Graphical Notation for Document Database Modeling

Goals and objectives. Graphical models have proven to be a reliable, clear and convenient tool for creating sketch models of databases. Most of the existing notations are designed for the relational data model, the dominant data model for the last thirty years. However, the development of information technologies has led to an increase in the popularity of non-relational data models, primarily the document model. One of the problems of its application in practice is the lack of suitable tools that allow performing graphical modeling of the database, taking into account the features of the document model, at the stage of logical design. The development of appropriate tools is an important and actual task, since their application in practical research makes it possible to identify, classify and analyze typical modeling errors that allow the designer to reduce the risk of their occurrence in the future. The purpose of this article is to develop a graphical notation that, on the one hand, providing convenience for the designer, and on the other hand, taking into account the peculiarities of creating and functioning of the noSQL document storage model.Materials and methods. The materials for the study were numerous publications devoted to the development of graphical notations in problems and their application to database design for various information systems. The selected materials were analyzed and the main graphical notations used to describe the relational data model were identified. Three notations were selected from them, a set of graphic stereotypes, which were most different from each other, the analysis of which allowed us to identify the main image patterns of the components of the relational model.The resulting patterns were applied to the main elements of the document database, which were obtained by analyzing the documentation of the popular MongoDB DBMS.Results. The result of the research was the creation of a new tool for modeling document databases at the logical level, which consists of a set of graphic stereotypes and rules for their application. On the one hand, the development is well known to practitioners who have previously worked with relational data models, since its development took into account many years of experience in using graphical models in the field of relational database design, and on the other hand, it reflects the features of the structure of the document model.Conclusion. The practical application of the developed model has shown the convenience of its use both in the process of designing document databases and in the process of teaching students within this subject area. The use of graphical models constructed in the proposed graphical notation will allow researchers to create and illustrate typical patterns of document databases, which will undoubtedly have a positive impact on the dynamics of the development of promising data storage technologies.

A domain specific language notation for a language learning activity generation tool

Globalization has increased the need for society to master new languages. This need has encouraged the launch of many applications dedicated to language learning. This paper presents a graphical notation for a domain specific language to represent language learning activities. It describes how this notation enables developers to represent language learning activity characteristics using workflow, presentation, content, media and activity model conforming a metamodel that defines the abstract syntax of the domain specific language. This notation is implemented as part of an integrated development environment to build model-based applications. Finally, this proposal is evaluated with a framework that uses the cognitive dimensions of notations for notational systems. The proposed graphic diagram editor exceeds the experience that the user has with the reflexive model editor. In relation to the creation and editing of workflow models and presentation/activity models, the proposed graphical notation its more intuitive and easy to maintain visually than the traditional reflexive tree notation used by many model-based development frameworks.

Cognition of Graphical Notation for Processing Data in ERDAS IMAGINE

This article presents an evaluation of the ERDAS IMAGINE Spatial Model Editor from the perspective of effective cognition. Workflow models designed in Spatial Model Editor are used for the automatic processing of remote sensing data. The process steps are designed as a chain of operations in the workflow model. The functionalities of the Spatial Model Editor and the visual vocabulary are both important for users. The cognitive quality of the visual vocabulary increases the comprehension of workflows during creation and utilization. The visual vocabulary influences the user’s exploitation of workflow models. The complex Physics of Notations theory was applied to the visual vocabulary on ERDAS IMAGINE Spatial Model Editor. The results were supplemented and verified using the eye-tracking method. The evaluation of user gaze and the movement of the eyes above workflow models brought real insight into the user’s cognition of the model. The main findings are that ERDAS Spatial Model Editor mostly fulfils the requirements for effective cognition of visual vocabulary. Namely, the semantic transparency and dual coding of symbols are very high, according to the Physics of Notations theory. The semantic transparency and perceptual discriminability of the symbols are verified through eye-tracking. The eye-tracking results show that the curved connector lines adversely affect the velocity of reading and produce errors. The application of the Physics of Notations theory and the eye-tracking method provides a useful evaluation of graphical notation as well as recommendations for the user design of workflow models in their practice.

An analysis of Existential Graphs–part 2: Beta

This paper provides an analysis of the notational difference between Beta Existential Graphs, the graphical notation for quantificational logic invented by Charles S. Peirce at the end of the 19th century, and the ordinary notation of first-order logic. Peirce thought his graphs to be “more diagrammatic” than equivalently expressive languages (including his own algebras) for quantificational logic. The reason of this, he claimed, is that less room is afforded in Existential Graphs than in equivalently expressive languages for different ways of representing the same fact. The reason of this, in turn, is that Existential Graphs are a non-linear, occurrence-referential notation. As a non-linear notation, each graph corresponds to a class of logically equivalent but syntactically distinct sentences of the ordinary notation of first-order logic that are obtained by permuting those elements (sentential variables, predicate expressions, and quantifiers) that in the graphs lie in the same area. As an occurrence-referential notation, each Beta graph corresponds to a class of logically equivalent but syntactically distinct sentences of the ordinary notation of first-order logic in which the identity of reference of two or more variables is asserted. In brief, Peirce’s graphs are more diagrammatic than the linear, type-referential notation of first-order logic because the function that translates the latter to the graphs does not define isomorphism between the two notations.

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

Evolution of the Business Model: Arriving at Open Business Model Dynamics

The business is an abstraction of the way in which value is created and delivered. The concrete representation is the business model, expressed by a group of artifacts built with different languages. It serves to describe, explain, analyze, design, and evaluate the business. The set of concepts, construction rules, artifacts, and languages required to express it, are defined by a Meta-Business Model (MBM). Multiple authors have proposed different MBMs, each one with a specific motivation and objective. Some of these MBMs are widely recognized and have been applied in contexts like innovation and entrepreneurship. Due to new challenges, such as sustainability, being faced by businesses and given new ways of producing and delivering value, like the sharing economy, Novel Complex Businesses (NCBs) are emerging. NCBs are businesses characterized by circular structures made out of numerous inter-related components, and by creating value out of the product/service schema. While existing MBMs fulfill certain purposes, they do not have the expressiveness required to describe NCBs precisely enough to describe and analyze them. This paper introduces an MBM with the concepts, construction rules, and graphical notation needed to represent NCBs. We also illustrate an NCB and present the results of the validation for our MBM.

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the BricKs Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

Systems biology graphical notation markup language (SBGNML) version 0.3

This document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to foster efficient and accurate representation, visualization, storage, exchange, and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn.org. With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.