Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. M-HEALTH <s> THE AMOUNT OF DATA BEING DIGITALLY COLLECTED AND stored is vast and expanding rapidly. As a result, the science of data management and analysis is also advancing to enable organizations to convert this vast resource into information and knowledge that helps them achieve their objectives. Computer scientists have invented the term big data to describe this evolving technology. Big data has been successfully used in astronomy (eg, the Sloan Digital Sky Survey of telescopic information), retail sales (eg, Walmart’s expansive number of transactions), search engines (eg, Google’s customization of individual searches based on previous web data), and politics (eg, a campaign’s focus of political advertisements on people most likely to support their candidate based on web searches). In this Viewpoint, we discuss the application of big data to health care, using an economic framework to highlight the opportunities it will offer and the roadblocks to implementation. We suggest that leveraging the collection of patient and practitioner data could be an important way to improve quality and efficiency of health care delivery. Widespread uptake of electronic health records (EHRs) has generated massive data sets. A survey by the American Hospital Association showed that adoption of EHRs has doubled from 2009 to 2011, partly a result of funding provided by the Health Information Technology for Economic and Clinical Health Act of 2009. Most EHRs now contain quantitative data (eg, laboratory values), qualitative data (eg, text-based documents and demographics), and transactional data (eg, a record of medication delivery). However, much of this rich data set is currently perceived as a byproduct of health care delivery, rather than a central asset to improve its efficiency. The transition of data from refuse to riches has been key in the big data revolution of other industries. Advances in analytic techniques in the computer sciences, especially in machine learning, have been a major catalyst for dealing with these large information sets. These analytic techniques are in contrast to traditional statistical methods (derived from the social and physical sciences), which are largely not useful for analysis of unstructured data such as text-based documents that do not fit into relational tables. One estimate suggests that 80% of business-related data exist in an unstructured format. The same could probably be said for health care data, a large proportion of which is text-based. In contrast to most consumer service industries, medicine adopted a practice of generating evidence from experimental (randomized trials) and quasi-experimental studies to inform patients and clinicians. The evidence-based movement is founded on the belief that scientific inquiry is superior to expert opinion and testimonials. In this way, medicine was ahead of many other industries in terms of recognizing the value of data and information guiding rational decision making. However, health care has lagged in uptake of newer techniques to leverage the rich information contained in EHRs. There are 4 ways big data may advance the economic mission of health care delivery by improving quality and efficiency. First, big data may greatly expand the capacity to generate new knowledge. The cost of answering many clinical questions prospectively, and even retrospectively, by collecting structured data is prohibitive. Analyzing the unstructured data contained within EHRs using computational techniques (eg, natural language processing to extract medical concepts from free-text documents) permits finer data acquisition in an automated fashion. For instance, automated identification within EHRs using natural language processing was superior in detecting postoperative complications compared with patient safety indicators based on discharge coding. Big data offers the potential to create an observational evidence base for clinical questions that would otherwise not be possible and may be especially helpful with issues of generalizability. The latter issue limits the application of conclusions derived from randomized trials performed on a narrow spectrum of participants to patients who exhibit very different characteristics. Second, big data may help with knowledge dissemination. Most physicians struggle to stay current with the latest evidence guiding clinical practice. The digitization of medical literature has greatly improved access; however, the sheer <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. M-HEALTH <s> Mobile health (mHealth) seeks to improve individuals' health and well-being by continuously monitoring their status, rapidly diagnosing medical conditions, recognizing behaviors, and delivering just-in-time interventions, all in the user's natural mobile environment. The Web extra at http://youtu.be/o2mieSywutY is an audio interview in which Santosh Kumar, Wendy Nilsen, and Mani Srivastava discuss the path toward realizing mobile health systems. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. M-HEALTH <s> Big data is often discussed in the context of improving medical care, but it also has a less appreciated but equally important role to play in preventing disease. Big data can facilitate action on the modifiable risk factors that contribute to a large fraction of the chronic disease burden, such as physical activity, diet, tobacco use, and exposure to pollution. It can do so by facilitating the discovery of risk factors for disease at population, subpopulation, and individual levels, and by improving the effectiveness of interventions to help people achieve healthier behaviors in healthier environments. In this article, we describe new sources of big data in population health, explore their applications, and present two case studies illustrating how big data can be leveraged for prevention. We also discuss the many implementation obstacles that must be overcome before this vision can become a reality. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. M-HEALTH <s> Cloud computing has opened many possibilities for organizations that did not exist before.Among the new sectors that are embracing and/or exploring the cloud are healthcare providers.The cloud has the potential to offer huge benefits for healthcare provision but this will also depend on overcoming many challenging factors. Cloud or utility computing is an emerging new computing paradigm designed to deliver numerous computing services through networked media such as the Web. This approach offers several advantages to potential users such as "metered" use (i.e., pay-as-you-go) which offers scalability, online delivery of software and virtual hardware services (e.g., collaboration programmes, virtual servers, virtual storage devices) which would enable organizations to obviate the need to own, maintain and update their software and hardware infrastructures. The flexibility of this emerging computing service has opened many possibilities for organizations that did not exist before. Among those organizations are those engaged in healthcare provision. The aim of this article is to shed some light on this development and explore the potential (and future) of cloud computing in contributing to the advancement of healthcare provision. A small case study will also be presented and discussed. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. M-HEALTH <s> The paper presents the methodologies on information fusion for social media.The methodologies, frameworks, and software used to work with big data are given.The state of the art in the data analytic techniques on social big data is provided.Social big data applications for various domains are described and analyzed. Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. M-HEALTH <s> A new era of interaction between people and technology is about to begin.Wearable technology has great potential of reshaping healthcare provision and medical education.The market for wearable technology is still at the "early adoption" phase of Rogers's diffusion of innovation trajectory. Developments in digital and communication technology are now reaching the realms that border on science fiction. Computing devices are no longer items that we use in our homes and places of work and carry in our bags and pockets. We can now wear those devices. This article focuses on this development. In doing so, it explores old and recent developments in wearable technology with a focus on their current and potential use in the field of healthcare and medical education. It also highlights the challenges that are likely to face this technology. Moreover, it attempts to provide some insights into the prospects of this technology from the theoretical perspective of the theory of disruptive innovations as proposed by Clayton Christensen and his colleagues from Harvard Business School. <s> BIB006 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. M-HEALTH <s> Wearable device and big data technology bring opportunities for healthcare sector.The effect of big data analytics on competition in wearable device firms is studied.Consumers preferences and firms benefits and costs for big data analytics are considered.Firms optimal strategies and performances under different scenarios are derived. As policy-makers and business practitioners across the globe expend extraordinary effort toward the field of e-health, the thriving development of healthcare-wearable technology is creating great opportunities and posing a remarkable future for healthcare services. This paper employs a game theory model to investigate the dynamics of wearable device market. We extend the two-dimensional product differentiation model by incorporating consumer diversity, consumer density, and firms big data analytics (BDA) investment strategy. Our model reveals that with differentiated consumer densities firms are more likely to engage in quality competition and the firm that invests in BDA can achieve higher profits. Furthermore, the overall quality of biomedical and healthcare services can be improved under various market conditions. Our findings provide practical guidance to wearable device manufacturers on optimizing competition strategies and offer insights to social planners on potential policy-making to promote better healthcare services. <s> BIB007
The extensive use of e-health records by hospitals across the globe has generated vast datasets BIB001 . Apart from hospitals, there are other sources of BD in healthcare such as clinical decision support systems, laboratories, pharmacies, and insurance companies. The gathering and analysis of healthcare related BD has been given various terminologies such as e-health, m-health (medical-health), digital health, health 2.0, and e-medicine BIB002 . One of the VOLUME 6, 2018 FIGURE 2. M-Health System at Chelsea and Westminster Hospital (London) (Adapted from BIB004 ). features of such data is crowdsourcing which is the basis of social media technologies. A number of websites has emerged recently trying to consolidate crowdsourced data such as PatientsLikeMe, Health Tracking Network BIB003 , and Global Public Health Intelligence Network BIB005 . Barrett et al. BIB003 state that crowdsourced data is especially beneficial for observing the spread of infectious diseases. Similarly, HealthMap -an online aggregator uses informal online data sources such as news, reports, and online discussion forums to observe and map diseases-related information. In the same fashion, individual users' (Facebook and Twitter) profiles generate diseases-related data using the keywords in their status updates/tweets which helps in mapping the location and intensity of a disease. In addition to above, a more accurate source of such data is the use of mhealth applications installed on smartphones and sensory wearable devices BIB007 . The growing trend of health related applications on smartphone has triggered the market for personal sensors with pre-loaded applications -which serve as passive and manual data generation source BIB003 . In addition, devices such as Google Glass, Apple iWatch, and Samsung Gear among others have built in hardware and software functionalities for monitoring users' heart, pulse, and respiratory rates  . Sultan BIB006 reported the life-saving incident of a patient using Google Glass at Boston's Beth Israel Deaconess Medical center where physicians learned a patient's complete medical history from his wearable device. This patient had earlier provided medical staff with an incomplete medical history. Google Glass helped the physicians in an emergency situation and saved time accessing patient's medical records with more accuracy than searching through physical records. Following this incident, the hospital extended the use of Google to the entire emergency department. Another potential use of wearable devices in healthcare is for data access. Sultan BIB004 mentioned the use of DACAR (data capture and auto identification reference) to gather remote data from patients that allows close family and friends to access data buckets of patients for surveillance and care purposes. Family members or friends can set threshold values (e.g., heart rate and blood pressure) in the system to trigger alarms related to a patient's health condition. Figure 2 shows the functioning of the m-health system 'DACAR' adapted by the Chelsea and Westminster hospital in London BIB001 . The system is basically a platform that enables remote delivery, storage and access to patient's electronic records using platform as a service (PaaS). DACAR is hosted on an IaaS (infrastructure as a service) cloud platform. The system enables close friends and family of a patient as well as the hospital/doctors to access his/her data buckets at FIGURE 3. Types of data intensive routing with route selection factors for each type  . any given time. The system is essentially connected to wearable technology on a patient and a data bucket allows creating, holding, updating, and deletion of patient's data. A cloud server stores all the updated data which is accessible by multiple stakeholders such as friends/family for monitoring the patient, the patient itself, and the doctor for accessing medical records. A consulting doctor or a staff can always access the real-time updates of any patient, as the data is continuously saved on the cloud. Data from the cloud can also be sent to the data center for further analysis. In addition, the DACAR system allows a patient to easily share information with the consultant if he/she is to seek any medical advice. Finally, for critical and elderly patients, their close family members are notified via messages if any health monitoring values breach the threshold levels (such as high blood pressure, pulse, and heartbeat).