Improving quality of service in application clusters

Quality of service (QoS) requirements, which include availability, integrity, performance and responsiveness are increasingly needed by science and engineering applications. Rising computational demands and data mining present a new challenge in the IT world. As our needs for more processing, research and analysis increase, performance and reliability degrade exponentially. In this paper we present a software system that manages quality of service for Unix based distributed application clusters. Our approach is synthetic and involves intelligent agents that make use of static and dynamic ontologies to monitor, diagnose and correct faults at run time, over a private network. Finally, we provide experimental results from our pilot implementation in a production environment

IT process architectures for enterprises development: A survey from a maturity model perspective

During the last years much has been published about IT governance. Close to the success of many governance efforts are the business frameworks, quality models, and technology standards that help enterprises improve processes, customer service, quality of products, and control. In this paper we i) survey existing frameworks, namely ITIL, ASL and BiSL, ii) find relations with the IT Governance framework CobiT to determine if the maturity model of CobiT can be used by ITIL, ASL and BiSL, and (iii) provide an integrated vista of IT processes viewed from a maturity model perspective. This perspective can help us understand the importance of maturity models for increasing the efficiency of IT processes for enterprises development and business-IT alignment

Rethinking Gardens and Public Parks (GPP) beyond Greenness: an Integrated Perspective about GPP ecosystem services in Cities

Increased urbanization carries socioenvironmental challenges that may be tackled by promoting green spaces as safeguards of sustainability and of the connection between users and the biosphere – reducing social inequalities, improving air quality, regenerating the ecosystem, and mitigating climate change impacts. Urban green spaces are the main drivers for increasing the quality of urban environments, potentializing local resilience, promoting sustainable lifestyles, as well as improving both the health and well-being of their users. The 11th goal of the 2030 United Nations Agenda for Sustainable Development states that societies must ensure universal access to safe, inclusive, and fully accessible public spaces. Nevertheless, cities are unequal spaces regarding the distribution of environmental resources, resulting in environmental injustice: the most vulnerable areas in socioeconomic terms are those that mostly experience environmental vulnerability, partially due to the lack of green spaces, the intensive road traffic - resulting in poor air quality and the increase of noise pollution – and the social segregation. The fair distribution of urban green spaces must be ensured in both quantity and quality in terms of their ecosystem services: provision, regulation, support, and cultural services. Municipal leaders are the main ones responsible for the maintenance of Gardens and Public Parks (GPP). However, current evidence identifies limited knowledge about urban green infrastructure governance, since the lack of data about PUGS is the main obstacle to effective intervention. Set against this background, this study aimed to identify clusters of ecosystem services potential in 25 GPP in the city of Porto, Portugal, through a validated tool application. Multivariate techniques allowed for the identification of predictor dimensions of ecosystem services potential, environmental quality, and facilities. Five PUGS clusters were validated: i) Environmentally Empowered and Socially Expectant Spaces, ii) Socioenvironmentally Empowered Spaces, iii) Environmentally Empowered but Socially Un-dynamic Spaces, iv) Socioenvironmentally Disempowered Spaces, and v) Socioenvironmentally Unexplored Spaces. Also, ecosystem service potential differs within the city and, in some cases, results in environmental injustice situations. These results are useful in the design of place-based intervention in PUGS, contributing to the increase of ecosystem services potential and in the improvement of urban environment quality and sustainability. Furthermore, instead of focusing on political-administrative jurisdictions, this methodological approach allows gains in scale and enhances ecosystem services potential at a municipal level.info:eu-repo/semantics/publishedVersio

Word Adjacency Graph Modeling: Separating Signal From Noise in Big Data

There is a need to develop methods to analyze Big Data to inform patient-centered interventions for better health outcomes. The purpose of this study was to develop and test a method to explore Big Data to describe salient health concerns of people with epilepsy. Specifically, we used Word Adjacency Graph modeling to explore a data set containing 1.9 billion anonymous text queries submitted to the ChaCha question and answer service to (a) detect clusters of epilepsy-related topics, and (b) visualize the range of epilepsy-related topics and their mutual proximity to uncover the breadth and depth of particular topics and groups of users. Applied to a large, complex data set, this method successfully identified clusters of epilepsy-related topics while allowing for separation of potentially non-relevant topics. The method can be used to identify patient-driven research questions from large social media data sets and results can inform the development of patient-centered interventions