Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Identifying healthcare actors involved in the adoption of information systems

The adoption of information systems in healthcare is no less significant than in any other commercial or caring organisation. The literature on IS adoption in healthcare, makes it clear that the actors involved in the adoption process are almost universally seen as crucial, which matches our research results too. However, how such actors should be identified remains a topic for investigatory work since these are early days in achieving this. We derive and propose a structured method to model how actors might be identified: structured because such a rationale is explicable and such a method is more readily usable when transferred to others. Our structured method, named IGOHcaps, uses a static and then a dynamic step to pull out the individual, group, organisational and human determinants of the critical actors. In this process, the individual actors’ differing views emerge which could enable decision-making bodies to produce more robust proposals if they incorporated some of the appropriate views. We discuss the application of IGOHcaps through a hospital case study. While a single case study cannot be a proof, the engagement of the actors was encouraging

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Toward a process theory of entrepreneurship: revisiting opportunity identification and entrepreneurial actions

This dissertation studies the early development of new ventures and small business and the entrepreneurship process from initial ideas to viable ventures. I unpack the micro-foundations of entrepreneurial actions and new ventures’ investor communications through quality signals to finance their growth path. This dissertation includes two qualitative papers and one quantitative study. The qualitative papers employ an inductive multiple-case approach and include seven medical equipment manufacturers (new ventures) in a nascent market context (the mobile health industry) across six U.S. states and a secondary data analysis to understand the emergence of opportunities and the early development of new ventures. The quantitative research chapter includes 770 IPOs in the manufacturing industries in the U.S. and investigates the legitimation strategies of young ventures to gain resources from targeted resource-holders.Open Acces

Designing the Health-related Internet of Things: Ethical Principles and Guidelines

The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols