Mobile Big Data Analytics in Healthcare

Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move

Authorization policies: Using Decision Support System for context-aware protection of user's private data

International audienceNowadays privacy in ambient system is a real issue. Users will have to control their data more and more in the future. Current security systems don't support a strong constraint: policy writers are non-technical users and not security experts. We propose in this paper to use Decision Support techniques and more specifically Multi-Criteria Decision Analysis in the process of authorization policy writing. This research area provides techniques to inform and assist non-technical users to write their own authorization policies following the paradigm of Attribute-Based Access Control

Moving from a "human-as-problem" to a "human-as-solution" cybersecurity mindset

Cybersecurity has gained prominence, with a number of widely publicised security incidents, hacking attacks and data breaches reaching the news over the last few years. The escalation in the numbers of cyber incidents shows no sign of abating, and it seems appropriate to take a look at the way cybersecurity is conceptualised and to consider whether there is a need for a mindset change.To consider this question, we applied a "problematization" approach to assess current conceptualisations of the cybersecurity problem by government, industry and hackers. Our analysis revealed that individual human actors, in a variety of roles, are generally considered to be "a problem". We also discovered that deployed solutions primarily focus on preventing adverse events by building resistance: i.e. implementing new security layers and policies that control humans and constrain their problematic behaviours. In essence, this treats all humans in the system as if they might well be malicious actors, and the solutions are designed to prevent their ill-advised behaviours. Given the continuing incidences of data breaches and successful hacks, it seems wise to rethink the status quo approach, which we refer to as "Cybersecurity, Currently". In particular, we suggest that there is a need to reconsider the core assumptions and characterisations of the well-intentioned human's role in the cybersecurity socio-technical system. Treating everyone as a problem does not seem to work, given the current cyber security landscape.Benefiting from research in other fields, we propose a new mindset i.e. "Cybersecurity, Differently". This approach rests on recognition of the fact that the problem is actually the high complexity, interconnectedness and emergent qualities of socio-technical systems. The "differently" mindset acknowledges the well-intentioned human's ability to be an important contributor to organisational cybersecurity, as well as their potential to be "part of the solution" rather than "the problem". In essence, this new approach initially treats all humans in the system as if they are well-intentioned. The focus is on enhancing factors that contribute to positive outcomes and resilience. We conclude by proposing a set of key principles and, with the help of a prototypical fictional organisation, consider how this mindset could enhance and improve cybersecurity across the socio-technical system

PRIVACY ASSURANCE AND NETWORK EFFECTS IN THE ADOPTION OF LOCATION-BASED SERVICES: AN IPHONE EXPERIMENT

The use of geospatially aware mobile devices and applications is increasing, along with the potential for the unethical use of personal location information. For example, iPhone apps often ask users if they can collect location data in order to make the program more useful. The purpose of this research is to empirically examine the significance of this new and increasingly relevant privacy dimension. Through a simulation experiment, we examine how the assurance of location information privacy (as well as mobile app quality and network size) influences users\u27 perceptions of location privacy risk and the utility associated with the app which, in turn, affects their adoption intentions and willingness-to-pay for the app. The results indicate that location privacy assurance is of great concern and that assurance is particularly important when the app’s network size is low or if its quality cannot be verified

Three Essays On Interfirm Interdependence And Firm Performance

This dissertation explicitly examines the structure of interdependencies that firms are subjected to within a platform-based ecosystem and its implications for firm performance. Two theoretical themes emerge from this dissertation: (1) a firm’s interdependence with other actors in the ecosystem matters both for its performance and the sustainability of its superior performance; and (2) a manager’s understanding of these interdependencies can have significant implications on firm performance and the choice of governance structures. The first essay explores how a firm’s innovation differs with respect to its interdependence with various elements of the ecosystem and examines its implications on the innovation’s commercialization success. The core set of data is based on all the apps that were launched in the Apple iPhone ecosystem from 2008 to 2013. The results suggest that firms can enhance the value of their innovation by drawing on the broader set of complementary technologies that are available in the ecosystem. But, these complementarities also subject firms to an array of bottlenecks limiting their innovation’s value creation. The second essay examines how ecosystem-level interdependencies affect the extent to which firms can sustain their value creation in a platform-based ecosystem. The analysis is based on a panel dataset of top-performing app developers in the iOS and Android ecosystems from January 2012 to January 2014. The results suggest that a firm’s ability to sustain its superior performance is facilitated by the technological interdependence faced by its innovation within an ecosystem and the experience gained within the ecosystem, but hampered by technological transitions initiated by the central firm. The third essay addresses the performance consequences of misrepresentation of interdependence structures in the alliance context using an agent-based simulation. The results suggest that the misrepresentation of interdependence structures plays an important role in determining performance consequences of various governance modes to manage the alliance relationship. Specifically, overrepresentation of interdependence structures requires fully integrated or more hierarchical governance modes, whereas underrepresentation of interdependence structures requires more decentralized governance modes. Collectively, these essays contribute to the literature on ecosystems and alliances, shedding new light on the role of structure of interdependence ins shaping firm’s performance

The dynamics of digital platform innovation: unfolding the paradox of control and generativity in Apple's iOS

Mobile digital platforms provide an architectural basis for third party innovation of platform complements. Platform owners have property rights, enabling them to establish a boundary of permissible innovation demarcating the permitted from the prohibited. This allows for the curation of complements, which provides a means of controlling for value creation. Consequently, platform innovationthe innovation of platform complements is occasionally refused by platform owners. When this occurs tensions may arise between the two parties over where the boundary of permissible innovation should lie. Tussles may break out, embodied in complex interactions, as each party attempts to get its way. Eventually an outcome is achieved, and a platform innovation is either allowed or prohibited. A body of platform innovation literature is emerging from fields including information systems. Whilst this literature considers many aspects of platform innovation, the dynamics concerning the control of the innovation of platform innovation complements is overlooked. This research attempts to address that gap. Its relevance to information systems concerns the digitalisation of platforms as systemsdigital infrastructures, which affects their capacity for innovation and regulation. This research uses the method of narrative networks to analyse 45 examples of contested platform innovation. This approach, informed by empirical data sourced from over 4500 blog entries, identifies patterned sequences of actions across the examples. These sequences describe how tension builds, how control is asserted, and how control is then resisted. A theory of formal managerial control is used to explain how mechanisms of control are applied by platform owners as well as how developers respond to control. The principle contribution of this research is to theory. It develops and presents a theory to describe and explain the dynamics of contested innovation of complements on curated digital platforms. In doing so, iIt challenges the understanding that the platform owner alone controls platform design rules and concerning which platform complements are allowed, and which are notthe boundary of permissible innovation. Furthermore, tThe study indicates opens up the possibility that the forces of digitalisation provide third parties with the power to affect influence platform architecture, but at the cost of additional means of being controlled

Avoimen lähdekoodin ohjelmistojen soveltaminen liiketoiminnassa

My experience working with open source software exposed a lack of comprehensive, easily graspable, introductory articles suitable for non-technical readers. The objective of this study is to provide the reader with a comprehensive understanding on how to utilize open source software in a commercial environment. Through a literature review this study identifies common patterns among open source projects and related companies. Patterns have been organized into four identified domains: legal, social, technological and business. Real life examples of the patterns are provided to assist understanding. In conclusion, this thesis argues that open source can be utilized to build successful commercial operations. Open source can be used to improve software development, software quality, to gain feedback, to expand the user base, to influence the direction of technological progress and to benefit from innovations, which would otherwise be hard to monetize. However, open source also introduces serious drawbacks and risks regarding commercial operations, including low appropriability, market destruction and loss of control of the software development.Pitkäaikainen työskentelyni avoimen lähdekoodin parissa on osoittanut puutteen kattavalle, helposti sisäistettävälle johdantoartikkeleille, jotka soveltuisivat myös maallikoille. Tämän tutkimuksen tavoitteena on tarjota lukijalle kattava ymmärrys avoimen lähdekoodin ohjelmistojen käyttämisestä kaupallisessa liiketoiminnassa. Tutkimus esittelee kirjallisuudesta yleisiä säännönmukaisuuksia avoimen lähdekoodin projektien ja yritysten toiminnasta. Nämä säännönmukaisuudet on jaoteltu neljään tunnistettuun osa-alueeseen: lakitekniikka, sosiaalinen ympäristö, teknologia ja liiketoiminta. Ymmärtämisen helpottamiseksi tutkimukseen on kerätty esimerkkejä kaupallisista yrityksistä. Johtopäätöksenä tämä tutkimus osoittaa, että avointa lähdekoodia voidaan käyttää menestyksekkään liiketoiminnan kehittämiseen. Avointa lähdekoodia voidaan hyödyntää ohjelmiston kehittämiseen, laadun parantamiseen, käyttäjäpalautteen keräämiseen, käyttäjämäärien kasvattamiseen, teknologisen kehityksen ohjaamiseen, tai sellaisien innovaatioiden hyödyntämiseen, joiden tuotteistus olisi muuten hankalaa tai mahdotonta. Avoimella lähdekoodilla on kuitenkin myös kauaskantoisia haittapuolia, kuten alentunut liikevaihto, lisensointimahdollisuuksien häviäminen, sekä ohjausvallan menetys ohjelmiston kehityksessä

Spam elimination and bias correction : ensuring label quality in crowdsourced tasks.

Crowdsourcing is proposed as a powerful mechanism for accomplishing large scale tasks via anonymous workers online. It has been demonstrated as an effective and important approach for collecting labeled data in application domains which require human intelligence, such as image labeling, video annotation, natural language processing, etc. Despite the promises, one big challenge still exists in crowdsourcing systems: the difficulty of controlling the quality of crowds. The workers usually have diverse education levels, personal preferences, and motivations, leading to unknown work performance while completing a crowdsourced task. Among them, some are reliable, and some might provide noisy feedback. It is intrinsic to apply worker filtering approach to crowdsourcing applications, which recognizes and tackles noisy workers, in order to obtain high-quality labels. The presented work in this dissertation provides discussions in this area of research, and proposes efficient probabilistic based worker filtering models to distinguish varied types of poor quality workers. Most of the existing work in literature in the field of worker filtering either only concentrates on binary labeling tasks, or fails to separate the low quality workers whose label errors can be corrected from the other spam workers (with label errors which cannot be corrected). As such, we first propose a Spam Removing and De-biasing Framework (SRDF), to deal with the worker filtering procedure in labeling tasks with numerical label scales. The developed framework can detect spam workers and biased workers separately. The biased workers are defined as those who show tendencies of providing higher (or lower) labels than truths, and their errors are able to be corrected. To tackle the biasing problem, an iterative bias detection approach is introduced to recognize the biased workers. The spam filtering algorithm proposes to eliminate three types of spam workers, including random spammers who provide random labels, uniform spammers who give same labels for most of the items, and sloppy workers who offer low accuracy labels. Integrating the spam filtering and bias detection approaches into aggregating algorithms, which infer truths from labels obtained from crowds, can lead to high quality consensus results. The common characteristic of random spammers and uniform spammers is that they provide useless feedback without making efforts for a labeling task. Thus, it is not necessary to distinguish them separately. In addition, the removal of sloppy workers has great impact on the detection of biased workers, with the SRDF framework. To combat these problems, a different way of worker classification is presented in this dissertation. In particular, the biased workers are classified as a subcategory of sloppy workers. Finally, an ITerative Self Correcting - Truth Discovery (ITSC-TD) framework is then proposed, which can reliably recognize biased workers in ordinal labeling tasks, based on a probabilistic based bias detection model. ITSC-TD estimates true labels through applying an optimization based truth discovery method, which minimizes overall label errors by assigning different weights to workers. The typical tasks posted on popular crowdsourcing platforms, such as MTurk, are simple tasks, which are low in complexity, independent, and require little time to complete. Complex tasks, however, in many cases require the crowd workers to possess specialized skills in task domains. As a result, this type of task is more inclined to have the problem of poor quality of feedback from crowds, compared to simple tasks. As such, we propose a multiple views approach, for the purpose of obtaining high quality consensus labels in complex labeling tasks. In this approach, each view is defined as a labeling critique or rubric, which aims to guide the workers to become aware of the desirable work characteristics or goals. Combining the view labels results in the overall estimated labels for each item. The multiple views approach is developed under the hypothesis that workers\u27 performance might differ from one view to another. Varied weights are then assigned to different views for each worker. Additionally, the ITSC-TD framework is integrated into the multiple views model to achieve high quality estimated truths for each view. Next, we propose a Semi-supervised Worker Filtering (SWF) model to eliminate spam workers, who assign random labels for each item. The SWF approach conducts worker filtering with a limited set of gold truths available as priori. Each worker is associated with a spammer score, which is estimated via the developed semi-supervised model, and low quality workers are efficiently detected by comparing the spammer score with a predefined threshold value. The efficiency of all the developed frameworks and models are demonstrated on simulated and real-world data sets. By comparing the proposed frameworks to a set of state-of-art methodologies, such as expectation maximization based aggregating algorithm, GLAD and optimization based truth discovery approach, in the domain of crowdsourcing, up to 28.0% improvement can be obtained for the accuracy of true label estimation