Achieving Autonomic Computing through the Use of Variability Models at Run-time

Increasingly, software needs to dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing infrastructure and in the surrounding physical environment. Adaptability is emerging as a necessary underlying capability, particularly for highly dynamic systems such as context-aware or ubiquitous systems. By automating tasks such as installation, adaptation, or healing, Autonomic Computing envisions computing environments that evolve without the need for human intervention. Even though there is a fair amount of work on architectures and their theoretical design, Autonomic Computing was criticised as being a \hype topic" because very little of it has been implemented fully. Furthermore, given that the autonomic system must change states at runtime and that some of those states may emerge and are much less deterministic, there is a great challenge to provide new guidelines, techniques and tools to help autonomic system development. This thesis shows that building up on the central ideas of Model Driven Development (Models as rst-order citizens) and Software Product Lines (Variability Management) can play a signi cant role as we move towards implementing the key self-management properties associated with autonomic computing. The presented approach encompass systems that are capable of modifying their own behavior with respect to changes in their operating environment, by using variability models as if they were the policies that drive the system's autonomic recon guration at runtime. Under a set of recon guration commands, the components that make up the architecture dynamically cooperate to change the con guration of the architecture to a new con guration. This work also provides the implementation of a Model-Based Recon guration Engine (MoRE) to blend the above ideas. Given a context event, MoRE queries the variability models to determine how the system should evolve, and then it provides the mechanisms for modifying the system.Cetina Englada, C. (2010). Achieving Autonomic Computing through the Use of Variability Models at Run-time [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7484Palanci

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Internet of Things 2.0: Concepts, Applications, and Future Directions

Applications and technologies of the Internet of Things are in high demand with the increase of network devices. With the development of technologies such as 5G, machine learning, edge computing, and Industry 4.0, the Internet of Things has evolved. This survey article discusses the evolution of the Internet of Things and presents the vision for Internet of Things 2.0. The Internet of Things 2.0 development is discussed across seven major fields. These fields are machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security. Other than these major fields, the architectural development of the Internet of Things and major types of applications are also reviewed. Finally, this article ends with the vision and current limitations of the Internet of Things in future network environments

PLA-based Runtime Dynamism in Support of Privacy-Enhanced Web Personalization

Software product line architectures (PLAs) have been widely recognized as a successful approach in industrial software development for improving productivity, software quality and time-to-market. In this paper, we focus on the usage of a PLA for a quite different purpose, namely, handling privacy constraints in web personalization. To provide personalized services such as customized recommendations, a personalized website collects users ’ personal data, which in turn stirs up various privacy concerns. We aim at reconciling the benefits of web personalization with privacy constraints that come from users themselves as well as from privacy legislations and regulations that apply to a given user. We propose a dynamic, privacy-enabling personalization infrastructure and conceive it as a PLA. This infrastructure allows for dynamically selecting and instantiating personalization architectures that provide personalized services to each individual user and comply with the prevailing privacy constraints. 1. Introduction an

Adaptive algorithms for real-world transactional data mining.

The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related) in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identification and classification of customers. Data models derived from the data mining process can aid in effectively recognizing the status and preference of customers - individually and as a group. Such data models can be incorporated into the business market segmentation, customer targeting and channelling decisions with the goal of maximizing the total customer lifetime profit. However, due to costs, privacy and/or data protection reasons, the customer data available for data mining is often restricted to verified and validated data,(in most cases,only the business owned transactional data is available). Transactional data is a valuable resource for generating such data models. Transactional data can be electronically collected and readily made available for data mining in large quantity at minimum extra cost. Transactional data is however, inherently sparse and skewed. These inherent characteristics of transactional data give rise to the poor performance of data models built using customer data based on transactional data. Data models for identifying, describing, and classifying customers, constructed using evolving transactional data thus need to effectively handle the inherent sparseness and skewness of evolving transactional data in order to be efficient and accurate. Using real-world transactional data, this thesis presents the findings and results from the investigation of data mining algorithms for analysing, describing, identifying and classifying customers with evolving needs. In particular, methods for handling the issues of scalability, uncertainty and adaptation whilst mining evolving transactional data are analysed and presented. A novel application of a new framework for integrating transactional data binning and classification techniques is presented alongside an effective prototype selection algorithm for efficient transactional data model building. A new change mining architecture for monitoring, detecting and visualizing the change in customer behaviour using transactional data is proposed and discussed as an effective means for analysing and understanding the change in customer buying behaviour over time. Finally, the challenging problem of discerning between the change in the customer profile (which may necessitate the effective change of the customer’s label) and the change in performance of the model(s) (which may necessitate changing or adapting the model(s)) is introduced and discussed by way of a novel flexible and efficient architecture for classifier model adaptation and customer profiles class relabeling

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Actas da 2ª Conferência Nacional em Interacção Pessoa-Máquina

Actas da 2ª Conferência Nacional em Interacção Pessoa-Máquina, Braga, 16-18 Outubro de 2006Depois do sucesso da primeira edição, organizada em Julho de 2004 na Faculdade de Ciências da Universidade de Lisboa, organizou-se em 2006 a Interacç˜ão 2006 – 2a. Conferência Nacional em Interacç˜ao Pessoa-Máquina – numa iniciativa conjunta do Grupo Português de Computação Gráfica e do Departamento de Informática/Centro de Ciências da Computação da Universidade do Minho. Tal como na sua primeira edição˜ a Interacção 2006 visou promover um ponto de encontro da comunidade interessada na Interacção Pessoa-Máquina em Portugal. Reunindo investigadores, docentes e profissionais, permitiu a divulgação de trabalhos e a troca de experiências entre as comunidades académica e industrial