Search results

Managing components' resources plays a critical role in the success of systems' architectures designed for cyber–physical systems (CPS). Performing the selection of candidate components to pursue a specific application's needs also involves identifying the relationships among architectural components, the network and the physical process, as the system characteristics and properties are related.

Using a Model-Driven Engineering (MDE) approach is a valuable asset therefore. Within this context, the authors present the so-called Systems Architecture Ontology (SAO), which allows the representation of a system architecture (SA), as well as the relationships, characteristics and properties of a CPS application.

SAO uses a common vocabulary inspired by the Architecture Analysis and Design Language (AADL) standard. To demonstrate SAO's applicability, this paper presents its use as an MDE approach combined with ontology-based modeling through the Ontology Web Language (OWL). From OWL models based on SAO, the authors propose a model transformation tool to extract data related to architectural modeling in AADL code, allowing the creation of a components' library and a property set model. Besides saving design time by automatically generating many lines of code, such code is less error-prone, that is, without inconsistencies.

To illustrate the proposal, the authors present a case study in the aerospace domain with the application of SAO and its transformation tool. As result, a library containing 74 components and a related set of properties are automatically generated to support architectural design and evaluation.

The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.

A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.

The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.

The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.

The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.

This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.

This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).

Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.

In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.

The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

The purpose of this paper is to provide a method to formalize information security control descriptions and a decision support system increasing the automation level and, therefore, the cost efficiency of the information security compliance checking process. The authors advanced the state-of-the-art by developing and applying the method to ISO 27002 information security controls and by developing a semantic decision support system.

The research has been conducted under design science principles. The formalized information security controls were used in a compliance/risk management decision support system which has been evaluated with experts and end-users in real-world environments.

There are different ways of obtaining compliance to information security standards. For example, by implementing countermeasures of different quality depending on the protection needs of the organization. The authors developed decision support mechanisms which use the formal control descriptions as input to support the decision-maker at identifying the most appropriate countermeasure strategy based on cost and risk reduction potential.

Formalizing and mapping the ISO 27002 controls to the security ontology enabled the authors to automatically determine the compliance status and organization-wide risk-level based on the formal control descriptions and the modelled environment, including organizational structures, IT infrastructure, available countermeasures, etc. Furthermore, it allowed them to automatically determine which countermeasures are missing to ensure compliance and to decrease the risk to an acceptable level.

Following the lead of neighboring fields such as strategy and organization studies, entrepreneurship is gradually joining in the adoption of a practice perspective. Entrepreneurship as practice (EaP) is thus a nascent domain of investigation where the methodological debate is still unsettled and very fluid. In this paper, the authors contribute to this debate with a focus on family entrepreneurship.

The authors develop a conceptual paper to discuss what it entails to look at family entrepreneurship through a practice lens and why it is fruitful. Moreover, the authors propose a research strategy novel to the field through which such investigation can be pursued, namely process tracing, and examine its inferential logic.

Process tracing is a strategy of data analysis underpinned by an ontology of causal mechanisms. The authors argue that it complements other practice methods by inferring social mechanisms from empirical evidence and thereby establishing a connection between praxis, practices and practitioners.

Process tracing helps the articulation of an “integrated model” of practice that relates praxis, practices and practitioners to the outcome they jointly produce. By enabling the assessment of impact, process tracing helps providing prima facie evidentiary grounds for policy action and intervention.

Process tracing affinity with the practice perspective has been so far acknowledged only to a limited extent in the social sciences, and it is, in fact, a novel research strategy for the family entrepreneurship field.

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

The article contributes to affective ethnography focussing on the fluidity of organizational spacing. Through the concept of affective space, it highlights those elements that are ephemeral and elusive – like affect, aesthetics, atmosphere, intensity, moods – and proposes to explore affect as spatialized and space as affective.

Fluidity is proposed as a conceptual lens that sits at the conjunction of space and affect, highlighting both the movement in time and space, and the mutable relationships that the capacity of affecting and being affected weaves. It experiments with “writing differently” in affective ethnography, thus performing the space of representation of affective space.

The article enriches the alternative to a conceptualization of organizations as stable entities, considering organizing in its spatial fluidity and in being a fragmented, affective and dispersed phenomenon.

The article's writing is an example of intertextuality constructed through five praxiographic stories that illustrate the multiple fluidity of affective spacing in terms of temporal fluidity, fluidity of boundaries, of participation, of the object of practice, and atmospheric fluidity.