Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies

Using Deep Learning for Big Spatial Data Partitioning

This article explores the use of deep learning to choose an appropriate spatial partitioning technique for big data. The exponential increase in the volumes of spatial datasets resulted in the development of big spatial data frameworks. These systems need to partition the data across machines to be able to scale out the computation. Unfortunately, there is no current method to automatically choose an appropriate partitioning technique based on the input data distribution. This article addresses this problem by using deep learning to train a model that captures the relationship between the data distribution and the quality of the partitioning techniques.We propose a solution that runs in two phases, training and application. The offline training phase generates synthetic data based on diverse distributions, partitions them using six different partitioning techniques, and measures their quality using four quality metrics. At the same time, it summarizes the datasets using a histogram and well-designed skewness measures. The data summaries and the quality metrics are then use to train a deep learning model. The second phase uses this model to predict the best partitioning technique given a new dataset that needs to be partitioned.We run an extensive experimental evaluation on big spatial data, andwe experimentally showthe applicability of the proposed technique.We showthat the proposed model outperforms the baseline method in terms of accuracy for choosing the best partitioning technique by only analyzing the summary of the datasets

Optimizing the electronic health records through big data analytics: A knowledge-based view

Many hospitals are suffering from ineffective use of big data analytics with electronic health records (EHRs) to generate high quality insights for their clinical practices. Organizational learning has been a key role in improving the use of big data analytics with EHRs. Drawing on the knowledge-based view and big data lifecycle, we investigate how the three modes of knowledge can achieve meaningful use of big data analytics with EHRs. To test the associations in the proposed research model, we surveyed 580 nurses of a large hospital in China in 2019. Structural equation modelling was used to examine relationships between knowledge mode of EHRs and meaningful use of EHRs. The results reveal that know-what about EHRs utilization, know-how EHRs storage and utilization, and know-why storage and utilization can improve nurses' meaningful use of big data analytics with EHRs. This study contributes to the existing digital health and big data literature by exploring the proper adaptation of analytical tools to EHRs from the different knowledge mode in order to shape meaningful use of big data analytics with EHRs

Evaluation of Big Data Maturity Models – A Benchmarking Study to Support Big Data Maturity Assessment in Organizations

Big Data is defined as high volume, high velocity and high variety information assets, a result of the explosive growth of data facilitated by the digitization of our society. Data has always had strategic value, but with Big Data and the new data handling solutions even more value creation opportunities have emerged. Studies have shown that adopting Big Data initiatives in organizations enhance data management and analytical capabilities that ultimately improve competitiveness, productivity as well as financial and operational results. There are differences between organizations in terms of Big Data capabilities, performance and to what effect Big Data can be utilized. To create value from Big Data, organizations must first assess their current situation and find solutions to advance to a higher Big Data capability level, also known as Big Data maturity. Conceptual artefacts called Big Data maturity models have been developed to help in this endeavor. They allow organizations to have their Big Data methods and processes assessed according to best practices. However, it is a tough job for an organization to select the most useful and appropriate model, as there are many available and each one differ in terms of extensiveness, quality, ease of use, and content. The objective of this research was to evaluate and compare available Big Data maturity models in terms of good practices of maturity modeling and Big Data value creation, ultimately supporting the organizational maturity assessment process. This was done by conducting a benchmarking study that quantitatively evaluated maturity model attributes against specific evaluation criteria. As a result, eight Big Data maturity models were chosen, evaluated and analyzed. The theoretical foundations and concepts of the research were identified through systematical literature reviews. The benchmarking scores suggest that there is great variance between models when examining the good practices of maturity modeling. The degree of addressing Big Data value creation opportunities is more balanced. However, total scores clearly lean towards a specific group of models, identified as top-performers. These top-performers score relatively high in all examined criteria groups and represent currently the most useful Big Data maturity models for organizational Big Data maturity assessment. They demonstrate high quality of model structure, extensiveness and detail level. Authors of these models use a consistent methodology and good practices for design and development activities, and engage in high quality documentation practices. The Big Data maturity models are easy to use, and provide an intuitive tool for assessment as well as sufficient supporting materials to the end user. Lastly, they address all important Big Data capabilities that contribute to the creation of business value

Research on the path of Ideological and Political Education Enabled by artifi cial Intelligence Technology

With the comprehensive arrival of the “Internet +” era, artifi cial intelligence technology has been widely used in the fi eld of education, which has opened up a new channel for the reform of ideological and political education in colleges and universities, which is conducive to the construction of smart classroom, digital teaching resource bank, promote the communication between teachers and students, and then improve the quality of ideological and political education. Ideological and political teachers in colleges and universities should actively learn artifi cial intelligence technology, use big data to collect Internet ideological and political education resources, and promote the sharing of high-quality educational resources; Construct human-computer interactive teaching model, comprehensively monitor students’ learning status, and scientifi cally adjust teaching methods; Actively carry out online live teaching, and innovate ideological and political education methods; Artifi cial intelligence will lead the reform of teaching evaluation, and use cloud computing and big data for accurate evaluation, so as to comprehensively improve the quality of ideological and political education in colleges and universities

Big data analytics in auditing and the consequences for audit quality: A study using the technology acceptance model (TAM)

The study examines the impacts of using two dimensions of the technology acceptance model (TAM), perceived usefulness and perceived ease of use, on the adoption of big data analytics in auditing, and the subsequent impact on audit quality. Five hypotheses were developed. A questionnaire survey was undertaken with external affiliated audit companies and offices in Jordan. Eventually, 130 usable questionnaires were collected, representing a 72.22% response rate. Structural equation modelling (SEM) was employed for diagnosing the measurement model, and to test the hypotheses of the study. The study finds that perceived usefulness and perceived ease of use have a direct effect on audit quality, without mediating the actual use of data analytics. However, the use of big data analytics is shown to moderate the relationship between perceived usefulness and audit quality, but not between the perceived ease of use and audit quality. The study is one of the first to examine auditors’ acceptance of big data analytics in their work and the impact of this acceptance and actual use on audit quality. It contributes to the existing literature in auditing through its application of SEM to examine the impact of big data analytics usage on audit quality by using the TAM