A survey on service quality description

Quality of service (QoS) can be a critical element for achieving the business goals of a service provider, for the acceptance of a service by the user, or for guaranteeing service characteristics in a composition of services, where a service is defined as either a software or a software-support (i.e., infrastructural) service which is available on any type of network or electronic channel. The goal of this article is to compare the approaches to QoS description in the literature, where several models and metamodels are included. consider a large spectrum of models and metamodels to describe service quality, ranging from ontological approaches to define quality measures, metrics, and dimensions, to metamodels enabling the specification of quality-based service requirements and capabilities as well as of SLAs (Service-Level Agreements) and SLA templates for service provisioning. Our survey is performed by inspecting the characteristics of the available approaches to reveal which are the consolidated ones and which are the ones specific to given aspects and to analyze where the need for further research and investigation lies. The approaches here illustrated have been selected based on a systematic review of conference proceedings and journals spanning various research areas in computer science and engineering, including: distributed, information, and telecommunication systems, networks and security, and service-oriented and grid computing.

Two risky and costly end-user related taboos when developing information systems: qualifications and accountability

This work highlights two critical taboos in organizations: 1)taking for granted the quality of certain capabilities and attitudes of the end-user representatives (EUR) in information systems development projects (ISDP), and 2) the EUR´s inherent accountability for losses in IS investments. These issues are neither addressed by theory nor research when assessing success/ failure. A triangulation approach was applied to combine quantitative and qualitative methods, having convergent results and showing that in problematic cases, paradoxically, the origin of IS rejection by end users (EU) points towards the EUR themselves. It has been evaluated to what extent some EUR factors impacted a macro ISDP involving an enterprise resource planning (ERP) package, ranking the ‘knowledge of the EUR’ as the main latent variable. The results validate some issues found throughout decades of praxis, confirming that when not properly managed the EUR role by itself has a direct relationship with IS rejection and significant losses in IS investments.

Nondestructive quality evaluation and monitoring of Braeburn apples by Spatially Resolved Spectroscopy.

Contact Spatially Resolved Spectroscopy (SRS) measurements by means of a fiber-optics probe were employed for nondestructive assessment and monitoring of Braeburn apples during shelflife storage. SRS measurements and estimation of optical properties were calibrated and validated by means of liquid optical phantoms with known optical properties and a metamodeling method. The acquired optical properties (absorption and reduced scattering coefficients) for the apples during shelf-life storage were found to provide useful information for nondestructive evaluation of apple quality attributes (firmness and SSC) and for monitoring the changes in their microstructure and chemical composition. On-line SRS measurement was achieved by mounting the SRS probe over a conveyor system

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.

Key quality factors at urban interchanges.

Quality of service should not be overlooked in public transport planning and policy making, as it influences modal shift from car use to more sustainable means. Little research has been conducted on the quality of public transport interchanges from the perspective of current travellers (i.e. perceived quality). This work is thus aimed at identifying key quality factors in urban interchanges, through an exploratory approach (multiple correspondence analysis) that provides novel elements for further research. The methodology was applied at interchanges in Madrid and Gothenburg and the data used in the analysis were collected through customer satisfaction surveys conducted in 2011. The analysis identified five key quality factors per interchange. Ticketing plays a key role at both interchanges while physical and environmental issues emerged at Avenida de America in Madrid, and services, temporal issues and interconnectivity characterise Gothenburg central station. Compared with other quality aspects, classical issues such as safety/security and information are not perceived as important by intermodal travellers.

Towards a Learning Object pedagogical quality metric based on the LORI evaluation model

Evaluating and measuring the pedagogical quality of Learning Objects is essential for achieving a successful web-based education. On one hand, teachers need some assurance of quality of the teaching resources before making them part of the curriculum. On the other hand, Learning Object Repositories need to include quality information into the ranking metrics used by the search engines in order to save users time when searching. For these reasons, several models such as LORI (Learning Object Review Instrument) have been proposed to evaluate Learning Object quality from a pedagogical perspective. However, no much effort has been put in defining and evaluating quality metrics based on those models. This paper proposes and evaluates a set of pedagogical quality metrics based on LORI. The work exposed in this paper shows that these metrics can be effectively and reliably used to provide quality-based sorting of search results. Besides, it strongly evidences that the evaluation of Learning Objects from a pedagogical perspective can notably enhance Learning Object search if suitable evaluations models and quality metrics are used. An evaluation of the LORI model is also described. Finally, all the presented metrics are compared and a discussion on their weaknesses and strengths is provided.

3LD: towards high quality, industry-ready linguistic Linked Licensed Data

The application of Linked Data technology to the publication of linguistic data promises to facilitate interoperability of these data and has lead to the emergence of the so called Linguistic Linked Data Cloud (LLD) in which linguistic data is published following the Linked Data principles. Three essential issues need to be addressed for such data to be easily exploitable by language technologies: i) appropriate machine-readable licensing information is needed for each dataset, ii) minimum quality standards for Linguistic Linked Data need to be defined, and iii) appropriate vocabularies for publishing Linguistic Linked Data resources are needed. We propose the notion of Licensed Linguistic Linked Data (3LD) in which different licensing models might co-exist, from totally open to more restrictive licenses through to completely closed datasets.

Social Indicators in the Sustainable Refurbishment: Calculating Quality of Life Indicator in a Residential Building in Málaga

How can we measure ‘quality of life’? The sustainable refurbishment goes beyond strictly energy aspects. Sustainability indicators are needed to facilitate data collection and to provide information which does not require too time-consuming calculations. Thus, you can offer an idea of the extent and quality of the rehabilitation before starting the project and, also, the obtained results can be evaluated in an agile way after the refurbishment. From a list of social indicators gathered from different methods, sustainability assessment tools and International and European standards, three social indicators are proposed: Users Satisfaction, Participation Agreement and Quality of Life. This paper shows the development of Quality of Life social indicator, the more closely related to the main objectives of Researchand Development Project “Sustainable Refurbishment”: improving energy efficiency and wellbeing of users in existing residential buildings. Finally, this social indicator is applied to a real case study in Málaga (Spain).

Urban transport interchanges: Importance-Performance analysis for evaluating perceived quality

As the world becomes more urbanised, public transport in cities must seek to provide viable alternatives to individual car transport. At an urban level, interchanges in public transport networks provide easy transfers between and within different transport modes and facilitate seamless travel. This study proposes a methodological framework with which to identify the factors that travellers view as key elements of an urban transport interchange. An attitudinal survey was undertaken in order to collect information about users? needs and perceptions in the Moncloa interchange in Madrid, Spain. The results obtained from an Importance-Performance Analysis (IPA) show that aspects related to the signposting of different facilities and transport services, the internal design of the interchange and the surrounding area, and safety and security are the greatest strengths of the interchange.

Information reconciliation methods in secret key distribution

We consider in this thesis the problem of information reconciliation in the context of secret key distillation between two legitimate parties. In some scenarios of interest this problem can be advantageously solved with low density parity check (LDPC) codes optimized for the binary symmetric channel. In particular, we demonstrate that our method leads to a significant efficiency improvement, with respect to earlier interactive reconciliation methods. We propose a protocol based on LDPC codes that can be adapted to changes in the communication channel extending the original source. The efficiency of our protocol is only limited by the quality of the code and, while transmitting more information than needed to reconcile Alice’s and Bob’s sequences, it does not reveal any more information on the original source than an ad-hoc code would have revealed.---ABSTRACT---En esta tesis estudiamos el problema de la reconciliación de información en el contexto de la destilación de secreto entre dos partes. En algunos escenarios de interés, códigos de baja densidad de ecuaciones de paridad (LDPC) adaptados al canal binario simétrico ofrecen una buena solución al problema estudiado. Demostramos que nuestro método mejora significativamente la eficiencia de la reconciliación. Proponemos un protocolo basado en códigos LDPC que puede ser adaptado a cambios en el canal de comunicaciones mediante una extensión de la fuente original. La eficiencia de nuestro protocolo está limitada exclusivamente por el código utilizado y no revela información adicional sobre la fuente original que la que un código con la tasa de información adaptada habría revelado.

Are team personality and climate related to satisfaction and software quality? Aggregating results from a twice replicated experiment

Research into software engineering teams focuses on human and social team factors. Social psychology deals with the study of team formation and has found that personality factors and group processes such as team climate are related to team effectiveness. However, there are only a handful of empirical studies dealing with personality and team climate and their relationship to software development team effectiveness. Objective We present aggregate results of a twice replicated quasi-experiment that evaluates the relationships between personality, team climate, product quality and satisfaction in software development teams. Method Our experimental study measures the personalities of team members based on the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and team climate factors (participative safety, support for innovation, team vision and task orientation) preferences and perceptions. We aggregate the results of the three studies through a meta-analysis of correlations. The study was conducted with students. Results The aggregation of results from the baseline experiment and two replications corroborates the following findings. There is a positive relationship between all four climate factors and satisfaction in software development teams. Teams whose members score highest for the agreeableness personality factor have the highest satisfaction levels. The results unveil a significant positive correlation between the extraversion personality factor and software product quality. High participative safety and task orientation climate perceptions are significantly related to quality. Conclusions First, more efficient software development teams can be formed heeding personality factors like agreeableness and extraversion. Second, the team climate generated in software development teams should be monitored for team member satisfaction. Finally, aspects like people feeling safe giving their opinions or encouraging team members to work hard at their job can have an impact on software quality. Software project managers can take advantage of these factors to promote developer satisfaction and improve the resulting product.

Quality and productivity analysis in a very small enterprise using TSPi

This article shows the benefits of developing a software project using TSPi in a "Very Small Enterprise" based in quality and productivity measures. An adapted process from the current process based on the TSPi was defined and the team was trained in it. The pilot project had schedule and budget constraints. The workaround began by gathering historical data from previous projects in order to get a measurement repository, and then the project metrics were collected. Finally, the process, product and quality improvements were verified

Quality cost in bus operations based on activity-based costing

The study of service quality and its implication for transport contracts has several approaches in research and practical applications, where the main emphasis is the consideration of quality from the user's point of view, thus obtaining a customer satisfaction index as a measurement of the overall quality with no further implications for service providers. The main aim of this paper is to estimate the real economic impact of improving quality attributes for a bus operator. An application of the activity-based costing methodology is developed for a bus contract in Madrid, using quality data from surveys together with economic and performance information, and focusing on headway as a quality variable. Results show the consistency and practicality of this methodology, overcoming simplifications from traditional procedures. This method is a powerful tool in quality-based contracting as well as for effective investment in transport quality under poverty funding constraints

New QR Survey Methodologies to Analyze User Perception of Service Quality in Public Transport: The Experience of Madrid

Customer Satisfaction Surveys (CSS) have become an important tool for public transport planners, as improvements in the perceived quality of service lead to greater use of public transport and lower traffic pollution. Until now, Intelligent Transportation System (ITS) enhancements in public transport have traditionally included fleet management systems based on Automatic Vehicle Location (AVL) technologies, which can be used to optimize routing and scheduling, and to feed real-time information into passenger information channels. However, surveys of public transport users could also benefit from the new information technologies. As most customers carry their smartphones when traveling, Quick Response (QR) codes open up the possibility of conducting these surveys at a lower cost.This paper contributes to the limited existing literature by developing the analysis of QR codes applied to CSS in public transport and highlighting their importance in reducing the cost of data collection and processing. The added value of this research is that it provides the first assessment of a real case study in Madrid (Spain) using QR codes for this purpose. This pilot experience was part of a research project analyzing bus service quality in the same case study, so the QR code survey (155 valid questionnaires) was validated using a conventional face-to-face survey (520 valid questionnaires). The results show clearly that, after overcoming a few teething troubles, this QR code application will ultimately provide transport management with a useful tool to reduce survey costs

Proposal of a trust and reputation model to provide recommendation in open quality oriented environments

El auge y penetración de las nuevas tecnologías junto con la llamada Web Social están cambiando la forma en la que accedemos a la medicina. Cada vez más pacientes y profesionales de la medicina están creando y consumiendo recursos digitales de contenido clínico a través de Internet, surgiendo el problema de cómo asegurar la fiabilidad de estos recursos. Además, un nuevo concepto está apareciendo, el de pervasive healthcare o sanidad ubicua, motivado por pacientes que demandan un acceso a los servicios sanitarios en todo momento y en todo lugar. Este nuevo escenario lleva aparejado un problema de confianza en los proveedores de servicios sanitarios. Las plataformas de eLearning se están erigiendo como paradigma de esta nueva Medicina 2.0 ya que proveen un servicio abierto a la vez que controlado/supervisado a recursos digitales, y facilitan las interacciones y consultas entre usuarios, suponiendo una buena aproximación para esta sanidad ubicua. En estos entornos los problemas de fiabilidad y confianza pueden ser solventados mediante la implementación de mecanismos de recomendación de recursos y personas de manera confiable. Tradicionalmente las plataformas de eLearning ya cuentan con mecanismos de recomendación, si bien están más enfocados a la recomendación de recursos. Para la recomendación de usuarios es necesario acudir a mecanismos más elaborados como son los sistemas de confianza y reputación (trust and reputation) En ambos casos, tanto la recomendación de recursos como el cálculo de la reputación de los usuarios se realiza teniendo en cuenta criterios principalmente subjetivos como son las opiniones de los usuarios. En esta tesis doctoral proponemos un nuevo modelo de confianza y reputación que combina evaluaciones automáticas de los recursos digitales en una plataforma de eLearning, con las opiniones vertidas por los usuarios como resultado de las interacciones con otros usuarios o después de consumir un recurso. El enfoque seguido presenta la novedad de la combinación de una parte objetiva con otra subjetiva, persiguiendo mitigar el efecto de posibles castigos subjetivos por parte de usuarios malintencionados, a la vez que enriquecer las evaluaciones objetivas con información adicional acerca de la capacidad pedagógica del recurso o de la persona. El resultado son recomendaciones siempre adaptadas a los requisitos de los usuarios, y de la máxima calidad tanto técnica como educativa. Esta nueva aproximación requiere una nueva herramienta para su validación in-silico, al no existir ninguna aplicación que permita la simulación de plataformas de eLearning con mecanismos de recomendación de recursos y personas, donde además los recursos sean evaluados objetivamente. Este trabajo de investigación propone pues una nueva herramienta, basada en el paradigma de programación orientada a agentes inteligentes para el modelado de comportamientos complejos de usuarios en plataformas de eLearning. Además, la herramienta permite también la simulación del funcionamiento de este tipo de entornos dedicados al intercambio de conocimiento. La evaluación del trabajo propuesto en este documento de tesis se ha realizado de manera iterativa a lo largo de diferentes escenarios en los que se ha situado al sistema frente a una amplia gama de comportamientos de usuarios. Se ha comparado el rendimiento del modelo de confianza y reputación propuesto frente a dos modos de recomendación tradicionales: a) utilizando sólo las opiniones subjetivas de los usuarios para el cálculo de la reputación y por extensión la recomendación; y b) teniendo en cuenta sólo la calidad objetiva del recurso sin hacer ningún cálculo de reputación. Los resultados obtenidos nos permiten afirmar que el modelo desarrollado mejora la recomendación ofrecida por las aproximaciones tradicionales, mostrando una mayor flexibilidad y capacidad de adaptación a diferentes situaciones. Además, el modelo propuesto es capaz de asegurar la recomendación de nuevos usuarios entrando al sistema frente a la nula recomendación para estos usuarios presentada por el modo de recomendación predominante en otras plataformas que basan la recomendación sólo en las opiniones de otros usuarios. Por último, el paradigma de agentes inteligentes ha probado su valía a la hora de modelar plataformas virtuales complejas orientadas al intercambio de conocimiento, especialmente a la hora de modelar y simular el comportamiento de los usuarios de estos entornos. La herramienta de simulación desarrollada ha permitido la evaluación del modelo de confianza y reputación propuesto en esta tesis en una amplia gama de situaciones diferentes. ABSTRACT Internet is changing everything, and this revolution is especially present in traditionally offline spaces such as medicine. In recent years health consumers and health service providers are actively creating and consuming Web contents stimulated by the emergence of the Social Web. Reliability stands out as the main concern when accessing the overwhelming amount of information available online. Along with this new way of accessing the medicine, new concepts like ubiquitous or pervasive healthcare are appearing. Trustworthiness assessment is gaining relevance: open health provisioning systems require mechanisms that help evaluating individuals’ reputation in pursuit of introducing safety to these open and dynamic environments. Technical Enhanced Learning (TEL) -commonly known as eLearning- platforms arise as a paradigm of this Medicine 2.0. They provide an open while controlled/supervised access to resources generated and shared by users, enhancing what it is being called informal learning. TEL systems also facilitate direct interactions amongst users for consultation, resulting in a good approach to ubiquitous healthcare. The aforementioned reliability and trustworthiness problems can be faced by the implementation of mechanisms for the trusted recommendation of both resources and healthcare services providers. Traditionally, eLearning platforms already integrate recommendation mechanisms, although this recommendations are basically focused on providing an ordered classifications of resources. For users’ recommendation, the implementation of trust and reputation systems appears as the best solution. Nevertheless, both approaches base the recommendation on the information from the subjective opinions of other users of the platform regarding the resources or the users. In this PhD work a novel approach is presented for the recommendation of both resources and users within open environments focused on knowledge exchange, as it is the case of TEL systems for ubiquitous healthcare. The proposed solution adds the objective evaluation of the resources to the traditional subjective personal opinions to estimate the reputation of the resources and of the users of the system. This combined measure, along with the reliability of that calculation, is used to provide trusted recommendations. The integration of opinions and evaluations, subjective and objective, allows the model to defend itself against misbehaviours. Furthermore, it also allows ‘colouring’ cold evaluation values by providing additional quality information such as the educational capacities of a digital resource in an eLearning system. As a result, the recommendations are always adapted to user requirements, and of the maximum technical and educational quality. To our knowledge, the combination of objective assessments and subjective opinions to provide recommendation has not been considered before in the literature. Therefore, for the evaluation of the trust and reputation model defined in this PhD thesis, a new simulation tool will be developed following the agent-oriented programming paradigm. The multi-agent approach allows an easy modelling of independent and proactive behaviours for the simulation of users of the system, conforming a faithful resemblance of real users of TEL platforms. For the evaluation of the proposed work, an iterative approach have been followed, testing the performance of the trust and reputation model while providing recommendation in a varied range of scenarios. A comparison with two traditional recommendation mechanisms was performed: a) using only users’ past opinions about a resource and/or other users; and b) not using any reputation assessment and providing the recommendation considering directly the objective quality of the resources. The results show that the developed model improves traditional approaches at providing recommendations in Technology Enhanced Learning (TEL) platforms, presenting a higher adaptability to different situations, whereas traditional approaches only have good results under favourable conditions. Furthermore the promotion period mechanism implemented successfully helps new users in the system to be recommended for direct interactions as well as the resources created by them. On the contrary OnlyOpinions fails completely and new users are never recommended, while traditional approaches only work partially. Finally, the agent-oriented programming (AOP) paradigm has proven its validity at modelling users’ behaviours in TEL platforms. Intelligent software agents’ characteristics matched the main requirements of the simulation tool. The proactivity, sociability and adaptability of the developed agents allowed reproducing real users’ actions and attitudes through the diverse situations defined in the evaluation framework. The result were independent users, accessing to different resources and communicating amongst them to fulfil their needs, basing these interactions on the recommendations provided by the reputation engine.