Conceptual Modeling Techniques Research Articles

Modeling the rainfall–runoff process in the Bird Creek River Basin: a comparative analysis of conceptual and artificial neural network techniques

ABSTRACT The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.

Evidence of large-scale conceptual disarray in multi-level taxonomies in Wikidata

The distinction between types and individuals is key to most conceptual modeling techniques and knowledge representation languages. Despite that, there are a number of situations in which modelers navigate this distinction inadequately, leading to problematic models. We show evidence of a large number of representation mistakes associated with the failure to employ this distinction in the Wikidata knowledge graph, which can be identified with the incorrect use of instantiation, which is a relation between an instance and a type, and specialization (or subtyping), which is a relation between two types. The prevalence of the problems in Wikidata’s taxonomies suggests that methodological and computational tools are required to mitigate the issues identified, which occur in many settings when individuals, types, and their metatypes are included in the domain of interest. We conduct a conceptual analysis of entities involved in recurrent erroneous cases identified in this empirical data, and present a tool that supports users in identifying some of these mistakes.

Choice of conceptualization of a problem situation by an intelligent agent in decision-making tasks

The research in the domain of autonomous intelligent agent is the foreground of the introduction of artificial intelligence solution in all areas of economy. The intelligent autonomous systems combine the usage of pattern recognition, reasoning, decision making, conceptual modeling techniques and methods. The important part of intelligent agent implementation is to find the conceptualization which is suitable to the current problematic situation. Despite all progress around autonomous intelligent agents, humans are much more flexible and creative in making the right conceptualizations. They seamlessly adapt to the situation at hand and filter out all irrelevant details, using multiple perspectives and representations for the same objects. This research makes assumptions that every intelligent agent dynamically creates its own ontology used to interpret local knowledge. The mappings are established with this local ontology and the ontologies of other agents when needed, in order to share and reuse knowledge. In the article a formal model of problematic situation in the context of decision-making operation is presented. Models used in decision making and their relationships are described. In the second part of the article we analyze the process of conceptualization selection and arrive to the conclusion that this selection is done on multiple levels, starting from selecting the communicating agent with relevant domain of expertise, selecting and aligning ontologies of agents, selecting patterns and patterns languages which better correspond to the situation and lastly, selecting the relevant interpretations of concepts and relationships. In the last part of article, the problem of the selection of relevant knowledge provider is solved, using modified TOPSIS method. The proposed approach and directions of research will help to add flexibility to conceptual modeling of problematic situations by intelligent agents.

Understanding supply chain resilience through conceptual modelling techniques

PurposeThis paper aims to propose a conceptualization of the supply chain resilience domain using conceptual modelling techniques formalized through a metamodel and illustrated through an example.Design/methodology/approachThis research uses conceptual modelling techniques to build and modularize the metamodel, the latter to manage complexity. The metamodel was built iteratively and subsequently instantiated with an example of a yogurt factory to analyse its usefulness and theoretical relevance, and thus its contributions to the domain.FindingsConceptual modelling techniques can represent a complex domain such as supply chain resilience simply, and the proposed metamodel makes it possible to create models that become valuable decision support tools.Originality/valueConsolidation and structuring of concepts in the supply chain resilience domain through conceptual modelling techniques.

Polycode as a Strategic Resource of Intercultural Communication

The study discusses the application of adaptive strategies in the process of cultural integration under the conditions of multilingualism, taking into account the dynamics of aggregation of foreign cultural elements in the linguistic consciousness of foreigners. The study was carried out in an interdisciplinary plane, integrating the developments of psycholinguistics, communication theory, linguoculturology and sociolinguistics. The work is based on the basic methods of conducting a free associative experiment, lexico-semantic analysis of the received reactions, contextual analysis and interpretation of verbal associations, interlingual comparison techniques, conceptual modeling techniques and linguistic and cultural commentary. The relevance of the research is due to the importance of the interactive interaction of languages and cultures that form the civilizational communicative space. The novelty of the undertaken research is related to the identification of the role of the multilingual cultural landscape as a translator of ethnic and national values by scanning the slices of the introduction of foreign cultural language segments into the linguistic consciousness of foreign students. The article is aimed at monitoring the multilingual diversity and the strategic repertoire of learning new foreign languages in the multicultural environment of the host country based on the data obtained as a result of a free associative experiment using the «adaptation in the country and region of education» stimulus. The results of the study indicate that the reactions to a complex stimulus obtained in a phased experiment made it possible to identify a core universal for the phased dynamics of adaptation in the structure of the field model of the associative layer of the concept and describe the content of cognitive features formed by the reactions. The analysis of verbal representations demonstrated the motivation for the use of communicative response strategies by foreigners and the specifics of embedding in a foreign cultural environment.

An Integrated DPSIR-SD Framework for Sustainability Assessment of Roads in Australia

The Australian Sustainability Development Goals (SDGs) Summit in 2018 attracted much-needed national attention towards environmental goals and targets compared with other aspects of sustainability. Road infrastructure is the backbone of modern society and plays a crucial role in accomplishing a targeted balance between these aspects of sustainability and achieving the SDGs. This article presents an integrated sustainability performance assessment methodology that acts as a decision support tool. A series of two conceptual modelling techniques—drivers—pressure—state—impact—response (DPSIR) and system dynamics (SD)—is employed, with the cause-and-effect relationships of the sustainability indicators developed utilising the DPSIR framework, and a quantitative analysis carried out through a subsequent SD model. The end result is the generation of a Sustainability Performance Index (SPI) for road infrastructure created by analysing the SD model and DPSIR index layer relationship. The benefits and applicability of the proposed methodology are validated through case study analysis. The overall aim is to determine restricting factors and response strategies influencing road infrastructure and transport sustainability performance during the operation and maintenance phase. Thus, a significant contribution is made through the proposed methodology for assessing factors influencing the long-term achievement of the SDGs.

Mathematical models on COVID-19 in India: A systematic review protocol

Background: More than 278 million cases and more than 5.4 million deaths due to coronavirus disease (COVID-19) were reported worldwide by the end of 2021. More than 34 million cases and more than 478,000 deaths have been reported in India. Epidemiologists, physicians and virologists are working on a number of conceptual, theoretical or mathematical modelling techniques in the battle against COVID-19. Protocol: This systematic review aims to provide a comprehensive review of published mathematical models on COVID-19 in India and the concepts behind the development of mathematical models on COVID-19, including assumptions, modelling techniques, and data inputs. Initially, related keywords and their synonyms will be searched in the Global Literature on Coronavirus Disease database managed by World Health Organisation (WHO). The database includes searches of bibliographic databases (MEDLINE, Scopus, Web of Science, EMBASE etc.,), preprints (MEDRXIV), manual searching, and the addition of other expert-referred scientific articles. This database is updated daily (Monday through Friday). Two independent reviewers will be involved in screening the titles and abstracts at the first stage and full-texts at the second stage, and they will select studies as per the inclusion and exclusion criteria. The studies will be selected for their quality, transparency, and ethical aspects, using the Overview, Design concepts, Details (ODD) protocol and International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making (ISPOR-SMDM) guidelines. Data will be extracted using standardized data extraction tools and will be synthesized for analysis. Disagreements will be resolved through discussion, or with a third reviewer. Conclusions: This systematic review will be performed to critically examine relevant literature of existing mathematical models of COVID-19 in India. The findings will help to understand the concepts behind the development of mathematical models on COVID-19 conducted in India in terms of their assumptions, modelling techniques, and data inputs.

On how to generalize species-specific conceptual schemes to generate a species-independent Conceptual Schema of the Genome

BackgroundUnderstanding the genome, with all of its components and intrinsic relationships, is a great challenge. Conceptual modeling techniques have been used as a means to face this challenge. The heterogeneity and idiosyncrasy of genomic use cases mean that conceptual modeling techniques are used to generate conceptual schemes that focus on too specific scenarios (i.e., they are species-specific conceptual schemes). Our research group developed two different conceptual schemes. The first one is the Conceptual Schema of the Human Genome, which is intended to improve Precision Medicine and genetic diagnosis. The second one is the Conceptual Schema of the Citrus Genome, which is intended to identify the genetic cause of relevant phenotypes in the agri-food field.MethodsOur two conceptual schemes have been ontologically compared to identify their similarities and differences. Based on this comparison, several changes have been performed in the Conceptual Schema of the Human Genome in order to obtain the first version of a species-independent Conceptual Schema of the Genome. Identifying the different genome information items used in each genomic case study has been essential in achieving our goal. The changes needed to provide an expanded, more generic version of the Conceptual Schema of the Human Genome are analyzed and discussed.ResultsThis work presents a new CS called the Conceptual Schema of the Genome that is ready to be adapted to any specific working genome-based context (i.e., species-independent).ConclusionThe generated Conceptual Schema of the Genome works as a global, generic element from which conceptual views can be created in order to work with any specific species. This first working version can be used in the human use case, in the citrus use case, and, potentially, in more use cases of other species.

A Conceptual Model-Based Approach to Improve the Representation and Management of Omics Data in Precision Medicine

Precision medicine has emerged as a disrupting medical model to transform a historically reactive medicine into a proactive one that focuses on delivering individualized treatment. A relevant challenge of precision medicine is to integrate the large amount of omics data that exists. This data has a high degree of heterogeneity, dispersion, and isolation. In addition, there is a lack of a solid ontological commitment regarding domain concepts and definitions, and a unified guideline of how to transform data into knowledge is missing. In this work, we report our experience applying conceptual modeling to deal with these challenges in a specific genomics dimension, i.e., Precision Medicine. To do so, we have applied conceptual modeling techniques. The use of these techniques allows us to create representations of the world (i.e., conceptual schemes) that can be used for the purposes of understanding, communicating, and problem-solving. They also help to establish a common ontological framework to facilitate both communication and knowledge evolution in complex domains. We identify a set of limitations that emerged after working in a precision medicine context, and we describe how we have solved them using conceptual modeling. Thus, the main contribution of this work is to present the subsequent Conceptual Schema that allowed us to overcome these limitations, which provides a better representation of proteomics data and eases its integration.

KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

Modern information systems require the orchestration of ontologies, conceptual data modeling techniques, and efficient data management so as to provide a means for better informed decision-making and to keep up with new requirements in organizational needs. A major question in delivering such systems, is which components to design and put together to make up the required “knowledge to data” pipeline, as each component and process has trade-offs. In this paper, we introduce a new knowledge-to-data architecture, KnowID. It pulls together both recently proposed components and we add novel transformation rules between Enhanced Entity-Relationship (EER) and the Abstract Relational Model to complete the pipeline. KnowID's main distinctive architectural features, compared to other ontology-based data access approaches, are that runtime use can avail of the closed world assumption commonly used in information systems and of full SQL augmented with path queries.

Towards Higher Maturity for Machine Learning: A Conceptual Modelling Approach

Integrating machine learning (ML) applications into business settings presents challenges for many organizations despite rapid advances in ML technologies. There is a lack of systematic guidance in integrating ML into business applications. Conceptual modelling techniques have been used widely to analyze and enhance information systems. This paper outlines how conceptual modelling can be used to help organizations design their processes in order to fit their different needs when integrating ML into business applications. We identify the characteristics and challenges of ML as well as demonstrate how conceptual modelling can be applied to enhance ML integration processes and assist them in meeting these challenges.

Preliminary Evaluation of Interactive Search Engine Interface for Visually Impaired Users

This work designs, evaluates, and improves a proposed search engine interface for Visually Impaired VI users to efficiently perform web search activities. Our conceptual modeling technique is based on Formal Concept Analysis FCA that is used for data analysis. This approach highlights the hierarchized approach to represent the discovered concepts. It is combined with context interactive navigation in an interface which is called interactive search engine (InteractSE). This interface aims to reduce the time and effort required by the VI users to browse search results. InteractSE was evaluated by experts using Nielsen heuristics and Web Content Accessibility Guidelines WCAG 2.0 for its usability and accessibility. The analysis was carried out based on the usability problems identified and their average severity ratings. The results show that the most frequently violated heuristics from Nielsen set are consistency, documentation, and the average severity rating of all the problems is minor. The results also show that the most frequently violated WCAG 2 guidelines are distinguishable, followed by navigable and affordance. The average severity rating of all the problems found using WCAG 2 guidelines is also minor. The results show that Nielsen heuristics and WCAG 2.0 guidelines contributed to identifying several usability problems, which might have missed out if either of them was used alone.

Interactions between Analysts in Developing Collaborative Conceptual Models

Conceptual models are frequently developed as part of IS analysis and design. Development of such models involves expertise in conceptual modeling techniques, served by modeling analysts, and in the domain applications, served by domain analysts. This paper focuses on understanding how these two types of analysts interact and develop conceptual models collaboratively. The subjects as analysts were placed in pairs (one modeling and one domain) and asked to develop conceptual models collaboratively on a complex domain. The interactions during conceptual model development were analyzed using a sensemaking framework. It was found that all groups performed the reciprocal acts of modifying each other’s understanding of the concepts that were required to develop the model collaboratively. The study also indicates that the groups which had high incidence of sensebreaking acts (i.e. attempts to question the existing understanding of other) and high incidence of shared conceptualization, created higher quality conceptual models.

Improved integrated water resource modelling by combining DPSIR and system dynamics conceptual modelling techniques.

Similar to any modelling technique, system dynamics (SD) modelling should start with the essential step of scoping and identifying the problem of interest before further analysis and modelling. In practice, this first step is a challenging task, especially when wicked issues such as water management are being addressed. There is still a vital need for modelling methods and tools that can support modellers to identify and assemble essential data to inform problem scoping and boundary setting. This article aims to narrow this gap by presenting a methodology for combining a series of conceptual modelling techniques (extending the usually linear Driver-Pressure-State-Impact-Response framework with causal loop diagrams, system archetypes, stock and flow diagrams) towards the development of a quantitative SD model. A case study of the Gorganroud-Gharesu Basin, in Iran, is used to illustrate the benefits of the methodology. Our experience shows that combining multiple conceptual models provides complementary insights into the problem boundaries and model structure, as a basis for developing the SD model.

Comparing traditional conceptual modeling with ontology-driven conceptual modeling: An empirical study

This paper conducts an empirical study that explores the differences between adopting a traditional conceptual modeling (TCM) technique and an ontology-driven conceptual modeling (ODCM) technique with the objective to understand and identify in which modeling situations an ODCM technique can prove beneficial compared to a TCM technique. More specifically, we asked ourselves if there exist any meaningful differences in the resulting conceptual model and the effort spent to create such model between novice modelers trained in an ontology-driven conceptual modeling technique and novice modelers trained in a traditional conceptual modeling technique. To answer this question, we discuss previous empirical research efforts and distill these efforts into two hypotheses. Next, these hypotheses are tested in a rigorously developed experiment, where a total of 100 students from two different Universities participated. The findings of our empirical study confirm that there do exist meaningful differences between adopting the two techniques. We observed that novice modelers applying the ODCM technique arrived at higher quality models compared to novice modelers applying the TCM technique. More specifically, the results of the empirical study demonstrated that it is advantageous to apply an ODCM technique over an TCM when having to model the more challenging and advanced facets of a certain domain or scenario. Moreover, we also did not find any significant difference in effort between applying these two techniques. Finally, we specified our results in three findings that aim to clarify the obtained results.

Knowledge Management for research sustainability of academic researchers in India-An overview and theoretical framework

Objectives: Knowledge Management (KM) is an integration of numerous endeavors and fields of study. This paper provides a theoretical framework for characterizing attributes of academic researchers and techniques available to knowledge management. It provides an overview of a few key terms and concepts, describes the framework, provides examples of how to use it, and explores a variety of potential application areas the area of research. Methodology: The study is based on primary and secondary sources. It includes extensive literature review, survey-based research, interpretative structural modeling. The sample unit was post graduate colleges affiliated to deemed universities and Savitribai Phule Pune University in Pune City. The sample frame was randomly chosen. The sample extent was Pune city defined by Pune Municipal Corporation (PMC). The sample size was 30 academic researchers. Sample Element was academic researchers with a designation of professor and Associate professor. To ascertain the relationship among the various variables and propose a conceptual framework Isometric Modeling Technique(ISM) proposed by Warfield in 1973 is used. This model is further analyzed using MIC MAC analysis. Findings: The findings suggest that the term knowledge management is familiar to most of the professionals and there are positive outcomes contributing to quality research though the ways of knowing and degrees of their understanding are varied. When combined with academic research and research sustainability developing a sense of intellectual capital amongst academicians can sustain the growth of education. Application/ Improvements: The insights gained will shed light on positive outcomes of adopting knowledge management for research sustainability for academic researchers in India and the conceptual framework developed in the paper will help the policy makers and academicians for decision making. Research sustainability practices and academic research can be the key for personal and professional development of academicians. Keywords: Academic Researcher, Knowledge Management, Research Sustainability, Strategy

Modeling and Analysis of Indian Carnatic Music Using Category Theory

This paper presents a category theoretic ontology of Carnatic music. Our goals here are twofold. First, we will demonstrate the power and flexibility of conceptual modeling techniques based on a branch of mathematics called category theory (CT), using the structure of Carnatic music as an example. Second, we describe a platform for collaboration and research sharing in this area. The construction of this platform uses formal methods of CT (colimits) to merge our Carnatic ontology with a generic model of music information retrieval tasks. The latter model allows us to integrate multiple analytical methods, such as hidden Markov models, machine learning algorithms, and other data mining techniques like clustering, bagging, etc., in the analysis of a variety of different musical features. Furthermore, the framework facilitates the storage of musical performances based on the proposed ontology, making them available for additional analysis and integration. The proposed framework is extensible, allowing future work in the area of rāga recognition to build on our results, thereby facilitating collaborative research. Generally speaking, the methods presented here are intended as an exemplar for designing collaborative frameworks supporting reproducibility of computational analysis and simulation.

Business-driven data analytics: A conceptual modeling framework

The effective development of advanced data analytics solutions requires tackling challenges such as eliciting analytical requirements, designing the machine learning solution, and ensuring the alignment between analytics initiatives and business strategies, among others. The use of conceptual modeling methods and techniques is seen to be of considerable value in overcoming such challenges. This paper proposes a modeling framework (including a set of metamodels and a set of design catalogues) for requirements analysis and design of data analytics systems. It consists of three complementary modeling views: business view, analytics design view, and data preparation view. These views are linked together to connect enterprise strategies to analytics algorithms and to data preparation activities. The framework includes a set of design catalogues that codify and represent an organized body of business analytics design knowledge. As the first attempt to validate the framework, three real-world data analytics case studies are used to illustrate the expressiveness and usability of the framework. Findings suggest that the framework provides an adequate set of concepts to support the design and implementation of analytics solutions.

Iterative conceptual modeling: A case study in cardiac patient survival simulation

Iterative conceptual modeling techniques were used to abstract a computer model of emergency department patient characteristics from a medical dataset. This research effort focused on unbalanced medical data then developed and defined a simulation of cardiac patient survival. The approach focused on desired real-world outcomes for the model. Specifically, iterative conceptual modeling ensured alignment between real-world needs and model development. Model validity was enhanced and the problem of unbalanced medical data was discovered and addressed through iterative prototyping. Overall, the effort approach reduced Type II Errors and improved simulation accuracy. This research yielded tangible benefits in an emergency department cardiac patient study where each improvement in accuracy can directly impact life and death decisions.

Multi-level ontology-based conceptual modeling

Since the late 1980s, there has been a growing interest in the use of foundational ontologies to provide a sound theoretical basis for the discipline of conceptual modeling. This has led to the development of ontology-based conceptual modeling techniques whose modeling primitives reflect the conceptual categories defined in a foundational ontology. The ontology-based conceptual modeling language OntoUML, for example, incorporates the distinctions underlying the taxonomy of types in the Unified Foundational Ontology (UFO) (e.g., kinds, phases, roles, mixins, etc.). This approach has focused so far on the support to types whose instances are individuals in the subject domain, with no provision for types of types (or categories of categories). In this paper we address this limitation by extending the Unified Foundational Ontology with the MLT multi-level theory. The UFO-MLT combination serves as a foundation for conceptual models that can benefit from the ontological distinctions of UFO as well as MLT's basic concepts and patterns for multi-level modeling. We discuss the impact of the extended foundation to multi-level conceptual modeling.