Modelling mobile health systems: an application of augmented MDA for the extended healthcare enterprise

Mobile health systems can extend the enterprise computing system of the healthcare provider by bringing services to the patient any time and anywhere. We propose a model-driven design and development methodology for the development of the m-health components in such extended enterprise computing systems. The methodology applies a model-driven design and development approach augmented with formal validation and verification to address quality and correctness and to support model transformation. Recent work on modelling applications from the healthcare domain is reported. One objective of this work is to explore and elaborate the proposed methodology. At the University of Twente we are developing m-health systems based on Body Area Networks (BANs). One specialization of the generic BAN is the health BAN, which incorporates a set of devices and associated software components to provide some set of health-related services. A patient will have a personalized instance of the health BAN customized to their current set of needs. A health professional interacts with their\ud patients¿ BANs via a BAN Professional System. The set of deployed BANs are supported by a server. We refer to this distributed system as the BAN System. The BAN system extends the enterprise computing system of the healthcare provider. Development of such systems requires a sound software engineering approach and this is what we explore with the new methodology. The methodology is illustrated with reference to recent modelling activities targeted at real implementations. In the context of the Awareness project BAN implementations will be trialled in a number of clinical settings including epilepsy management and management of chronic pain

Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

Resource Management in Large-scale Systems

The focus of this thesis is resource management in large-scale systems. Our primary concerns are energy management and practical principles for self-organization and self-management. The main contributions of our work are: 1. Models. We proposed several models for different aspects of resource management, e.g., energy-aware load balancing and application scaling for the cloud ecosystem, hierarchical architecture model for self-organizing and self-manageable systems and a new cloud delivery model based on auction-driven self-organization approach. 2. Algorithms. We also proposed several different algorithms for the models described above. Algorithms such as coalition formation, combinatorial auctions and clustering algorithm for scale-free organizations of scale-free networks. 3. Evaluation. Eventually we conducted different evaluations for the proposed models and algorithms in order to verify them. All the simulations reported in this thesis had been carried out on different instances and services of Amazon Web Services (AWS). All of these modules will be discussed in detail in the following chapters respectively

Model Operations for Quality-Driven Multimedia Delivery

With the recent advances in distributed systems and wireless technology, users can access any information, from anywhere with any device. Multimedia delivery services are currently under development to operate in such environments. In this context, it appears essential to offer and support different levels of service according to users requirements and expectations and to work towards quality-driven delivery (QDD). Implementing QDD mechanisms leads us to consider different issues such as system components interoperability, quality information management, distributed execution of QDD activities and multi-criteria optimization. In this paper, we focus on quality information management to support QDD. We propose a model management approach to the problem and we introduce metamodel and model operations for that purpose. We use conceptual graphs formalism to develop our QDD metamodel and we show how the conceptual graph derivation mechanism can be applied to implement some fundamental model operations

Human Resource and Employment Practices in Telecommunications Services, 1980-1998

[Excerpt] In the academic literature on manufacturing, much research and debate have focused on whether firms are adopting some form of “high-performance” or “high-involvement” work organization based on such practices as employee participation, teams, and increased discretion, skills, and training for frontline workers (Ichniowski et al., 1996; Kochan and Osterman, 1994; MacDuffie, 1995). Whereas many firms in the telecommunications industry flirted with these ideas in the 1980s, they did not prove to be a lasting source of inspiration for the redesign of work and employment practices. Rather, work restructuring in telecommunications services has been driven by the ability of firms to leverage network and information technologies to reduce labor costs and create customer segmentation strategies. “Good jobs” versus “bad jobs,” or higher versus lower wage jobs, do not vary according to whether firms adopt a high- involvement model. They vary along two other dimensions: (1) within firms and occupations, by the value-added of the customer segment that an employee group serves; and (2) across firms, by union and nonunion status. We believe that this customer segmentation strategy is becoming a more general model for employment practices in large-scale service | operations; telecommunications services firms may be somewhat more | advanced than other service firms in adopting this strategy because of certain unique industry characteristics. The scale economies of network technology are such that once a company builds the network infrastructure to a customer’s specifications, the cost of additional services is essentially zero. As a result, and notwithstanding technological uncertainty, all of the industry’s major players are attempting to take advantage of system economies inherent in the nature of the product market and technology to provide customized packages of multimedia products to identified market segments. They have organized into market-driven business units providing differentiated services to large businesses and institutions, small businesses, and residential customers. They have used information technologies and process reengineering to customize specific services to different segments according to customer needs and ability to pay. Variation in work and employment practices, or labor market segmentation, follows product market segmentation. As a result, much of the variation in employment practices in this industry is within firms and within occupations according to market segment rather than across firms. In addition, despite market deregulation beginning in 1984 and opportunities for new entrants, a tightly led oligopoly structure is replacing the regulated Bell System monopoly. Former Bell System companies, the giants of the regulated period, continue to dominate market share in the post-1984 period. Older players and new entrants alike are merging and consolidating in order to have access to multimedia markets. What is striking in this industry, therefore, is the relative lack of variation in management and employment practices across firms after more than a decade of experience with deregulation. We attribute this lack of variation to three major sources. (1) Technological advances and network economics provide incentives for mergers, organizational consolidation, and, as indicated above, similar business strategies. (2) The former Bell System companies have deep institutional ties, and they continue to benchmark against and imitate each other so that ideas about restructuring have diffused quickly among them. (3) Despite overall deunionization in the industry, they continue to have high unionization rates; de facto pattern bargaining within the Bell system has remained quite strong. Therefore, similar employment practices based on inherited collective bargaining agreements continue to exist across former Bell System firms

A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare

[EN]Knowledge management is one of the key priorities of many organizations. They face di erent challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge—experience, skills, insights, intuition, judgment and know-how—into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a di erent context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from di erent sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain

Automated Network Service Scaling in NFV: Concepts, Mechanisms and Scaling Workflow

Next-generation systems are anticipated to be digital platforms supporting innovative services with rapidly changing traffic patterns. To cope with this dynamicity in a cost-efficient manner, operators need advanced service management capabilities such as those provided by NFV. NFV enables operators to scale network services with higher granularity and agility than today. For this end, automation is key. In search of this automation, the European Telecommunications Standards Institute (ETSI) has defined a reference NFV framework that make use of model-driven templates called Network Service Descriptors (NSDs) to operate network services through their lifecycle. For the scaling operation, an NSD defines a discrete set of instantiation levels among which a network service instance can be resized throughout its lifecycle. Thus, the design of these levels is key for ensuring an effective scaling. In this article, we provide an overview of the automation of the network service scaling operation in NFV, addressing the options and boundaries introduced by ETSI normative specifications. We start by providing a description of the NSD structure, focusing on how instantiation levels are constructed. For illustrative purposes, we propose an NSD for a representative NS. This NSD includes different instantiation levels that enable different ways to automatically scale this NS. Then, we show the different scaling procedures the NFV framework has available, and how it may automate their triggering. Finally, we propose an ETSI-compliant workflow to describe in detail a representative scaling procedure. This workflow clarifies the interactions and information exchanges between the functional blocks in the NFV framework when performing the scaling operation.Comment: This work has been accepted for publication in the IEEE Communications Magazin

Wharekauri, Rēkohu, Chatham Islands health and social needs

This report is an independent review of the health and social needs of Chatham Islanders. Executive summary Background: Whānau Ora is about the transformation of whānau/family – with whānau/family setting their direction. Whānau Ora is driven by a focus on outcomes: that whānau/family will be self-managing; living healthy lifestyles; participating fully in society; confidently participating in te ao Māori (the Māori world); economically secure and successfully involved in wealth creation; and cohesive, resilient and nurturing. Ha O Te Ora O Wharekauri Trust – Māori Community Services (‘Māori Community Services’) is one of 34 Whānau Ora provider collectives across New Zealand. Within these Whānau Ora provider collectives, there are approximately 180 service providers. The number of providers within each provider collective varies from 1 to 20. Ha O Te Ora O Wharekauri Trust is one of the few Whānau Ora provider collectives with only one provider: their service arm Māori Community Services. Te Whānau Whāriki: Whānau Ora Business Plan was developed by Māori Community Services (2011) to ensure business continuity, enhance management and governance, and put in place adequate infrastructure and appropriately trained staff to support Whānau Ora-based delivery programmes. The business plan seeks innovative opportunities to do things differently to support whānau/families to realise their aspirations. The Ministry of Health commissioned a report on the health and social needs of Chatham Islands. Māori Community Services intends to use the report to guide their work based on the aspirations and realities of whānau/families living on Chatham Islands. Māori Community Services were also keen to explore the feasibility of holding a Health and Wellbeing Day on Chatham Island, potentially using a model similar to PHARMAC’s One Heart Many Lives Program. It is intended that this report will inform other health and social organisations based on Chatham Islands and on the ‘mainland’, so they can work together to support whānau/families on Chatham Islands to realise their aspirations in both the short and long term

A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks

In this work, we present a micro-service architecture which defines a Digital Twin (DT) framework for adaptive building automation and control. The DT framework primarily involves the orchestration of several containerized micro-services, promoting the scalability and deployability of the proposed framework within the industrial context. In the proposed framework, containerized microservices facilitate: (i) model-based control strategies; (ii) data-driven learning; (iii) data management; (iv) the inclusion of an internal High-Fidelity Simulator (HFS) to enable bootstrapped learning; and (v) a User Interface/User Experience (UI/UE) micro-service orchestrator. To validate the usefulness of the proposed framework, we implement a Physics Inspired Neural Network (PINN) to adapt the model-based control strategies for plant-model uncertainty and utilize bootstrap sampling against an internal HFS.publishedVersio