downturn to more stable and sustainable operations. For example, in June 2019, Baker Hughes and C3.ai began a joint venture (JV) agreement to deliver artificial intelligence (AI) solutions for the oil and gas industry. BakerHughesC3.ai brings together energy technology expertise with digital knowhow in solutions that optimize productivity and accelerate the energy transition. The alliance demonstrates a more collaborative approach to the digital transformation business. Five months later, Microsoft joined the alliance to support the suite of enterprise-scale AI applications on the Azure cloud computing platform. Shell is already utilizing the C3.ai platform to hasten digital transformation by focusing on AI and machine learning to improve overall operations starting with predictive maintenance. When the JV was announced, Shell Group CIO Jay Crotts said, “The fusion of existing oilfield technology and emerging digital technology will create new solutions that truly address oil-and-gas specific challenges with powerful advanced analytics technology.” While the number of pilot projects around digital and AI may be increasing, a major challenge has been difficulty getting traction to quickly transform them to scale. Interestingly, a survey found that more than 80% of organizations have implemented a digital initiative within the past 5 years but only 16% of those stated a successful improvement in performance (McKinsey &amp; Co. 2018). It is an area where we not only need to work together to try and achieve better results, but also take inspiration and best practices from other sectors which are already well ahead on their digital journey. This theme will be a focal point at ENGenious 2020, the sister event to SPE Offshore Europe.

Data Analytics and Security Policy

On January 16th, 2020, the Canadian Association for Security and Intelligence Studies (CASIS) Vancouver hosted its first roundtable event of the year, titled “Data Analytics and Security Policy: The New Paradigm?” This presentation featured Mr. Mark Masongsong, CEO of Urban Logiq, a Vancouver based data analytics company. Mr. Masongsong’s presentation focused on the development of data analytics technology, its utility in addressing recent security cases, and the projected future of the data analytics industry. The subsequent roundtable discussion centred around a case study on deep fakes and their prevalence throughout social media. Thereafter, audience members discussed the security implications of deep fake technology and ways to address the problem within online spaces.

Embrace artificial intelligence technologies for advanced analytics and management of intellectual properties

This short communication describes the background, objectives, and publications of World Patent Information's special issue on Artificial Intelligence for Intellectual Property (AI for IP). The report serves as the editorial for the WPI's special issue on AI for IP. We look forward to receiving future contributions in research articles, literature/book reviews, conference reports and short communications in the subject areas.

Navigating difficult decisions in medical care and research

The articles in this issue explore a number of difficult choices in medical care and research. They investigate ethical complexity in a range of decisions faced by policymakers and clinicians,...

Evaluating geovisualization for spatial learning analytics

ABSTRACT Contemporary systems for supporting digital learning are capable of collecting a wide range of data on learner behaviours. The emerging science and technology of learning analytics seeks to use this information to improve learning outcomes and support institutional assessment. In this work we explore the potential for the spatial dimension in learning analytics, and we evaluate a prototype geovisualization system designed to support what we call spatial learning analytics. A user evaluation with geographers and educators was conducted to characterize the usability and utility of our prototype spatial learning analytics system. By helping us understand what our prototype system does and does not do well, we are able to suggest a variety of new ways in which future spatial learning analytics systems can be developed.

On the use of semantic technologies for video analytics

The rapid proliferation of smart devices, surveillance cameras, infrastructures and buildings enhanced with the Internet of Things (IoT) technologies has led to a huge explosion of contents, especially in the video domain, determining an ever increasing interest towards the development of methods and tools for automatic analysis and interpretation of video sequences. Through the years, the availability of contextual knowledge has proven to improve video analysis performances in several ways, although the formal representation of semantic content in a shareable and fusion oriented manner is still an open problem, also considering the wide diffusion of Fog and Edge computing architectures for video analytics lately. In this context, an interesting answer has come from Semantic Web (SW) technologies, that opened a new perspective for the so-called Knowledge Based Computer Vision (KBCV), adding novel analytics opportunities, improving accuracy, and facilitating data exchange between video analysis systems in an open extensible manner. In this work, we propose a survey of the papers from the last eighteen years, back when first applications of semantic technologies to video analytics have appeared. The papers, analyzed under different perspectives to give a comprehensive overview of the technologies involved, reveal an interesting trend towards the adoption of SW technologies for video analytics scopes. As a result of our work, some insights about future challenges are also provided.

Evolution of morphology and structure of nano-(GaIn)2O3 with temperatures changes

(GaIn)2O3 nanostructures are synthesized successfully via chemical vapor deposition without any other reaction or catalyzer material. The morphology and crystal structure are characterized by an energy dispersive spectrometer, scanning electron microscopy, x-ray diffraction (XRD) analytic technologies, and photoluminescence (PL) spectra. With the increasing of temperature, the structure changed from monoclinic to cubic, while the morphology changed from column to spherical. The evolution of the structure and morphology should associate with the variation of the indium content of the (GaIn)2O3. The room temperature PL shows a wide emission band, which confirms the cubic and monoclinic structures reveled by XRD. The obtained (GaIn)2O3 with different morphologies and structures can offer different choices for applications.

Why human involvement is still required to move text analytics technologies leveraged with artificial intelligence from the trough of disillusionment to the plateau of productivity

The text analytics market, which has been predicted to be worth US$21.7bn by 2025, is falling headfirst into Gartner’s well-known trough of disillusionment by failing to deliver real organisational value and meet user expectations. The might of marketing has duped clients with hyped promises of illusive actionable insights delivered through fast, sexy interfaces, yet the industry is not delivering the value it promises. This paper explores the reasons behind the failure to deliver this expected value. It will define the terms ‘value’, ‘insight’ and ‘actionable insight’. It will use these definitions to identify where and why industry practice fails to meet these fundamental expectations. A short case study is included to provide an example of how emotion analytics of consumer-generated unstructured text data can help deliver meaningful and genuinely actionable insights.

Connecting Data to Value: An Operating Model for Healthcare Advanced Analytics.

Artificial intelligence offers the promise to revolutionize the way healthcare is delivered in the future. To capitalize on the value of advanced analytics and artificial intelligence, organizations must focus on building organizational capabilities. This report shares an operating model for insight and change in healthcare comprising six key components: analytics technology and operations, data governance, change and automation, advanced analytics and insights, analytics literacy and strategy and relationship management. The adoption of the proposed model will build core capabilities that will enable organizations to connect data to decision making and realize value from its investment in advanced analytics.

Marketing ecosystem: An outside-in view for sustainable advantage

In the modern business environment, consumers are increasingly influenced by megatrends involving marketplace, technology, socioeconomics, geopolitics, and natural environment. Simultaneously, the data and insights that can inform consumer attitudes and behaviors often reside outside of firms' direct control. Consciously incorporating these interdependent factors into firms' decision-making is essential for adaptability and sustainable profitability.Building on the “outside-in” perspective, we propose that firm strategies should be informed through the lens of the marketing ecosystem that considers the interrelated and dynamic megatrends. By leveraging advances in data and technology, firms can sense-make the marketplace by extracting insights from massive amounts of diverse consumer data with modern-day analytics. By mapping out the megatrends with marketing analytics, firms can 1) more accurately predict consumers' changing preferences and formulate appropriate strategies to engage with them; and 2) become more market-adaptable and competitive in the present and the future.To deliver sustainably compelling value to customers, firms should adopt an ecosystem mindset and cooperate with various stakeholders. A broad-thinking, agile, and humble firm culture can enable the development of more robust outside-in capabilities. We elaborate on the megatrends in the interconnected world of the marketing ecosystem, and propose emerging research directions in each area.

Analytic performance modeling and analysis of detailed neuron simulations

Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state-of-the-art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering, which is based on “white-box,” that is, first-principles performance models, to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive codesign efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually codesign of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.

Ambulatory digital phenotyping of blunted affect and alogia using objective facial and vocal analysis: Proof of concept

Negative symptoms reflect one of the most debilitating aspects of one of the most debilitating diseases known to humankind. As yet, our treatments for negative symptoms are palliative at best and our understanding of their causes is relatively superficial. To address this, we are developing objective ambulatory tools for digitally phenotyping their severity which can be used outside the confines of the traditional clinical and research settings. The present study evaluated the feasibility, reliability and validity of ambulatory vocal acoustic and facial emotion expression analysis. Videos were provided by 25 patients with schizophrenia or schizoaffective disorder and 27 nonpsychiatric controls using inexpensive, non-invasive ambulatory recording methods. Controls provided 411 video recordings, and patients provided 377 video recordings; an average of 15.22 and 14.50 per participant per group respectively. The vast majority (over 80%) of these videos were usable for analysis. An empirically-supported, limited-feature vocal (7 features) and facial (3 features) set was examined. Within participants, these features varied considerably over time, but showed moderate to good test-retest reliability in many cases once contextual factors (e.g., activity involved in at the time of testing) were accounted for. Vocal and facial features showed statistically significant convergence with a “gold standard” negative symptom measure. Ambulatory vocal/facial features were more strongly associated with engagement in social or work activities in patients than negative symptom ratings. These data support the use of ambulatory vocal/facial analytic technologies for digital phenotyping of these negative symptoms.

Automated Big Data Analytics for the Radiation Oncology Alternative Payment Model Proposal Using a Novel Health Care Software Technology.

The proposed Radiation Oncology Alternative Payment Model (RO-APM) aims to test prospective episode-based payments for radiotherapy episodes. Practices will need a tool that can calculate historical episode reimbursements to succeed in this new model. An automated software-based technology was created to calculate historical episode reimbursements within a large Network of community oncology practices. Claims data between January 1, 2017, and July 31, 2019, were cleaned, organized into episodes, and analyzed with a series of Python computer programs per proposed RO-APM methodology. Averaged Winsorized historical episode reimbursements were first calculated over the entire Network, then over 24 of the largest Practices, and then rerun after application of Clinical Rules to remove misattributed episodes. A total of 79,418 RO-APM-defined episodes were generated from 6,512,375 claims lines. A total of 7,086 episodes (8.9%) were removed because of no treatment delivery code within 28 days of treatment planning. The Network of practices had more bone metastases, and breast, cervical, and uterine cancers but less lung and prostate cancer than the RO-APM dataset. Combination-modality episodes were more costly and required more providers than single-modality episodes. Clinical Rules reattributed 2,495 episodes (3.4%) and increased episode reimbursement by +5.8% over all disease sites (+3.7% using volume weighting; P = .001). As payment models continue to shift from volume to value, practices will need an automated analytics technology to measure historical costs and prepare for operational and financial transformation. This automated approach can be adapted to future versions of the RO-APM. Our analysis suggests that future iterations of the RO-APM could incorporate Clinical Rules to remove misattributed palliative care episodes and could implement a separate payment for episodes with multiple radiation therapy modalities.

Is Big Data the New Stethoscope? Perils of Digital Phenotyping to Address Mental Illness

Advances in applications of artificial intelligence and the use of data analytics technology in biomedicine are creating optimism, as many believe these technologies will fill the need-availability gap by increasing resources for mental health care. One resource considered especially promising is smartphone psychotherapy chatbots, i.e., artificially intelligent bots that offer cognitive behavior therapy to their users with the aim of helping them improve their mental health. While a number of studies have highlighted the positive outcomes of using smartphone psychotherapy chatbots to handle various anxiety related problems no conclusive data illustrate their effectiveness or warrant their use in mental illness diagnosis and treatment settings. Yet smartphone psychotherapy is highly endorsed by experts in the field of mental health research. In this paper, I focus on the specific features of smartphone psychotherapy chatbots intended for the diagnosis and treatment of mental illness and criticize three popular promises; i.e., (i) they enable early diagnosis and intervention through digital phenotyping; (ii) they defy the stigma of mental illness diagnosis and treatment; (iii) they offer increased access to mental health treatment globally. Going against the popular enthusiasm, I argue smartphone psychotherapy chatbots have epistemic and ethical limitations in the diagnosis and treatment of illnesses. In light of these, I encourage researchers, clinicians, policy makers, patients, and caregivers to pause before jumping on the artificial intelligence bandwagon to seek solutions for mental illness on the grounds of these three promises.

Topological Data Analysis Based Feature Selection for Predicting Fatigue Strength of Steel Using Machine Learning

Routine Machine learning methodologies are based on Statistics and traditional data analysis methods, which may account for loss of some important information from the data during analytical process. So we need a new set of tools and algorithms that can effectively make most of such information. This paper investigates the implementation of new and growing data analytics technology known as topological data analysis (TDA). We use Mapper algorithm from TDA on data taken from public domain database of National Institute for Material Science (NIMS), for selecting key process parameters or features that impacts fatigue strength of steel. Mapper algorithm outputs a network graph called topological network that captures the important connectivity information and complex interactions between the clusters present in the data where traditional methods fail to capture such interactions. We analyse the shape of topological network obtained from mapper for selecting important features that impact the fatigue strength of steel. We then use XG-Boost prediction model with tuned hyper parameters using various tuning algorithms, to evaluate the impact of the selected features. This approach of data analysis and modelling helps to monitor and control the process in a more cost-effective manner. The experimental results have successfully proved the efficacy of TDA and machine learning (ML) and hence developed the predictive model for the same.