With the advancement of technology in data science and network technology, the world has stepped into the Era of Big Data, and the medical field is rich in data suitable for analysis. Thus, in recent years, there has been much research in medical big data, mainly targeting data collection, data analysis, and visualization. However, very few works provide a full survey of the medical big data on chronic diseases and health monitoring. This review investigates recent research efforts and conducts a comprehensive overview of the work on medical big data, especially as related to chronic diseases and health monitoring. It focuses on the full cycles of the big data processing, which includes medical big data preprocessing, big data tools and algorithms, big data visualization, and security issues in big data. It also attempts to combine common big data technologies with special medical needs by analyzing in detail existing works of medical big data. To the best of our knowledge, this is the first survey that targets chronic diseases and health monitoring big data technologies.

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Chronic Diseases and Health Monitoring Big Data: A Survey

Hajj, the annual pilgrimage to Makkah, is one of the most massive gathering events in the world. It is a mandatory religious activity, once in a lifetime for every sane well-off Muslim. Every year around 3 million Muslims across the globe visit Makkah to perform this spatiotemporally constrained set of religious activities, like circumambulation around the Kaaba and the encampment at Mina, Arafat and Muzdalifah. The key ritual at Rami-al-Jamarat (the stoning-of-the-pillars) is a place seen particularly crowded. The diversified nature of the crowd in terms of race, age, language and culture brings several administrative challenges for local organizers whose responsibility is to ensure the smooth organization of the event. The advent of digital technologies has helped researchers to explore and propose modern methods for better crowd management and devise control strategies to organize such a mass gathering. In this paper, we present a taxonomy that summarizes our survey on the utilization of technology areas for providing needful services and improving crowd management during the Hajj season. These technologies are Wireless, Computer Vision, Spatial Computing, Data Analytics, Mobile Applications, Immersive Technologies, and Crowd Modelling and Simulation. We have also included the current research work and its implementation plan for Hajj 2020. One dimension of the taxonomy is to understand different possible technologies for Crowd management. Another dimension is to understand the impact of technologies to handle such a massive crowd. The discussion section covers a comparison of technologies with multiple aspects that may help researchers to contribute more towards dealing with future mass gathering events.

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Digital Revolution for Hajj Crowd Management: A Technology Survey

Context: Big data has become the new buzzword in the information and communication technology industry. Researchers and major corporations are looking into big data applications to extract the maximum value from the data available to them. However, developing and maintaining stable and scalable big data applications is still a distant milestone. Objective: To look at existing research on how software engineering concepts, namely the phases of the software development project life cycle (SDPLC), can help build better big data application projects. Method: A literature survey was performed. A manual search covered papers returned by search engines resulting in approximately 2,000 papers being searched and 170 papers selected for review. Results: The search results helped in identifying data rich application projects that have the potential to utilize big data successfully. The review helped in exploring SDPLC phases in the context of big data applications and performing a gap analysis of the phases that have yet to see detailed research efforts but deserve attention.

Software engineering for big data projects: Domains, methodologies and gaps

[Context] Data is being collected at an unprecedented scale. Data sets are becoming so large and complex that traditionally engineered systems may be inadequate to deal with them. While software engineering comprises a large set of approaches to support engineering robust software systems, there is no comprehensive overview of approaches that have been proposed and/or applied in the context of engineering big data systems. [Goal] This study aims at surveying existing research on big data software engineering to unveil and characterize the development approaches and major contributions. [Method] We conducted a systematic mapping study, identifying 52 related research papers, dated from 2011 to 2016. We classified and analyzed the identified approaches, their objectives, application domains, development lifecycle phase, and type of contribution. [Results] As a result, we outline the current state of the art and gaps on employing software engineering approaches to develop big data systems. For instance, we observed that the major challenges are in the area of software architecture and that more experimentation is needed to assess the classified approaches. [Conclusion] The results of this systematic mapping provide an overview on existing approaches to support building big data systems and helps to steer future research based on the identified gaps.

A Systematic Mapping of Software Engineering Approaches to Develop Big Data Systems

Healthcare system facilitates the treatment of patients with the support of wearable, smart, and handheld devices, as well as many other devices These devices are producing a huge bulk of data that need to be moulded for extracting meaningful insights from them for the useful use of researchers and practitioners Various approaches, methods, and tools are in use for doing so and to extract meaningful information in the field of healthcare This information is being used as evidence to further analyze the data for the early care of patient and to devise treatment Early care and treatment can facilitate healthcare and the treatment of the patient and can have immense potentiality of dropping the care cost and quality refining of care and can decrease waste and chances of error To facilitate healthcare in general and cardiology in specific, the proposed study presents an overview of the available literature associated with big data, its insights, and analytics The presented report will help practitioners and researchers to devise new solutions for early care in healthcare and in cardiology

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Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview

with the advancement of human life, there is big increase in chronic diseases especially in heart diseases. This increase is due to environmental pollution, lack of exercise, eating habits and closed environments. We propose a proactive health monitoring system for cardiac patients. This system consists of electronic bands wear by the patients to collect the real-time health status and an e-health system to process the collected data. The e-health system is a complete big data framework that will be develop to solve major healthcare problems. This system will provide a proactive measure to all the patients suffering from heart diseases like Ischemic heart disease or Hypertensive heart disease. The system will monitor the patient health continuously from electronic band and generates alerts to patients and doctor accordingly. This system will also provide a guidance to patients in case of abnormal health readings. The framework of e-health will follow the best practices of software engineering, healthcare and big data. This system will help patients to take proactive measure against any abnormal behavior in their health and also help doctors to monitor the patient health continuously. This development will enhance the quality of life of patients as well as increase the performance of doctors and healthcare providers.

A big data approach for proactive healthcare monitoring of chronic patients

To manage large crowded events is a very challenging task nowadays. People are looking for solutions in which they can easily monitor the crowd, trace individuals, study formation and deformation patterns of the crowd. Millions of Muslims from around the world gather and perform Hajj in Makkah, Saudi Arabia in the 12th month of every lunar year. To monitor the crowd during Hajj is a great challenge. In this paper, we are proposing an identifiable crowd monitoring framework named iCrowd. This framework collect, identify, infer, analyze, store and retrieve the real-time location information of each of the pre-registered participants (pilgrims), and provides a digital view of crowd in near real-time. iCrowd framework is designed on a three-layer approach, device layer, middleware layer, and the application layer. We demonstrate our proposed framework with simulated data in the context of the event of Hajj.

Ehsan Ullah Waraich

Papers

A big data approach for proactive healthcare monitoring of chronic patients

iCrowd — A framework for monitoring of identifiable crowd