Building a tourism decision support system based on big data

Big data analytics in Industry 4.0 ecosystems

The confluence of science, technology, and medicine in our dynamic digital era has spawned new data applications to develop prescriptive analytics, to improve healthcare personalization and precision medicine, and to automate the reporting of health data for clinical decisions.1 Data science in health care has seen recent and rapid progress along 3 paths: (1) through big data via the aggregation of large and complex data sets including electronic medical records, social media, genomic databases, and digitized physiological data from wireless mobile health devices2; (2) through new open-access initiatives that seek to leverage the availability of clinical trial, research, and citizen science data sources for data sharing3; and (3) in analytic techniques particularly for big data, including machine learning and artificial intelligence that may enhance the analyses of both structured and unstructured data.4 As new data sets are created, analyzed, and become increasingly available, several key questions emerge including the following: What is the quality of unstructured data generation? Will the use of nonstandardized methods in data processing with traditional software and hardware lead to data fragmentation and analyses that are nonreproducible? Will healthcare systems incorporate and use big data especially from new publically and patient-generated sources? How will physicians and researchers learn from new open-sourced data and big-data analytics? And ultimately, How can they acquire the skills to create a knowledge translation in data sciences?5 Practicing in an era of continuous payment reform and decline in research funding, early career investigators are challenged to keep up with the accelerating pace of change in medicine, all while being expected to provide meaningful contributions through productive clinical, educational, and research experiences.6 In this perspective, we aim to highlight how data science can catalyze professional advancement and discuss the implications of big data, open access, …

Editorial

Advances in the field of technology enabled individuals and businesses to collect large amounts of data (structured and unstructured) from various sources like never before. Data from social media, user-generated, internet, health care, manufacturing, supply chain, financial institution, and sensors have grown exponentially. This paper’s objective is to review how big data drive and impact innovation, competitive advantage, productivity, and decision support. Methodology: A comprehensive literature review on big data and identifying the impact of big data analytics on innovation, competitive advantage, productivity, and decision support are studied. The reviewed literature created the foundation for studying, a model that was developed based on an extensive review of literature as well as case studies and future forecast by market leaders. Big data is the latest buzzword among businesses. A new model is suggested identifying big data and the correlation between innovation, competitive advantage, productivity, and decision support. Findings: A review of scholarly literature and existing case studies finds that there is a gap between existing frameworks and the integration of big data into various business and management functions and objectives. The findings are interesting that literature is rich with concepts and frameworks for achieving the end goal for business or management function along with framework but very little is available in the literature on the question of how to integrate big data analytics into those frameworks. This paper finds that a key question is missing i.e., what are the essential steps that businesses should perform to implement and integrate big data analytics into existing frameworks to fully exploit the big data potential. Research Limitations/Implications: The research was limited to a review of selective literature focused on in-depth understanding of big data. Additionally, it focuses on how big data leads to innovation, competitive advantage, productivity, and decision support. Although there are many other related fields of studies where big data impact can be studied but is not part of this study effort. Future studies can lead to more in-depth studies of other related areas of studies. Practical Implications: The review of the literature suggested that “Big Data” is playing an important role in innovations, creating competitive advantage, enhancing productivity, and assisting in data-driven decisions. Businesses are taking advantage of the customer insights that are innovating products and services which are very customer-centric, keeping the competition on the run, improving productivity at all levels, and making educated decisions every day. The future will be driven by smarter big data solutions and insights. Originality/Value: The study provides evidence that big data is the catalyst for innovation, creates competitive advantage, enhances productivity, and assists in decision making. The methodology is to review scholarly literature and case studies. It supports the need for developing new models, implementation frameworks for better insights, and patterns. The big data implementation methodologies, framework, and governance have been ignored in empirical research.

Smart healthcare, propelled by technological advancements, is witnessing a paradigm shift in the way healthcare services are delivered. This paper explores the transformative impact of Computational Intelligence (CI) and Big Data Analytics on smart healthcare systems. Computational Intelligence encompasses artificial neural networks, fuzzy logic, genetic algorithms, and expert systems, while Big Data Analytics involves the processing and analysis of large datasets to extract meaningful insights. This integration aims to enhance the efficiency, accuracy, and personalized nature of healthcare delivery. The application of CI in smart healthcare includes disease diagnosis through medical image analysis and predictive analytics for identifying highrisk patients. Moreover, CI facilitates personalized medicine by tailoring treatment plans based on individual characteristics. On the other hand, Big Data Analytics contributes to clinical decision support, population health management, and real-time monitoring of patients. The combination of CI and Big Data Analytics enables the development of predictive models, decision support systems, and efficient utilization of data from Internet of Things (IoT) devices and sensors. However, the adoption of these technologies in smart healthcare is not without challenges. Privacy and security concerns surrounding patient data, interoperability issues, and ethical considerations demand careful attention. Establishing standards for data interoperability and addressing ethical concerns related to consent and algorithmic biases are imperative for the successful implementation of CI and Big Data Analytics in healthcare.

Citation (2023), "Index", Visvizi, A., Troisi, O. and Grimaldi, M. (Ed.) Big Data and Decision-Making: Applications and Uses in the Public and Private Sector (Emerald Studies in Politics and Technology), Emerald Publishing Limited, Bingley, pp. 215-221. https://doi.org/10.1108/978-1-80382-551-920231014 Publisher: Emerald Publishing Limited Copyright © 2023 Anna Visvizi, Orlando Troisi and Mara Grimaldi INDEX Aadhaar data breach, 61 Accidental re-identification, 63 Acquired data, 164 Adaptive learning materials, 206 Aerospike, 64 Affect heuristic, 46 Agenda 2030, Sustainable Development Goals (SDGs), UN, 202 Agri-food sector (AFS), 122 bibliometric and descriptive results, 127–130 methodology, 123–126 thematic results, 130–136 Air France- KLM, 108 Albergo Diffuso (AD), 108 methodology, 109–110 results, 114–117 Amazon, 60 Application programming interfaces (APIs), 174 AppSheet technologies, 192 Artificial intelligence (AI), 2, 94, 117, 153 AI-based systems, 20 as operant resource for value co-creation in healthcare, 96–100 Asset reconfiguration, 31 “Augmented decision”-based model for value co-creation in healthcare, 100–102 Authority heuristic, 46, 49 Basic and transversal themes, 146 Bibliometric impact assessment, 128 Bibliometric methods, 144 Big data, 1–2, 5–6, 16, 28, 60, 78, 108, 122, 144, 162–165, 181 analysis algorithms, 108 analytics, 7 approaches, 181 benefits, 4 challenge of obtaining quality data, 17–18 challenge of utilization of big data in decision-making process, 18–20 diverse aspects, 3 society, and politics, 20–22 Biogas production, 166 Black box AI algorithms, 98 Blockchain, 2 Bottom-up approach, 185 Breaches, 61 Brunetta Reform, 33 Business, 1–3, 17–19 big data and, 147–151 data, 163 intelligence in information systems, 21, 163 models, 21 processes, 144–155 science mapping, 144–147 sector, 22 Business process management (BPM), 153 Cambridge Analytica scandal, 44, 48, 50 Cassandra, 64, 66, 69 Circular economy (CE), 3, 7, 123 CE-based economy system, 162 challenges for data-driven decision-making in SMEs, 163–166 lack of capabilities, 170–172 lack of resources, 169–170 methodology, 166–167 regulation, 172–173 utilization of data, 168–169 Citation analysis, 125 Clinical decision support systems (CDSS), 97 Closed-loop production systems, 162 Cloud computing, 20 Co-design of predictive decision model, 6 Co-evolutionary cycle, 188 in urban governance, 192–194 Co-evolutionary perspective, 8, 181 co-evolutionary cycle in urban governance, 192–194 on data-driven organization in smart city context, 186–188 data-driven organizations, 181–183 on data-driven urban organization during covid-19, 189–192 implications and future lines of research, 194195 theoretical background, 181–185 urban governance in digital era, 183–185 Coarse-grained access control, 64 Code Execution, 63 CodeIT, 208 Cognitive heuristics, 46 Cognitive technologies, 101 Common Vulnerability and Exposure (CVE), 60, 65 Communication theory, 79 Communities, 184 well-being, 21 Compromised databases, 69 Computer networks, 164 Computer science, 22 Connectivity, 208 Cooperation, 34 Coronavirus pandemic (see Covid-19—pandemic) CouchDB, 64, 66–67 Covid-19, 8, 32, 180, 194 co-evolutionary perspective on data-driven urban organization during, 189–192 pandemic, 22, 60 Crisis management, 60 Cross-Site Request Forgery (CSRF), 63–64 Customer-provided data, 164 Cyber-Physical Systems (CPS), 5, 150, 153 Cybersecurity techniques, 66 Data, 164, 205 analytics, 18 coding and emerging themes, 56–57 driven approach, 180 economy, 45 leakages, 61–62 management technologies, 180 ownership, 44 privacy, 44 processing, 99 protection, 45 science tools, 117 surveillance, 44 utilization, 169 Data analysis, 125–126, 182 process, 47 skills, 182 Data collection, 145, 147 and sampling process, 123–125 Data-driven approach, 109, 114, 117, 181, 191 decision-making based on, 192–194 opportunities and challenges from, 183–185 in tourism, 112–113 Data-driven circular economy, 6 Data-driven corporate culture, 96 Data-driven culture, 182 Data-driven decision-making (DDDM), 19, 96, 162 approach, 44 lack of capabilities, 165 lack of resources, 165 regulation, 165 in SMEs, 163 utilization of data, 164–165 Data-driven organizations, 181–183, 187 in smart city context, 186–188 Data-driven orientation, 19–20 key outcomes, 21 Data-driven segmentation studies, 113 Data-driven urban organization during covid-19, co-evolutionary perspective on, 189–192 Databases, 5, 60 Datum, 17 Decision standards, 99 Decision support systems (DSS), 184 Decision-making (see also Data-driven decision-making (DDDM)) approach, 180 challenge of utilization of big data in, 18–20 challenges and opportunities for decision-making based on data-driven approach, 192–194 in institutions and organizations, 21 process, 4, 180 shades in, 181 Delegation of decision-making authority, 79 Denial of Service, 63 Digital Agenda of Ukraine (2020), 203 Digital culture, 32 Digital era, urban governance in, 183–185 Digital governance (see also Urban governance), 28, 32 advent, 32–33 DCs as conceptual framework to study, 29–31 dynamic digital governance for improving performance management systems in inter-municipal context, 35–36 performance management systems within inter-municipal context, 33–35 theoretical background, 32–35 Digital technologies, 6, 122, 180, 186, 207 Digital transformations, 77 Digitalization, 78 policies, 32 Direct values, 60 Dirty data, 168 Double blind penetration or pentesting (see Double blind testing) Double blind testing, 65 Dynamic Application Security Testing (DAST), 64 Dynamic capabilities (DCs), 29 as conceptual framework to study, 29–31 Economic sustainability, 111–112 Educational institutions, 51 Effective leadership, 34 Elaboration Likelihood Model (ELM), 46 Elasticsearch, 66, 68, 72 Emerging or declining themes, 146 Entrepreneurial orientation (EO), 108, 115 Environmental sustainability, 111–112 European Union (EU), 33, 169, 173,175, 202 Explainability, 98 Facebook, 44, 50 File Inclusion, 63 Financial losses, 62 Firm-performance, 149 4th Industrial Revolution, 22 Freely available data, 164 Gain information, 63 Gain Privileges, 63 General Data Protection Regulation (GDPR), 4, 173 implementation, 49 studies, 44–45 Global Financial Crisis (2008), 33 Google, 60 Google Apps Script technologies, 192 Governments, 44 Grippe, 208 Hbase, 64 Healthcare service ecosystem, 94 Heuristics, 45–46 Highly developed and isolated themes, 146 Hospitality, 3 HTTP response splitting, 63 HTTP REST APIs, 63 Human resource orientation (HRO), 108, 116 Humane Entrepreneurship (HumEnt), 6, 108–111 in tourism, 112–113 Indirect values, 60 Industrial Internet of Things (IIoT), 5 Industry 4.0, 145, 150 Information and communication technology (ICT), 16, 28, 77, 94, 114, 202 Information systems, 169 of city planning cadaster, 207 Infrastructure integration, 185 Innovation, 79–80, 112, 150, 162, 164, 203 InOrdinatio index, 125, 128 Inspectability, 100 Intelligibility, 98 Intention–behavior gap, 50 Inter-municipal cooperative contexts, 37 International Communication Union (ITU), 203 Internet of Everything (IoE), 61 Internet of things (IoT), 2, 5, 150, 153, 183, 191 Internet of Things Search Engines (IoTSE), 61, 65 Interpretation, 145–146, 148 Intervention process, 188 Itomych Studio, 208 JavaScript injections, 63 Keyword frequency analysis, 125 Kharkiv City Council, 207 Kharkiv Smart City, 206–208 Knowledge creation, 80 Knowledge management, 78–80 Knowledge sharing, 79–80 findings, 82–84 literature review, 79–80 methodology, 81–82 SMEs and, 84 technology and, 83 Knowledge utilization, 80 Knowledge-based CDSS, 97 Kyiv Smart City, 206–208 Leadership, 112 Learning, 31 Legislation, 172–173 LG networking, 28 Linear economy models, 7 LinkedIn data breach, 61 Lufthansa, 108 Machine learning (ML), 2, 20, 66, 96, 191 Machines, 164 Malicious queries, 63 Managerial actions, 35 Material recycling, 166 Memcached, 66, 68 Misuse or mishandling of personal information, 44 Mobile applications, 164, 184 MongoDB, 62, 64, 66, 69 Motor themes, 146 Municipalities, 28 MySQL, 66, 70 National Vulnerability Database (NVD), 63 Neo4j, 64 Networks, 205 New knowledge, 80 New Public Governance (NPG), 28 New Public Management (NPM), 28 Non-knowledge-based CDSS, 97 NoSQL databases, 5, 60 data leakages, 61–62 methods and techniques for inspecting security of data storage facility, 64–66 open databases, 67 search engine for IoE as tool for detecting vulnerable open data sources, 66–67 security concerns, 62–64 Online marketing, 46 Open data, 164 Open databases, 67 Open Source Intelligence (OSINT), 65 Open Web Application Security Project (OWASP), 65 Optimism bias, 46 Organization for Economic Co-operation and Development (OECD), 203 OrientDB, 64 Passive assessment, 66 Penetration testing, 60 Performance analysis, 125 Performance management systems within inter-municipal context, 33–35 Performance measurement and management systems (PMMS), 4, 36 Personal data, 44 data coding and emerging themes, 56–57 interview questions, 55 methods, 46–47 privacy and security, 22–23 privacy paradox, 45–46 results, 47–51 Personal information, 43 Piggy-backed queries, 63 Policymaking, 3 in institutions and organizations, 21 Political institutions, 44 Politics, 20–22 Pollution, 202 PostgreSQL, 66, 69 Preferred Reporting Items for Systematic Reviews and Meta-Analysis method (PRISMA method), 5, 78, 80, 123 Privacy calculus theory, 45 Privacy paradox, 44–46, 49, 51 Privacy-breaching patterns, 63 Process management, 182 Property rights, 49–50 Public policy, 3 Quality data, challenge of obtaining, 17–18 Reactivity, 100 Real-time processing, 168 Redis, 64, 66, 69 “Reduce, reuse, and recycle” paradigm (3R paradigm), 7, 122 Reduce, reuse, recycle, redesign, 130–136 Reporting, 100 Resource coordination/integration, 31 Resource-based-view, 150 Risk disclosure, 100 Science Mapping method, 7, 144 SciMat analysis, 7, 144 Search Engines, 66 for IoE as tool for detecting vulnerable open data sources, 66–67 Secure by design, 67 Security breaches, 61 Security concerns, 62–64 Selection, 187 Self-Service Business Intelligence tools, 183 Semantic analysis, 117 Sensors, 20, 164 Service ecosystem, 95 Shodan-and Binary Edge-based vulnerable open data sources detection tool (ShoBEVODSDT), 61, 66–67 Small data, 16–17 Small and medium-sized enterprises (SMEs), 3, 81–82 context of research on, 86 and knowledge sharing, 84 and knowledge sharing and diffusion process, 87 Smart city, 202–206 algorithm model, 208–209 co-evolutionary perspective on data-driven organization in, 186–188 systems, 191 Smart city 3.0, technology test bed to, 202–205 Smart contracts, 2 Smart integrated systems, 21–22 Smart management, 204 Smart nudges, 101 Smart production systems (SPS), 153 Smart Sustainable City, 202–203 implementation smart cities projects in Ukraine, 206–208 modeling smart city algorithm, 208–209 smart cities worldwide, 205–206 smart city, 202–205 Smart technologies, 8 integrated infrastructure of, 182 Social inclusion, 21 Social learning, 185 Social media services, 164 Social networks, 184 Social sustainability, 111–112 Social systems, 180, 194 Socialization, externalization, combination, and internalization model (SECI model), 80 Society, 2, 20–22 Society 5.0, 127 SQL injection, 63 Starwood (Marriott) data breach, 61 Static code analysis, 64–65 Strategy, 2, 4 Supply chain management, 150 Sustainability, 111, 122, 162 Sustainability orientation (SO), 108, 115–116 Sustainable tourism (ST), 6, 108–109, 111–112 Swiss Air, 108 “Take–make–dispose” paradigm, 122 Tautologies, 63 Technological infrastructures, 32 Technological platforms, 185 Technology/technologies, 32 savvy, 47 technology-mediated services, 185 test bed to smart city 3. 0, 202–205 Telematic services, 32 Thematic networks, 145 Top management, 31 Transparency, 94 AI as operant resource for value co-creation in healthcare, 96–100 in AI-supported systems, 5 “augmented decision”-based model for value co-creation in healthcare, 100–102 DDDM, 96 problems in terms of, 98–100 service ecosystem and value co-creation, 95 Triple Criterion Model, 204 Twitter data breach, 61 Uber data breach, 61 Ukraine digital agenda, 203 implementation smart cities projects in, 206–208 Union of Municipalities (UMs), 4, 33, 35 Union queries, 63 United Nations (UN), 8 United Nations Environment Program, World Tourism Organization (UNEP-UNWTO), 108 Urban data-driven approach, 194 Urban governance (see also Digital governance) co-evolutionary cycle in, 192–194 in digital era, 183–185 models, 8 Urban Observatory program (UO program), 191 Urbanization, 202 User adoption, 46 Value co-creation, 95 Variations, 186 Variation–selection–retention circuit of new solutions, 31 Visualization, 145, 148 VulDB, 63 Vulnerability, 60 Vulnerable data, 64 in motion, 64 Waste management, 166, 171 Weak authentication, 63–64 Weak security, 61 Web of Science (WoS), 145 Websites, 4, 184 World Health Organization (WHO), 98 Yahoo data breach, 61 ZOOM data breach, 61 Book Chapters Prelims Chapter 1: Big data and Decision-making: How Big Data Is Relevant Across Fields and Domains Part 1: Conceptualizing Big Data, Its Value Added and Relevance in the Modern World Chapter 2: Mapping and Conceptualizing Big Data and Its Value Across Issues and Domains Chapter 3: Digital Governance for Addressing Performance Challenges Within Inter-municipalities Chapter 4: Misuse of Personal Data: Exploring the Privacy Paradox in the Age of Big Data Analytics Chapter 5: NoSQL Security: Can My Data-driven Decision-making Be Influenced from Outside? Part 2: Big Big Data and Its Application Across Policy Fields Chapter 6: Big Data, Knowledge Sharing, and the Innovation Process: A Systematic Literature Review Chapter 7: Transparency in AI Systems for Value Co-creation in Healthcare Chapter 8: Big Data and Its Impact on Tourism and Entrepreneurship Chapter 9: Big Data and Digital Technologies for Circular Economy in the Agri-food Sector Part 3: Business and Policy-making Process Empowered by Big Data Chapter 10: Business Processes Powered by Big Data: Current Issues and New Research Directions Chapter 11: Barriers and Practical Challenges for Data-driven Decision-making in Circular Economy SMEs Chapter 12: A Co-evolutionary Perspective on Data-driven Organization: Highlights from Smart Cities in the Covid-19 Era Chapter 13: What Does It Take to Build a Smart Sustainable City? – Modeling an Algorithm of Smart Cities Index

Big data refers to the data sets that have become so large and/or complex that traditional data technology is inadequate to process them effectively. Big data has large volume; changes quickly; has great variety; and has a good deal of uncertainty in its veracity. During the digitization of healthcare, hospitals become important sources of big data. Large volume of data is created from medical records, medical images, and distance medicine. For healthcare industry, big data analytics are evolving beyond traditional business intelligence and clinical decision support systems. It utilizes the vast amount of data gathered from healthcare networks for evidence-based medicine, propels precision medicine, health management, disease prevention, even the establishment and application of biobanks. This paper summarizes the current composition, characteristics, and trend of big data. It introduces the framework, tools, and methods for big data analytics. Key words: Big data analytics; Big data management; Precision medicine; Disease prevention

The narrative review aims to highlight several recently published 'big data' studies pertinent to the field of obstetric anesthesiology. Big data has been used to study rare outcomes, to identify trends within the healthcare system, to identify variations in practice patterns, and to highlight potential inequalities in obstetric anesthesia care. Big data studies have helped define the risk of rare complications of obstetric anesthesia, such as the risk of neuraxial hematoma in thrombocytopenic parturients. Also, large national databases have been used to better understand trends in anesthesia-related adverse events during cesarean delivery as well as outline potential racial/ethnic disparities in obstetric anesthesia care. Finally, real-time analysis of patient data across a number of disparate health information systems through the use of sophisticated clinical decision support and surveillance systems is one promising application of big data technology on the labor and delivery unit. 'Big data' research has important implications for obstetric anesthesia care and warrants continued study. Real-time electronic surveillance is a potentially useful application of big data technology on the labor and delivery unit.

Crisis management has become a critical aspect of modern business and public administration, especially in the face of global crises such as economic recessions, pandemics, and natural disasters. In this context, digital technologies are playing an increasingly important role, providing new tools and approaches for effective crisis management. The definition of crisis management includes a set of measures aimed at identifying, assessing and neutralizing crisis situations, as well as minimizing their negative consequences. It is a management discipline that covers strategic, operational and tactical actions that allow organizations to respond quickly to changes in the external and internal environment. The role of digital technologies in modern management cannot be overestimated. They provide tools for the rapid collection, analysis and processing of information, which is critical in crisis situations. For example, Big Data management systems allow analyzing huge amounts of information in real time, which contributes to a more accurate assessment of the situation and informed decision-making. Cloud technologies provide access to resources and data from anywhere in the world, which is especially important in a crisis when it is necessary to ensure the continuity of business processes and the work of teams on a remote basis. Big data analytics is one of the key components of digital technologies in crisis management. It allows collecting and analyzing data from various sources, including social media, news, internal company systems, etc., to identify potential crises at early stages and predict their development. This enables organizations to respond quickly to threats and minimize negative consequences. Cloud technologies provide flexibility and scalability of the IT infrastructure, allowing organizations to quickly adapt to changes in the external environment and ensure business continuity. They also help reduce IT infrastructure costs and increase resource efficiency. Artificial intelligence and machine learning are powerful tools for automating crisis management processes. They can be used to analyze large amounts of data, detect anomalies, predict the development of crisis situations, and support decision-making. Machine learning algorithms can analyze historical data to identify patterns that precede crises and recommend appropriate actions to prevent them. Digital platforms and tools for communication and collaboration, such as Microsoft Teams, Slack, Zoom, ensure continuous interaction between employees and teams, which is critical in crisis situations. They allow for quick information exchange, virtual meetings, and coordination of actions, which contributes to more effective crisis management. Practical cases of successful use of digital technologies in crisis management include the experience of large corporations, government organizations, and international organizations. For example, Microsoft uses Azure cloud technologies to ensure business continuity during crises such as the COVID-19 pandemic. Government agencies, such as the US Federal Emergency Management Agency (FEMA), use big data management systems and analytics to coordinate actions during natural disasters. Practical cases of successful use of digital technologies in crisis management include the experience of large corporations, government organizations, and international organizations. For example, Microsoft uses Azure cloud technologies to ensure business continuity during crises such as the COVID-19 pandemic. Government agencies, such as the US Federal Emergency Management Agency (FEMA), use big data management systems and analytics to coordinate actions during natural disasters. Recommendations for the implementation of digital technologies in crisis management include strategies and steps for successful implementation, the role of management, IT department and employees, as well as planning and preparation for implementation. It is important that the organization's management understands the importance of digital technologies in crisis management and provides the necessary resources for their implementation. The IT department should be prepared to quickly deploy new technologies and ensure their smooth operation. Employees should be trained to use new tools and technologies, which may require additional training and professional development. Planning and preparation for implementation include the development of a detailed action plan that covers all stages of digital technology implementation, from needs assessment and technology selection to deployment and integration into existing business processes. It is also important to ensure monitoring and evaluation of the effectiveness of the implemented technologies in order to be able to identify and eliminate possible problems in time. The conclusions summarize the importance of digital technologies for crisis management, the main results of the study, and prospects for further research in this area. In particular, it is noted that the use of digital technologies allows organizations to more effectively manage crisis situations, reduce risks and minimize negative consequences. Prospects for further research include the study of new technologies, such as blockchain and the Internet of Things (IoT), and their potential for crisis management.

Introduction:Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is “How can big data analytics support people-centred and integrated health services?”Methods:A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review.Results:Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%).Discussion:The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics.Conclusions:Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.

Decision support systems: A decade in perspective

Call for Papers: Special Issue on Profit-Driven Analytics.

In this paper we tried to perform a classification of modern decision support systems, applied in the oil industry, the methods and approaches implemented, the tasks and problems to be decided by the oil and gas production operators, applying these methods.

This article aims to discuss the construction and application of business management decision support system (DSS) based on big data. This article adopts a systematic approach, analyzes the application status and challenges of big data in business management decision-making, and puts forward the construction scheme of business management DSS based on big data. The model design of this article follows the design principles of systematicness, flexibility and user-friendliness, and defines the system goals of improving decision-making efficiency and promoting data-driven cultural transformation. By constructing a system architecture including data acquisition layer, storage and processing layer, analysis and mining layer and decision support layer, it provides a comprehensive solution for enterprise decision support. The results show that the business management DSS based on big data can significantly improve the efficiency and accuracy of enterprise decision-making, reduce the risk of decision-making, and promote the transformation of data-driven culture within enterprises. Through the implementation of the system, enterprises can make better use of big data resources and optimize management processes.

Ralph H. Sprague Jr. was a leader in the MIS field and helped develop the conceptual foundation for decision support systems (DSS). In this paper, I pay homage to Sprague and his DSS contributions. I take a personal perspective based on my years of working with Sprague. I explore the history of DSS and its evolution. I also present and discuss Sprague’s DSS development framework with its dialog, data, and models (DDM) paradigm and characteristics. At its core, the development framework remains valid in today’s world of business intelligence and big data analytics. I present and discuss a contemporary reference architecture for business intelligence and analytics (BI/A) in the context of Sprague’s DSS development framework. The practice of decision support continues to evolve and can be described by a maturity model with DSS, enterprise data warehousing, real-time data warehousing, big data analytics, and the emerging cognitive as successive generations. I use a DSS perspective to describe and provide examples of what the forthcoming cognitive generation will bring.