Big Data Analytics in Healthcare——A Systematic Literature Review and Roadmap for Practical Implementation

The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.

A Cloud Service Architecture for Analyzing Big Monitoring Data

Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing（such as Hadoop） and stream processing（such as Spark） of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.

CALL FOR PAPERS Special Section of JCST on Computer Architecture and Systems for Big Data

Introduction Research on computer architecture and systems is typically driven by technology and applications. Big data has emerged as an important application domain which has shown its huge impact on scientific research, business, and society. Big data is known for its large volume, high velocity, and a variety of formats. The collection, storage, retrieval, processing, and visualization of big data issues many challenges to computer architecture and systems. This special section is an effort to encourage and promote research to address the big data challenges from the computer architecture and systems perspectives.

WAEAS:An Optimization Scheme of EAS Scheduler for Wearable Applications

The rapid development of wearable computing technologies has led to an increased involvement of wearable devices in the daily lives of people.The main power sources of wearable devices are batteries;so,researchers must ensure high performance while reducing power consumption and improving the battery life of wearable devices.The purpose of this study is to analyze the new features of an Energy-Aware Scheduler(EAS)in the Android 7.1.2 operating system and the scarcity of EAS schedulers in wearable application scenarios.Also,the paper proposed an optimization scheme of EAS scheduler for wearable applications(Wearable-Application-optimized Energy-Aware Scheduler(WAEAS)).This scheme improves the accuracy of task workload prediction,the energy efficiency of central processing unit core selection,and the load balancing.The experimental results presented in this paper have verified the effectiveness of a WAEAS scheduler.