Big Data Projects Research Articles

PIONEER big data platform for prostate cancer: lessons for advancing future real-world evidence research.

Prostate Cancer Diagnosis and Treatment Enhancement through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer. PIONEER brings together 34 private and public stakeholders from 9 countries in one multidisciplinary research consortium with the aim of positively transforming the field of prostate cancer clinical care by answering pressing questions related to prostate cancer screening, diagnosis and treatment. PIONEER has developed a unique state-of-the-art big data analytic platform by integrating existing data sources from patients withprostate cancer. PIONEER leveraged this platform to address prioritized research questions, filling knowledge gaps in the characterization, management and core outcomes of prostate cancer across the different disease stages. The network has benefited from sustained patient and stakeholder involvement and engagement, but many challenges remain when using real-world data for big data projects. To continue to advance prostate cancer care, data need to be available, suitable methodologies should be selected and mechanisms for knowledge sharing must be in place. Now acting as the prostate cancer arm of the European Association of Urology's new endeavour, UroEvidenceHub, PIONEER maintains its goal of maximizing the potential of big data to improve prostate cancer care.

Learning through Simulation: A Role-Based Learning Exercise to Equip Students in Big Data Requirement Elicitation Skills and Challenges

Bridging the gap between theoretical knowledge and practical application remains challenging in big data education. Our paper proposes a platform where students actively participate in a role-based learning (RBL) exercise designed to simulate real-world big data projects. This RBL exercise assigns each student participant to a business analyst role tasked with eliciting requirements or a customer role that provides those big data requirements. We conducted RBL sessions for postgraduate and undergraduate students learning big data analytics/data warehousing. Through role-playing in a collaborative environment, students face challenges such as unclear objectives, data privacy concerns, and evolving requirements. We evaluated the effectiveness of RBL simulations through brainstorming sessions, which verified that the students achieved the learning outcomes through RBL exercises. We also collected students’ feedback through a survey and found the RBL experience helped us understand the big data requirement elicitation skills, highlighting the significance of communication and collaboration skills. Further, we have evaluated students through a final exam question, and identified that students who participated in the RBL exercise outperformed in the big data requirement elicitation question. In summary, our research demonstrates that this RBL approach offers a valuable learning experience by enabling students to directly experience the complexities of big data requirement elicitation and identify the future requirements or challenges that encourage them to acquire the required skills.

The Arcus Experience: Bridging the Data Science Gap for Nurse Researchers.

For years, nurse researchers have been called upon to engage with "big data" in the electronic health record (EHR) by leading studies focusing on nurse-centric patient outcomes and providing clinical analysis of potential outcome indicators. However, the current gap in nurses' data science education and training poses a significant barrier. We aimed to evaluate the viability of conducting nurse-led, big-data research projects within a custom-designed computational laboratory and examine the support required by a team of researchers with little to no big-data experience. Four nurse-led research teams developed a research question reliant on existing EHR data. Each team was given its own virtual computational laboratory populated with raw data. A data science education team provided instruction in coding languages-primarily structured query language and R-and data science techniques to organize and analyze the data. Three research teams have completed studies, resulting in one manuscript currently undergoing peer review and two manuscripts in progress. The final team is performing data analysis. Five barriers and five facilitators to big-data projects were identified. As the data science learning curve is steep, organizations need to help bridge the gap between what is currently taught in doctoral nursing programs and what is required of clinical nurse researchers to successfully engage in big-data methods. In addition, clinical nurse researchers require protected research time and a data science infrastructure that supports novice efforts with education, mentorship, and computational laboratory resources.

Practices and barriers for big data projects

The adoption of big data analytics is increasing in every major industry, demanding investments in new projects, technologies, architectures, and processes to allow the integration of big data platforms with legacy systems; however, many organizations have failed to incorporate it effectively into their decision-making processes and project benefits have not been adequately captured. This study aims to further investigate how a big data analytics project can be implemented in insurance companies. A case study was conducted on one of the largest insurance companies in Brazil with interviews and document analysis. The study identified five main practices that were adopted to successfully implement a big data analytics project (implement automatic autoscaling alerts, use specialized big data tools, integrate the platform with legacy systems, comply with privacy legislation, and ensure the documentation of technical architecture using business process modeling), as well as four barriers that prevent its proper adoption (complexity of access to multicloud data sources, high processing requirements of unstructured data analysis, failure to attend to business necessities at the right time, and project delays brought by bureaucratic interdepartmental processes); some of these have not previously been identified. Finally, an action plan to address these issues is presented.

Innovative Pedagogy Through Big Data and ArcGIS: Perspectives on the Collaboration Among First-Generation Students, Nonprofits, and Open Data in Los Angeles

This article focuses on the innovative teaching practices using big data that arose from a 4-year collaboration between faculty, first-generation students at California State University, Los Angeles (CSULA), and local communities. The aim of the National Science Foundation–funded Big Data Project at CSULA was to engage students and faculty in using big data to tackle social issues such as racial violence, poverty, environmental inequalities, urban ecology, and structural barriers in Los Angeles, while preserving the cultural richness of the communities in East Los Angeles and surrounding areas. Once a year, faculty members applied to the program to incorporate data projects into their courses, received training, adapted their curricula, and worked with nonprofits. Our experience showed that the students who participated in this semester-long, hands-on learning experience exceeded the learning objectives of the program and specific courses. They were key contributors in generating new insights using big data, fostering meaningful dialogue, and creating research and scholarship opportunities that positively impacted their communities.

Unveiling the Secrets of Big Data Projects: Harnessing Machine Learning Algorithms and Maturity Domains to Predict Success

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Social Workers and The Elderly in The Era of Big Data: Innovative Strategies for Achieving Sustainable Development Goals

Background: Collaboration in the Big Data era that combines social workers and the elderly is an innovative strategy supporting achieving Indonesia's Sustainable Development Goals (SDGs). This research aims to explore and analyze the impact of combining social workers and the elderly in Big Data projects on achieving the Sustainable Development Goals (SDGs) in Indonesia.
 
 Method: Research methods involve interviews, surveys, and project data analysis involving this cross-generational collaboration. The author collected qualitative data through interviews with social workers and the elderly. Interviews were conducted with structured question guides covering using Big Data in social work, challenges faced, and benefits seen. Quantitative data was collected through surveys distributed to social workers with questions related to the research topic. The author also collected secondary data from previous research reports and related case studies.
 
 Results: The research results show that this collaboration contributes positively by collecting more accurate data, empowering the elderly through technology, and making a solid contribution to various SDGs.
 
 Conclusion: In facing the complexity of social challenges, combining the experiences of the Elderly with technology becomes an innovative strategy that supports inclusive and sustainable development.

Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)

Abstract. The PRIMAVERA project aimed to develop a new generation of advanced and well-evaluated high-resolution global climate models. As part of PRIMAVERA, seven different climate models were run in both standard and higher-resolution configurations, with common initial conditions and forcings to form a multi-model ensemble. The ensemble simulations were run on high-performance computers across Europe and generated approximately 1.6 PiB (pebibytes) of output. To allow the data from all models to be analysed at this scale, PRIMAVERA scientists were encouraged to bring their analysis to the data. All data were transferred to a central analysis facility (CAF), in this case the JASMIN super-data-cluster, where it was catalogued and details made available to users using the web interface of the PRIMAVERA Data Management Tool (DMT). Users from across the project were able to query the available data using the DMT and then access it at the CAF. Here we describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this multi-model dataset. We believe that PRIMAVERA's experience using a CAF demonstrates how similar, multi-institute, big-data projects can efficiently share, organise and analyse large volumes of data.

Impact of big data analytics in project success: Mediating role of intellectual capital and knowledge sharing

Purpose: This study empirically investigates the effect of big data analytics (BDA) on project success (PS). Additionally, in this study, the investigation includes an examination of how intellectual capital (IC) and (KS) act as mediators in the correlation between BDA and KS. Lastly, a connection between entrepreneurial leadership (EL) and BDA is also explored. Design/Methodology- Using a sample of 422 senior-level employees from the IT sector in Peru. The partial least squares structural equation modeling technique tested the hypothesized relationships. Findings- According to the findings, the relationship between BDA and PS is mediated by structural capital (SC) and relational capital (RC), and BDA demonstrates a positive and noteworthy correlation with PS. Furthermore, EL is positively associated with BDA in a significant manner. Practical implications- The finding of this study reinforce the corporate experience of BDA and suggest how senior levels of the IT sector can promote SC, RC, and EL. Originality/Value- This study is one of the first to consider big data analytics as an important antecedent of project success. With little or no research on the interrelationship of big data analytics, intellectual capital and knowledge sharing the study contributes by investigating the mediating role of intellectual capital and knowledge sharing on the relationship between big data analytics and project success.

Smart Cities with Big Data

With the rise of data-driven technology and the growing demand for sustainable urban development, the idea of smart cities has attracted a lot of attention recently. This study provides a comprehensive analysis of smart cities within the big data framework, emphasizing issues, problems, and reference models. The report starts off by outlining the essential elements of a smart city, focusing on how different data sources should be integrated and how data analytics is crucial for making well-informed decisions. It then explores the examination of current reference models, stressing their advantages and disadvantages in handling the intricacy of smart city infrastructures. The study also addresses the difficulties of using big data technology in urban settings, including concerns about data security, privacy, and governance. It also lists important factors to take into account when implementing big data projects in smart cities, stressing the need of stakeholder involvement, scalable infrastructure, and the creation of strong legal frameworks. Overall, this paper gives a thorough review of how big data will affect urban living in the future and offers insights into the key issues that must be resolved in order to realize the full potential of smart cities.

Challenges and prospects in big data analytics: a comprehensive review of developments, hurdles, and future research directions

Big data and business analytics are trends that are positively affecting the business world. This comprehensive review article explores the shifting paradigms and dynamic trends within Big Data Technology (BDT), predominantly for last 5 years, based on an extensive literature review and comparative analysis methodology. It elucidates the transformative influence of big data analytics (BDA) in various sectors, emphasizing the rapid ascendance of cloud computing, Artificial Intelligence (AI) integration, and development of sophisticated analytics tools. The review leverages a wealth of academic literature and market research to underscore the predicted expansion of the big data market. This projected growth indicates the widespread adoption of BDT across industries, with healthcare becoming a significant consumer, motivated by the demand for personalized medicine and improved patient care. The review then navigates emerging trends such as open data usage and ethical concerns surrounding big data, indicating the increasing necessity for stringent guidelines for data use and robust individual data control mechanisms. This is derived from a methodical analysis of recent scholarly articles and industry reports. The article also scrutinizes the evolving definition of "big data" through comparative study of the 3V model and the expanded 7V model in various literature sources, reflecting the evolving nature of data and the unique challenges introduced by modern big data analytics. The review also outlines the challenges for successful implementation of big data projects and highlights the current open research directions of big data analytics. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

Locating Multiple Equivalent Feature Subsets in Feature Selection for Imbalanced Classification

Feature selection can be used to solve imbalanced classification problems encountered in big data projects. There often exist multiple feature subsets achieving the same accuracy. These subsets tend to exhibit different acquisition difficulty and reliability, thus offering decision-makers with multiple choices if they can be well-identified. This work formulates feature selection as a Multimodal Multiobjective Problem (MMOP), where a point on Pareto front in objective space has multiple equivalent feature subsets in decision space. To seek more equivalent feature subsets, this work proposes a new multiobjective fireworks algorithm. It extends a latest single-objective fireworks algorithm to a multiobjective version such that it becomes suitable for solving MMOP. An adaptive strategy and special archive guidance are newly designed to improve its performance. A weighted extreme learning machine is chosen to classify datasets and return classification accuracy due to its fast learning speed. Experimental results show that the proposed algorithm outperforms its compared ones on 15 imbalanced classification datasets including 5 low-dimensional, 5 high-dimensional feature selection problems and 5 large-scale problems with larger imbalanced ratio, and its runtime is the least among them. Also, fault diagnosis in self-organizing cellular networks, as an important imbalance classification problem, is performed by the proposed algorithm and the results show that it can perform fault diagnosis well.

Establishment of Building Material Carbon Emission Coefficient Database and Application in Architectural Cases: Energy Conservation and Carbon Reduction Big Data Project

Establishment of Building Material Carbon Emission Coefficient Database and Application in Architectural Cases: Energy Conservation and Carbon Reduction Big Data Project

«Big Data» technology in the digital transformation of the economy

The authors aimed to investigate the essence and fundamentals of the functioning of "Big Data" in the digital economy and the features of successful use of "Big Data" technology by enterprises as a new economic resource. "Big Data" is presented by the authors as a new economic resource and digital technology, which allow by solving a priority task, i. e. the allocation at the conceptual level of research of the most significant factors (resources) that determine the organizational potential for the use of big data. The article emphasizes that in the ordinary sense, the term "big data" is simply associated with a large amount of information. However, the study of the reasons why companies decide to invest in big data projects shows that the main motivating factor for them is not a large amount of data as such, but their diversity, which allows obtaining quantitative and qualitative information about a complex (complex) system of material and non-material factors of a modern company. The authors state that the expected information revolution consists in the way we analyze business and justify management decisions, and it is due not just to an exponential, avalanche-like growth in the volume of big data, but to the emergence of a new quality of information as a result of such growth - information that analysts could not even dream of ten years ago. The problematic materials of the article are presented as follows: first, the analysis of publications necessary to determine the content of the concept of "big data" is given, and positive and normative models of big data are highlighted. Further, on the basis of a resource-oriented approach and based on the results of an empirical study of foreign leading companies in the implementation of big data projects, a conceptual model of intra-company factors determining the organizational potential of using big data is being developed. As a result, recommendations are formulated for domestic companies implementing or making decisions about projects using big data.

Identification of Aggregate Urban Mobility Patterns of Nonregular Travellers from Mobile Phone Data

Over the last two decades, mobile phone data have appeared to be a promising data source for mobility analysis. The structure, abundance, and accessibility of call detail records (CDRs) make them particularly suitable for such use. However, their exploitation is often limited to estimating origin–destination matrices of a restricted part of the population: regular travellers. Although these studies provide valuable information for policymakers, their scope remains limited to this subpopulation analysis. In the present work, we develop a collective mobility reconstruction method adapted to nonregular travellers. The method relies on the notion of the detour ratio, which makes it robust to the lack of mobile phone data as well as its application to large instances (large and dense telecommunication networks). It is used to conduct a longitudinal analysis of the macroscopic mobility patterns in Santiago de Cali, Colombia, thanks to call detail data shared by communication provider CLARO as part of a research project conducted by Citepa, Paris, the Green City Big Data Project.

Big Data Maturity Assessment Models: A Systematic Literature Review

Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable marketplace. The ability to be successful in Big Data projects is related to the organization’s maturity level. The maturity model is a tool that could be applied to assess the maturity level across specific key dimensions, where the maturity levels indicate an organization’s current capabilities and the desirable state. Big Data maturity models (BDMMs) are a new trend with limited publications published as white papers and web materials by practitioners. While most of the related literature might not have covered all of the existing BDMMs, this systematic literature review (SLR) aims to contribute to the body of knowledge and address the limitations in the existing literature about the existing BDMMs, assessment dimensions, and tools. The SLR strategy in this paper was conducted based on guidelines to perform SLR in software engineering by answering three research questions: (1) What are the existing maturity assessment models for Big Data? (2) What are the assessment dimensions for Big Data maturity models? and (3) What are the assessment tools for Big Data maturity models? This SLR covers the available BDMMs written in English and developed by academics and practitioners (2007–2022). By applying a descriptive qualitative content analysis method for the reviewed publications, this SLR identified 15 BDMMs (10 BDMMs by practitioners and 5 BDMMs by academics). Additionally, this paper presents the limitations of existing BDMMs. The findings of this paper could be used as a grounded reference for assessing the maturity of Big Data. Moreover, this paper will provide managers with critical insights to select the BDMM that fits within their organization to support their data-driven decisions. Future work will investigate the Big Data maturity assessment dimensions towards developing a new Big Data maturity model.

Legal narratives of smart cities: opacity, intelligibility, and compliancy in projects, norms, and futures

The study focuses on narratives and legal components that form the legal imaginary of smart city projects. This imaginary consists of archetypes of Law and Technology called to engage with prototypes of public policies and to (re)build legal stereotypes for smart city projects. After all, smart cities take core normative claims and goals into their code, in both the technological and legal senses of the term. The public storytelling of smart cities and the public policies of Big Data projects in the municipalities of Rio de Janeiro and Montréal are used as case studies, targeting the respective contexts, risks, and legislations. Three axes composed of six variables are applied for the legal analysis in the case studies: opacity (privacy and security), intelligibility (transparency and participation), and compliance (accountability and governance). Such components are central for the contextualization of technological issues of smart cities under a narrative that is more accessible by law, the identification of specific concerns that present social risks to the rule of law, and the justification of measures required by legislation to protect fundamental rights. Brazilian and Canadian legislative references are used as hypothetical exercises on the legal frameworks related to the in-house Big Data projects. Normative sources and elements for further research can be found in each analysis.

Use of the Hashtag #DataSavesLives on Twitter: Exploratory and Thematic Analysis.

"Data Saves Lives" is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area. This study explores how the hashtag #DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted #DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users' medical data. Public tweets published between April 19 and July 15, 2021, using the hashtag #DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory. Of 1026 unique tweets available for qualitative analysis, discussion around #DataSavesLives was largely positive (n=716, 69.8%) or neutral (n=276, 26.9%) toward the campaign with limited negative attitudes (n=34, 3.3%). Themes derived from the #DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag. The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to "Twitter bubbles": groups of similar-minded Twitter users.

Framework for Structuring Big Data Projects

This article aims to present a framework for structuring Big Data projects. The methodological procedures were divided into two phases: in-depth interview and focus group. The first phase embraces 12 in-depth individual interviews. In the second phase, three sessions of interviews with focus groups were applied. Both phases had as research subjects professionals with experience in Big Data projects. The analysis process was based on categorization through theory-driven and data-driven codes. Based on our analysis, it was possible to present a definition of a Big Data project and explore the beginning and its phases. We also identified 17 different critical factors in Big Data projects and proposed a discussion on technical and behavioral skills and decision-making issues in Big Data projects. We also developed and validated a framework to help structure a Big Data project. As the main theoretical contribution, our study aligns with a growing body of researchers who are proposing to debate Big Data projects, and with that increasing maturity on the topic. As a practical contribution, we present a framework that we hope will contribute to professionals working in the area, helping to conduct a Big Data project through a systemic view.

Common Language - One Law: Prospective Legal Unification Processes Based on a Practical Use of the e-Justice Portal

In search of interesting legal solutions connected with modern and effective functioning of the judiciary, the author draws attention to the use of the most technologically advanced solutions in the sphere of justice in countries of the European Union. The aim is, among others, to improve the functioning of the justice system. The paper briefly describes big data tools and projects based on them, such as e-CODEX or e-Justice Portal. In the further part of the article, reference is made to the trend of creating tools of linguistic and semantic nature, used not only by lawyers, but also by citizens as well. An example thereof is the IMOLA project implemented by the European Land Registry Association in Brussels covering national land registers, with the ambitious intention of launching an operational system that will eventually integrate national databases. The study concludes with Ludwik Zamenhof’s dream: ‘Let Us Work and Have Hope!’ (Ni laboru kaj esperu!), referring to the role of the universal language he has created as a bridge to world peace and well-being of all people. The idea is that a common language brings people together. This role could be taken over by English and the technological advances of the digital world, which bring European countries closer to his yearning for peace, albeit in a slightly different way than he imagined. Improving the quality of communication is the key to success, which builds trust in the state.