Big Data Analytics: Applications, Prospects and Challenges

A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis

The development of platforms for distributed analytics has been driven by a growing need to comply with various governance-related or legal constraints. Among these platforms, the so-called Personal Health Train (PHT) is one representative that has emerged over the recent years. However, in projects that require data from sites featuring different PHT infrastructures, institutions are facing challenges emerging from the combination of multiple PHT ecosystems, including data governance, regulatory compliance, or the modification of existing workflows. In these scenarios, the interoperability of the platforms is preferable. In this work, we introduce a conceptual framework for the technical interoperability of the PHT covering five essential requirements: Data integration, unified station identifiers, mutual metadata, aligned security protocols, and business logic. We evaluated our concept in a feasibility study that involves two distinct PHT infrastructures: PHT-meDIC and PADME. We analyzed data on leukodystrophy from patients in the University Hospitals of Tübingen and Leipzig, and patients with differential diagnoses at the University Hospital Aachen. The results of our study demonstrate the technical interoperability between these two PHT infrastructures, allowing researchers to perform analyses across the participating institutions. Our method is more space-efficient compared to the multi-homing strategy, and it shows only a minimal time overhead.

How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?

Despite researchers having averred that big data analytics (BDA) transforms firms' ways of doing business, knowledge about operationalizing these technologies in organizations to achieve strategic objectives is lacking. Moreover, organizations' great appetite for big data and limited empirical proof of whether BDA impacts organizations' transformational capacity poses a need for further empirical investigation. Therefore, this study explores the association between big data analytics management capabilities (BDAMC) and innovation performance via dynamic capabilities (DC), by applying the PLS-SEM technique to analyzing the feedback of 149 firms. Consequently, we ground our arguments on dynamic capability and social capital theory rather than a resource-based view that does not provide suitable explanations for the deployment of resources to adapt to change. Accordingly, we advance this research stream by finding that BDAMC significantly enhances innovation performance through DC. We also extend the literature by disclosing how BDAMC strengthens DC via strategic alignment and social capital.

Learning through online participation: A longitudinal analysis of participatory budgeting using Big Data indicators

Local authorities increasingly employ digital platforms to facilitate public engagement in participatory budgeting processes. This creates opportunities for and challenges in synthesizing citizens’ voices online in an iterated cycle, requiring a systematic tool to monitor democratic quality and produce formative feedback. In this paper, we demonstrate how cases of online deliberation can be compared longitudinally by using six Big Data-based, automated indicators of deliberative quality. Longitudinal comparison is a way of setting a reference point that helps practitioners, designers, and researchers of participatory processes to interpret analytics and evaluative findings in a meaningful way. By comparing the two rounds of OmaStadi, we found that the levels of participation remain low but that the continuity and responsiveness of online deliberation developed positively.

Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it.

Achieving Strategic Alignment of Big Data Projects in Saudi Firms

Big data projects can fail due to the lack of alignment between the big data project strategy and the overall business strategy. This research considers organizational culture as an enabler of a better alignment between the two. To test the research hypothesis, a questionnaire was collected from several dozen IT decision-makers in Saudi organizations who have implemented big data projects. Statistical analysis using PLS indicates that the alignment of big data projects and overall business strategy is highly influenced by the five dimensions of organizational culture identified by Smit et al. (2008), namely strategy, leadership, adaptability, coordination, and team relationships

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.