SinSO: An ontology of sustainability in software

Paybarjay H., Fallah Lajimi H., Hashemkhani Zolfani S.

Supplier segmentation is a strategic activity that evaluates suppliers, identifies different approaches, and identifies the most proper criteria to establish various segments. The main purpose is to form different supplier groups to adopt appropriate strategies for each group. Supplier development is another strategic activity designed to promote suppliers’ functions to create and maintain a network of competent suppliers that significantly affects a producer’s competitive advantage. To allocate scarce resources more efficiently, it is necessary to design some strategies to develop the suppliers for their different segments. This research has employed the sustainability approach that includes economic, environmental, and social dimensions to evaluate and then segment the suppliers. In the next step, the results of this segmentation have been utilized as a basis for supplier development. The sustainability approach is significant since it guarantees companies' short- and long-term interest by reducing expenditures and absorbing stakeholders who respect environmental and social values. To date, the sustainability approach has been used little for segmentation purposes. Using our supplier segmentation, the purchasing company was able to determine the economic, environmental, and social potential of its suppliers. As a result, the company can apply the appropriate development strategies that we proposed for each segment of its supplier base. We have employed an Interval Best–Worst Method and a Simple Additive Weighting in the grey environment to segment the suppliers and proposed a conceptual model to develop the suppliers in different segments (The proposed framework has been applied to a manufacturing company).

Zada I., Shahzad S., Ali S., Mehmood R.M.

González-Eras A., Santos R.D., Aguilar J., Lopez A.

COVID-19 has generated a lot of information in different formats, and one of them is in the ontology format. Also, there are previous ontologies from other disciplines that can help to analyze the COVID-19 pandemic. Thus, due to the large quantity of COVID-19 information in the form of ontologies, approaches to ontology integration and interoperability could be beneficial. In this context, this research proposes a new ontology, called COVID-19 Pandemic ontology, which is the product of an ontological engineering process proposed in this research that allows the integration of several ontologies to cover all the aspects of this infectious disease. The ontological engineering process defines tasks of fusion, alignment, and linking for integrating the ontologies. The resulting pandemic ontology provides a simple repository for storing information about the COVID-19, reusing existing ontologies, to offer multiple views about the disease, including the social context. This ontology has been tested in different case studies to prove its capabilities to infer useful information about the COVID-19 pandemic.

Raisian K., Yahaya J., Deraman A.

Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently, there are not enough works to address this problem, and the green status of software products is uncertain and unsure. This paper aims to identify the green measurements based on sustainable dimensions in a software product. The second objective is to reveal the relationships between the elements and measurements through empirical study. The study is conducted in two phases. The first phase is the theoretical phase, where the main components, measurements and practices that influence the sustainability of a software product are identified. The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achieving green software products. This study has revealed seven green measurements of software product: Productivity, Usability, Cost Reduction, Employee Support, Energy Efficiency, Resource Efficiency and Tool Support. The relationships are statistically significant, with a significance level of less than 0.01 (p = 0.000). Thus, the hypothesised relationships were all accepted. The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.

Borgo S., Ferrario R., Gangemi A., Guarino N., Masolo C., Porello D., Sanfilippo E.M., Vieu L.

dolce, the first top-level (foundational) ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. dolce is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. dolce clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet). Being a foundational ontology, dolce is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years dolce has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases.

Hoyos W., Aguilar J., Toro M.

Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years.Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified.Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%.We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.

Carver J.C., Cosden I.A., Hill C., Gesing S., Katz D.S.

Research software is a class of software developed to support research. Today a wealth of such software is created daily in universities, government, and commercial research enterprises worldwide. The sustainability of this software faces particular challenges due, at least in part, to the type of people who develop it. These Research Software Engineers (RSEs) face challenges in developing and sustaining software that differ from those faced by the developers of traditional software. As a result, professional associations have begun to provide support, advocacy, and resources for RSEs. These benefits are critical to sustaining RSEs, especially in environments where their contributions are often undervalued and not rewarded. This paper focuses on how professional associations, such as the United States Research Software Engineer Association (US-RSE), can provide this.

García-Berná J.A., Fernández-Alemán J.L., Carrillo de Gea J.M., Toval A., Mancebo J., Calero C., García F.

A personal health record is an eHealth technology in which users can observe their progress over time for a given condition. A research gap was identified in the literature concerning the study of the amount of energy that these systems need for their operation, and the energy efficiency that may be attained depending on their design. After the selection of five representative personal health records, a total of 20 tasks commonly done, and based on previous work, were performed with regard to two proposed scenarios, namely patient use and health personnel usage. The power consumption of the main components of a host machine was measured during the performance of the proposed duties. To that end, a hardware tool called the Energy Efficiency Tester was employed. The data collected were analyzed statistically, and significant differences were found in the respective consumption of the display (χ2 (4) = 23.782, p = 0.000), the processor (χ2 (4) = 29.018, p = 0.000) and the whole PC (χ2 (4) = 28.582, p = 0.000). For all of these components, NoMoreClipBoard was the personal health record that required the least energy (57.699 W for the display, 3.162 W for the processor and 181.113 W for the whole PC). A total of two strong correlations were found in the energy consumption between the hard disk and the graphics card (r = 0.791, p < 0.001), and the processor and the PC (r = 0.950, p < 0.001). Some features generated special amounts of power consumption, such as the news wall found on PatientsLikeMe, or the use of load icons that had an impact on most PC components. In addition, an in-depth analysis of the user interfaces was performed. A discussion was carried out on the design of the user interfaces, also taking into account recommendations drawn from the literature, checking for their implementation in the personal health records selected. With the aim of promoting sustainability among software developers, a best practice guideline on sustainable software design was proposed. Basic sustainability recommendations were collected for professionals to consider when developing a software system in general, and a personal health record in particular.

Saputri T.R., Lee S.

Context: Owing to the critical role of software-intensive systems in society, software engineers have the accountability to consider sustainability as a goal while structuring a software system. However, there are no practical guidelines providing a tangible decomposition of the sustainability aspect. Moreover, there are limited quantifiable methods to support sustainable design and analysis. Objectives: The purpose of this study is to help software practitioners to take sustainability into account by providing systematic guidelines for the software engineering process. We propose a framework that presents a meta model to decompose sustainability requirements and an assessment approach to evaluate sustainability achievements. Method: This work presents an integrated framework that combines a goal-based approach, scenario-based approach, and feature modeling to gather sustainability related requirements and corresponding features. For sustainability assessment, software analysis and machine learning techniques are utilized to analyze software products based on sustainability metrics and criteria. Results and Conclusions: The empirical study conducted with participants from academia and industry revealed that the proposed framework improves participant’s ability to consider sustainability aspect in their software engineering tasks through focusing on requirements, design, and evaluation. With the provided sustainability meta-model, the participants could extract more stakeholders, requirements, and features in shorter time. Moreover, the empirical study result also demonstrated that this study is capable to indicate specific scenarios that should be redesigned to improve the sustainability achievements level.

Khalifeh A., Farrell P., Alrousan M., Alwardat S., Faisal M.

PurposeThe paper aims to present a conceptual framework that helps in incorporating sustainability into software projects, highlights the importance of project sustainability and provides an extensive review of recent relevant contributions across various fields.Design/methodology/approachThe authors carried out a systematic bibliographic search on relevant published materials to analyse links between the two disciplines (sustainability and software projects). Furthermore, content analysis was applied to the final selected publications to identify and classify relevant triple bottom line (TBL) aspects to develop the framework.FindingsThe inclusion of TBL-related aspects is the most efficient and effective method used to incorporate sustainability into projects. Most of the relevant contributions in the software literature have focussed on either project product or project process or on one or two dimensions of sustainability rather than the three dimensions of the TBL theory. This study contributes by proposing a conceptual framework that encompasses TBL-related aspects for incorporating sustainability into processes and products of software projects.Research limitations/implicationsValidating the proposed framework empirically could be an interesting research issue. In addition, future works may focus on different types of industries, such as information systems, telecommunications and service sectors, which have seldom been studied in the literature.Practical implicationsSoftware companies – or other relevant organisations – may use the proposed framework as a measurement tool to evaluate the environmental and social impacts of their current products and project management practices. Consequently, these organisations may pay more attention to incorporating sustainability into their project management practices.Originality/valueThe proposed framework may contribute towards a more sustainable orientation by providing a unique combination of TBL-related aspects that gives academics and practitioners a better understanding of how software projects can be managed sustainably.

Sayah Z., Kazar O., Lejdel B., Laouid A., Ghenabzia A.

PurposeThis research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.Design/methodology/approachThis study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.FindingsThrough this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.Practical implicationsThe significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.Originality/valueThis work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.

Nazir S., Fatima N., Chuprat S., Sarkan H., F N., Sjarif N.N.

Mendonça M., Perozo N., Aguilar J.

This paper presents the concept of “Ontological Emergence”, a process that seeks to adapt an ontology to the changes and new components in a self-organized and emergent system, through the application of a set of rules that allows the emergence of a new conceptualization (emerging concepts). The Ontological Emergence provides the structuration of the information and knowledge that could be generated in the system, creating conceptual models that can adequately represent the new behavior that is emerging. It arises from the need to represent ontologically a conceptualization of a reality that is dynamic, which cannot be pre-defined or pre-determined, in order to generate emerging knowledge models that follows the scalability and the evolution of it. In that sense, in this paper is proposed an “Ontological Emergence Scheme” based on a set of processes of registration, monitoring, analysis and adaptation of the various conceptual models that interact in the system, as well as on some processing rules in regard to requirements and information of the context, in order to allow the ontological emergence. In this proposal scheme, the Meta-ontologies guide the ontological emergence process through the definition of general categories, to facilitate the integration of concepts from different ontologies or data sources. Finally, the paper presents some case studies, showing its utility in self-organized and emergent systems.

Huang C., Cai H., Xu L., Xu B., Gu Y., Jiang L.

To support intelligent manufacturing, providing a unified production data view by integrating distributed data collected by different enterprise information systems is critical. Because various information systems are often heterogeneous, ontology is widely adopted to present a global reference view for data integration. However, construction and maintenance of these ontologies is difficult because of the heterogeneity and dynamism of these large-scale data. In this paper, with the objective of intelligent manufacturing application implementation, we propose a comprehensive ontology generation and evolution method that automatically abstracts ontology from raw production data and dynamically adjusts the ontology in accordance with changes in the manufacturing data environment. The proposed method comprises four phases: data extraction, ontology construction, ontology connection, and ontology evolution. In the first phase, data from different sources are mapped to data entities to form a unified data structure. In the second phase, an initial ontology is generated via instance-driven ontology construction. In the third phase, to support intelligent manufacturing, the initial ontologies are organised in terms of the dimensions of the various business elements, such as stuff, machine, product, process, and scenarios. In the fourth phase, rules regarding ontology restrictions are formulated to realise ontology evolution that respond to changes in the manufacturing environment. To verify the efficacy of the proposed method, a prototype was implemented with real data from a manufacturing factory, in which the constructed ontology was used as the metadata of product data in intelligent manufacturing.

Wiśniewski D., Potoniec J., Ławrynowicz A., Keet C.M.

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented in an ontology. Despite that CQs are a part of several ontology engineering methodologies, the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of their respective formalizations in terms of, e.g., SPARQL queries. This paper aims to contribute to addressing the myriad of engineering hurdles to using CQs in ontology development. A prerequisite to this is to understand the relation between CQs and the queries over the ontology. We use a new dataset of 234 competency questions and their SPARQL-OWL queries for several ontologies in different domains developed by different groups, and analysed the CQs in two principal ways. The first stage focused on a linguistic analysis of the natural language text itself, i.e., a lexico-syntactic analysis without any presuppositions of ontology elements, and a subsequent step of semantic analysis in order to find patterns. This increased diversity of CQ sources resulted in a 4-5-fold increase of hitherto published patterns, to 106 distinct CQ patterns, which have a limited subset of few patterns shared across the CQ sets from the different ontologies. Next, we analysed the relation between the found CQ patterns and their respective SPARQL-OWL patterns, which revealed that one CQ pattern may be realized by more than one SPARQL-OWL query pattern, and vice versa. These insights may contribute to establishing common practices, templates, automation, and user tools that will support CQ formulation, formalization, execution, and general management.