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Meta4CBC is proposed as an innovative metamodel for competency-based curriculum design in higher education, addressing the critical need to adapt curricula to contemporary challenges driven by globalization, technological advancement, and climate change. Inspired by the Business Motivation Model and Semantics of Business Vocabulary and Business Rules metamodels, Meta4CBC seeks to overcome the limitations of current CBCD models by providing a coherent structure that integrates curriculum components at supra, macro, meso, micro, and nano levels and establishes a common language that facilitates interdisciplinary collaboration. The proposal promotes curriculum alignment and coherence while supporting the development of software tools to assist in curriculum design, automate repetitive tasks, and provide data analysis for informed decision-making. The validation of Meta4CBC was conducted through a multi-step methodology involving proof of concept, expert judgment, and practical application. After defining the components through a systematic literature review, the metamodel was tested in the Computer Engineering program at the University of La Frontera. This testing verified its adaptability and alignment across curriculum levels. Experts from various Chilean universities reviewed the model, providing feedback for refinement. [ABSTRACT FROM AUTHOR]

The Object Management Group (OMG) has put considerable effort into the standardization of various business modeling aspects within the context of model-driven systems development. Indeed, the Business Process Model and Notation (BPMN) is now arguably the most popular process modeling language. At the same time, the Semantics of Business Vocabulary and Business Rules (SBVR), which is a novel and formally sound standard for the specification of virtually any kind of knowledge using controlled natural language, is also gaining its grounds. Nonetheless, the integration between these two very much related standards remains weak. In this paper, we present one such integration effort, namely an approach for the extraction of SBVR process rules from BPMN processes. To accomplish this, we utilized model-to-model transformation technology, which is one of the core features of Model-Driven Architecture. At the core of the presented solution stands a set of model transformation rules and two algorithms specifying the formation of formally defined process rules from process models. Basic implementation aspects, together with the source code of the solution, are also presented in the paper. The experimental results acquired from the automatic model transformation have shown full compliance with the benchmark results and cover the entirety of the specified flow of work defined in the experimental process models. Following this, it is safe to conclude that the set of specified transformation rules and algorithms was sufficient for the given scope of the experiment, providing a solid background for the practical application and future developments of the solution. [ABSTRACT FROM AUTHOR]

Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework's efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions. [ABSTRACT FROM AUTHOR]

Strategy monitoring is essential for business management and for administrators, including managers and executives, to build a data-driven organization. Having a tool that is able to visualize strategic data is significant for business intelligence. Unfortunately, there are gaps between business users and information technology departments or business intelligence experts that need to be filled to meet user requirements. For example, business users want to be self-reliant when using business intelligence systems, but they are too inexperienced to deal with the technical difficulties of the business intelligence systems. This research aims to create an automatic matching framework between the key performance indicators (KPI) formula and the data in database systems, based on ontology concepts, in the case study of Prince of Songkla University. The mapping data schema with ontology (MapDSOnto) framework is created through knowledge adaptation from the literature review and is evaluated using sample data from the case study. String similarity methods are compared to find the best fit for this framework. The research results reveal that the "fuzz.token_set_ratio" method is suitable for this study, with a 91.50 similarity score. The two main algorithms, database schema mapping and domain schema mapping, present the process of the MapDS-Onto framework using the "fuzz.token_set_ratio" method and database structure ontology to match the correct data of each factor in the KPI formula. The MapDS-Onto framework contributes to increasing self-reliance by reducing the amount of database knowledge that business users need to use semantic business intelligence. [ABSTRACT FROM AUTHOR]

Low-Code Development is a new software development paradigm which is typically used to generate web applications from high-level visual notations. These notations allow to express the User Interface and Application Logic (usersystem interactions) in a way which is understandable by the end-users. Using certain model-driven approaches, the low-code environments allow for generating the front-end layer and basic CRUD operations in the back-end. Yet still, non-standard domain logic (data processing) operations still necessitate the use of traditional programming. In this article we present a visual language, called RSL-DL that can be used to represent such non-standard domain logic in a visual form. It allows to capture domain knowledge with complex domain rules aligned with requirements models. The language synthesises and extends approaches found in knowledge representation (ontologies) and software modelling language engineering. The development environment of RSL-DL enables fully automatic generation of domain logic code by reasoning over and reusing domain knowledge. The environment includes a dedicated model editor and a transformation engine. The language's abstract syntax is defined using a meta-model expressed in MOF. Its semantics is expressed with several translational rules that map RSL-DL models onto typical programming language constructs. The article presents a list of these rules in an informal way, and then introduces their formalisation using a graphical transformation notation. The RSL-DL environment includes an inference engine that enables processing queries to domain models and selecting appropriate invocations to generated code. It was also initially validated through studies that involve understandability, operability and complexity assessment. Based on these results, we conclude that declarative knowledge representations can be successfully used to produce imperative backend code with non-trivial logic. [ABSTRACT FROM AUTHOR]

Answer set programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain user groups who may find it more advantageous to employ a higher-level language that closely resembles natural language when specifying ASP programs. In this paper, we propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules and then exemplify the usage of the CNL for the specification of both synthetic and real-world combinatorial problems. Finally, we report the results of an experimental analysis conducted on the real-world problems to compare the performance of automatically generated encodings with the ones written by ASP practitioners, showing that our tool can obtain satisfactory performance on these benchmarks. [ABSTRACT FROM AUTHOR]

To date, work on the development of assurance cases has largely been concerned with the broad structure and content of arguments to contextualise the data. However, at a more detailed level, use of natural language in an argument can lead to conflicting terminology, to difficulties in understanding the nature of the claims being made or to logical inferences which are obscure to the readers of the argument. This problem has become increasingly complex as more and more suppliers are involved in the development chain, making it more difficult to evaluate the strengths and weaknesses of assurance data or to re-use it. This paper explores the development of controlled vocabulary and structured expressions for CPS in the automotive domain, using the Semantics of Business Vocabulary and Business Rules (SBVR) to improve communication and to provide presents some formal consistency checking of content. We highlight the challenges this work has exposed. [ABSTRACT FROM AUTHOR]

Automated support to enterprise modeling has increasingly become a subject of interest for organizations seeking solutions for storage, distribution and analysis of knowledge about business processes. This interest has recently resulted in approving the standard for specifying Semantics of Business Vocabulary and Business Rules (SBVR). Despite the existence of formally grounded notations, up to now SBVR still lacks a sound and consistent logical formalization which would allow developing automated solutions able to check the consistency of a set of business rules. This work reports on the attempt to provide logical foundations for SBVR by the means of defining a specific first-order deontic-alethic logic (FODAL). The connections of FODAL with the modal logic QK and the description logic 𝒜ℒ𝒞𝒬ℐ have been investigated and, on top of the obtained theoretical results, a special tool providing automated support for consistency checks of a set of 𝒜ℒ𝒞𝒬ℐ-expressible deontic and alethic business rules has been implemented. [ABSTRACT FROM AUTHOR]

Regulations in the Building Industry are becoming increasingly complex and involve more than one technical area, covering products, components and project implementations. They also play an important role in ensuring the quality of a building, and to minimize its environmental impact. Control or conformance checking are becoming more complex every day, not only for industrials, but also for organizations charged with assessing the conformity of new products or processes. This paper will detail the approach taken by the CSTB (Centre Scientifique et Technique du Bâtiment) in order to simplify this conformance control task. The approach and the proposed solutions are based on semantic web technologies. For this purpose, we first establish a domain-ontology, which defines the main concepts involved and the relationships, including one based on OWL (Web Ontology Language) [1]. We rely on SBVR (Semantics of Business Vocabulary and Business Rules) [2] and SPARQL (SPARQL Protocol and RDF Query Language) [3] to reformulate the regulatory requirements written in natural language, respectively, in a controlled and formal language. We then structure our control process based on expert practices. Each elementary control step is defined as a SPARQL query and assembled into complex control processes "on demand", according to the component tested and its semantic definition. Finally, we represent in RDF (Resource Description Framework) [4] the association between the SBVR rules and SPARQL queries representing the same regulatory constraints. [ABSTRACT FROM AUTHOR]

Object Constraint Language (OCL) is one kind of lightweight formal specification, which is widely used for software verification and validation in NASA and Object Management Group projects. Although OCL provides a simple expressive syntax, it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first‐order logic, which is approximately half accurate at the first stage of development. A deep neural network named DeepOCL is proposed, which takes the unrestricted natural language as inputs and automatically outputs the best‐scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule‐based generation approaches. To demonstrate the validity of our proposed approach, ablation experiments were conducted on a new sentence‐aligned dataset named OCLPairs. The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation, scored 74.30 on BLEU, and greatly outperformed experienced developers by 35.19%. The proposed approach is the first deep learning approach to generate the OCL expression from the natural language. It can be further developed as a CASE tool for the software industry. [ABSTRACT FROM AUTHOR]

The development of modern intelligent systems poses numerous challenges in their strategic alignment and their effectiveness to a greater extent. Enterprise modelling emerged as a significant paradigm for holistic and systematic enterprise analysis and design. We develop a multi-view approach based on enterprise modelling and simulation that bridges the strategy execution gap between talent acquisition processes and the underlying intelligent information systems design. The approach integrates a set of techniques and tools that are rooted in the conceptual modelling paradigm. We demonstrate the value of the proposed approach in a case study, and then discuss its practical and theoretical implications. [ABSTRACT FROM AUTHOR]

Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities. [ABSTRACT FROM AUTHOR]

Semantics of Business Vocabulary and Rules (SBVR) is a standard that is applied in describing business knowledge in the form of controlled natural language. Business process designers develop SBVR from formal documents and later translate it into business process models. In many immature companies, these documents are often unavailable and could hinder resource efficiency efforts. This study introduced a novel approach called informal document to SBVR (ID2SBVR). This approach is used to extract operational rules of SBVR from informal documents. ID2SBVR mines fact type candidates using word patterns or extracting triplets (actor, action, and object) from sentences. A candidate fact type can be a complex, compound, or complex-compound sentence. ID2SBVR extracts fact types from candidate fact types and transforms them into a set of SBVR operational rules. The experimental results show that our approach can be used to generate the operational rules of SBVR from informal documents with an accuracy of 0.91. Moreover, ID2SBVR can also be used to extract fact types with an accuracy of 0.96. The unstructured data is successfully converted into semi-structured data for use in pre-processing. ID2SBVR allows the designer to automatically generate business process models from informal documents. [ABSTRACT FROM AUTHOR]

Business rules govern how well‐managed companies perform every day. They are expected to be written in natural language because they are devised by business people. That makes it difficult to translate them automatically into executable rules that can be integrated into typical information systems. Our industrial and academic research concludes that the current tools have a number of problems, namely: many of them do not pay any attention to the SBVR standard; some of them are not multi‐language; most of them cannot achieve perfect parsing accuracy; some of them are not domain agnostic; some of them use proprietary technologies; some of them do not produce executable rules; and none supports exploratory what‐if analyses natively. In this article, we present Tier‐Rules, which is a system that overcomes the previous problems. We also report on an industrial case study that helps illustrate it in practice. [ABSTRACT FROM AUTHOR]

Controlled natural languages (CNLs) have been proposed to address some of the issues of natural language when it is used to express requirements. CNLs, however, are based on formal grammar, which can easily become complex, hard to read, and especially hard to write, and the implementation of support tools can also demand a significant effort. Moreover, unanticipated constructions cannot be handled or have to be handled in unexpected and cumbersome ways. In this article, we present REX, a CNL with a simple grammar that is, thus, easy to understand and easy to support, but still general purpose. To accomplish this, instead of trying to support every conceivable construction and imposing a language on the users, through a small but comprehensive set of rules and through patterns, users specify their own language and how natural it is. Another of the benefits of CNLs is the possibility to automate the transformation of a text or specification into something useful, thereby reducing manual labor and transformation errors. In this article, we also present the support tools that were used to transform a REX text into code and a complete application. It is also shown that this CNL and its support tools can be easily adapted to suit different needs. [ABSTRACT FROM AUTHOR]

Implementing test cases as programs to automate test execution is a popular testing practice. Current industrial practices usually use test functions to implement the test steps of a test case and then to compose the executable test case by choosing the test functions to call manually. It is time-consuming and could lead to invalid test results by selecting inappropriate test functions. In this article, we propose an automatic test function recommendation approach named Scenario-based Recommendation of TEst Function (SRTEF). Given a test step of a test case, SRTEF uses the weighted description similarity and the scenario similarity to recommend test functions. The description similarity utilizes the deep structured semantic model (DSSM) to measure the relatedness between a test step and a test function by their literal descriptions. The test scenario and the test function usage scenario are considered to calculate the scenario similarity. SRTEF has been successfully applied in Huawei. The systematic experiments have been conducted to evaluate SRTEF by using the dataset from Huawei and comparing with BiInformation source-based KnowledgE Recommendation (BIKER), reported as the best approach so far. The results show that SRTEF outperforms BIKER with significant positive ratios consistently in all the three selection strategies, i.e., Top-3, Top-5, and Top-10. The DSSM shows its advantage over word embedding by the double performance of capturing the semantic relatedness in SRTEF. [ABSTRACT FROM AUTHOR]

In this paper, we present an informal introduction to Logical English (LE) and illustrate its use to standardise the legal wording of the Automatic Early Termination (AET) clauses of International Swaps and Derivatives Association (ISDA) Agreements. LE can be viewed both as an alternative to conventional legal English for expressing legal documents, and as an alternative to conventional computer languages for automating legal documents. LE is a controlled natural language (CNL), which is designed both to be computer-executable and to be readable by English speakers without special training. The basic form of LE is syntactic sugar for logic programs, in which all sentences have the same standard form, either as rules of the form conclusion if conditions or as unconditional sentences of the form conclusion. However, LE extends normal logic programming by introducing features that are present in other computer languages and other logics. These features include typed variables signalled by common nouns, and existentially quantified variables in the conclusions of sentences signalled by indefinite articles. Although LE translates naturally into a logic programming language such as Prolog or ASP, it can also serve as a neutral standard, which can be compiled into other lower-level computer languages. [ABSTRACT FROM AUTHOR]

This paper proposes a method for knowledge-based document assembly. A document assembly system is implemented as a proof of concept with service contracts as a sample document type. In comparison to related research, the method presented in this paper uses an explicit formulation of legal norms that prescribes the content and the form of service contracts to assist in the document assembly process. The assembly system also generates semantic documents, i.e., documents that preserve the meaning of the embedded data. As the primary contribution, this approach enables the automation of document processing operations and semantic interoperability of enterprise information systems. [ABSTRACT FROM AUTHOR]

Computational artificial intelligence (AI) algorithms are increasingly used to support decision making by governments. Yet algorithms often remain opaque to the decision makers and devoid of clear explanations for the decisions made. In this study, we used an experimental approach to compare decision making in three situations: humans making decisions (1) without any support of algorithms, (2) supported by business rules (BR), and (3) supported by machine learning (ML). Participants were asked to make the correct decisions given various scenarios, while BR and ML algorithms could provide correct or incorrect suggestions to the decision maker. This enabled us to evaluate whether the participants were able to understand the limitations of BR and ML. The experiment shows that algorithms help decision makers to make more correct decisions. The findings suggest that explainable AI combined with experience helps them detect incorrect suggestions made by algorithms. However, even experienced persons were not able to identify all mistakes. Ensuring the ability to understand and traceback decisions are not sufficient for avoiding making incorrect decisions. The findings imply that algorithms should be adopted with care and that selecting the appropriate algorithms for supporting decisions and training of decision makers are key factors in increasing accountability and transparency. [ABSTRACT FROM AUTHOR]

Although clinical alarm systems are part of the knowledge management setting within healthcare organisations, modelling of business processes related to decision support and knowledge representation of decision rules are seldom described. We propose a customization of the Unified Process that takes into account user requirements for clinical alarm systems by introducing the Semantics of Business Vocabulary and Business Rules (SBVR). This methodology was applied to the design and implementation of a clinical alarm system for pharmaceutical validation at the European Hospital Georges Pompidou (HEGP). Rules were implemented using the IlogJRules Business Rule Management System. We produced 3 business rules patterns and 427 instances of rules. As SBVR is close to natural language, pharmacists were able to understand rules and participate to their design. [ABSTRACT FROM AUTHOR]

A contract written in natural language describes the parties' rights and obligations. It cannot, however, 'do' anything. It produces legal not physical effects. Traditional contracts do not instruct computers to perform any operations. Contracts expressed in code could. The question arises: can we convert contractual documents from being passive embodiments evidencing agreements into active files containing computer instructions? The ability to guarantee that contracts are performed as agreed, an idea underlying 'smart contracts,' depends on the ability to express obligations in code or, more specifically, on the ability of code to express obligations exactly as agreed. Not every obligation can, however, be encoded - be it due to its very nature or to the limited expressiveness of programming languages. Highlighting the interplay between form and substance, this paper presents the complexities involved in expressing contracts in code. Before contemplating the technical aspects of 'smart contracts' we must confront some preliminary questions: what are we trying to encode – and for what purpose? Can the entire contract be expressed in code? Should we focus on translating legal prose into code or on encoding specific tasks that serve to discharge obligations? This paper provides the groundwork for more ambitious projects in the area of 'smart contracts.' [ABSTRACT FROM AUTHOR]

Declarative process modelling languages focus on describing a process by restrictions over the behaviour, which must be satisfied throughout the whole process execution. Hence, they are well suited for modelling knowledge-intensive processes with many decision points. However, such models can be hard to read and understand, which affect the modelling and maintenance of the process models tremendously as well as their execution. When executing such declarative (multi-perspective) process models, it may happen that the execution of activities or the change of data values may result in the non-executability of crucial activities. Hence, it would be beneficial to know all consequences of decisions to give recommendations to the process participants. A look-ahead attempts to predict the effects of executing an activity towards possible consequences within an a priori defined time window. The prediction is based on the current state of the process execution, the intended next event and the underlying process model. While execution engines for single-perspective imperative process models already implement such functionality, execution approaches, for multi-perspective declarative process models that involve constraints on data and resources, are less mature. In this paper, we introduce a simulation-based look-ahead approach for multi-perspective declarative process models. This approach transforms the problem of a context-aware process simulation into a SAT problem, by translating a declarative multi-perspective process model and the current state of a process execution into a specification of the logic language Alloy. Via a SAT solver, process trajectories are generated that either satisfy or violate this specification. The simulated process trajectories are used to derive consequences and effects of certain decisions at any time of process execution. We evaluate our approach by means of three examples and give some advice for further optimizations. [ABSTRACT FROM AUTHOR]

The perpetual need for developing a sustainable economic environment places the education policies at the foundation of social adaptability. Creating and maintaining curriculum content that meets the demands of a continuously changing society, and the challenges that such a rapid evolution put on the labour market, is one of the top priorities for any education system and institution involved in education at any level. This paper proposes a cognitive computing solution for assessing, in a programmatic manner, large corpora of curriculum content created by teachers from lower secondary education environment for Informatics instruction in Romanian schools. The result of this initiative at the national level is corpora of curricular content that must be evaluated to verify the degree to which the material meets the requirements of the national curriculum. We addressed this crucial yet tedious process by designing and implementing a solution for automating curriculum assessment through cognitive computing. The paper outlines a sustainable framework to evaluate curriculum content in an automated fashion, and for providing critical feedback timely to both content creators, and to policy makers responsible for creating economically viable and future adaptable education strategies. [ABSTRACT FROM AUTHOR]

Business process modelling languages typically enable the representation of business process models by employing (graphical) symbols. These symbols can vary depending upon the verbosity of the language, the modelling paradigm, the focus of the language and so on. To make explicit different constructs and rules employed by a specific language, as well as bridge the gap across different languages, meta-models have been proposed in the literature. These meta-models are a crucial source of knowledge on what state-of-the-art literature considers relevant to describe business processes. The goal of this work is to provide the first extensive systematic literature review (SLR) of business process meta-models. This SLR aims to answer research questions concerning: (1) the kind of meta-models proposed in the literature, (2) the recurring constructs they contain, (3) their purposes and (4) their evaluations. The SRL was performed manually considering papers automatically retrieved from reference paper repositories as well as proceedings of the main conferences in the Business Process Management research area. Sixty-five papers were selected and evaluated against four research questions. The results indicate the existence of a reasonable body of work conducted in this specific area, but not a full maturity. In particular, in answering the research questions several challenges have (re-)emerged for the Business Process Community, concerning: (1) the type of elements that constitute a Business Process and their meaning, (2) the absence of a (or several) reference meta-model(s) for the community, (3) the purpose for which meta-models are introduced in the literature and (4) a framework for the evaluation of the meta-models themselves. Moreover, the classification framework devised to answer the four research questions can provide a reference structure for future descriptive categorizations. [ABSTRACT FROM AUTHOR]

To support decision making, organizations tend to operate according to Business Rules, which are usually represented in a natural language format easily understood by all intervenors. According to the business rules manifesto by the Business Rules Group (OMG), rules build on facts, and facts build on concepts as expressed by terms. To avoid ambiguity and misunderstanding, the standardization of the terminology used at the business level becomes a persistent need. However, doing so manually is error prone and time consuming, especially that the Business Rules are the subject of continuous updating. In this paper, we present an automated approach to generate the Business Vocabulary from textual statements of Business Rules. Our approach is distinguished from existing works in that it extracts the Terminological Dictionary as described by the Semantic of Business Vocabulary and Rules (SBVR) standard to provide a more comprehensive meaning for each concept. Accordingly, an in‐depth Natural Language Processing (NLP) is used to extract not only flat list of terms and relations, but also extra specifications and implicit knowledge. With a satisfactory result, our approach has proved its capability to automatically generate the SBVR Terminological Dictionary from large number of natural language business rules statements. [ABSTRACT FROM AUTHOR]

Natural Language (NL) is the root cause of ambiguity in the SRS document. The quality of the software development process can be improved by mitigating the risk with the use of semantically controlled representation. A possible solution to handle ambiguity can be the use of a mathematical formal logic representation in place of NL to capture software requirements. However, the use of formal logic is a complex task. A wrongly written formal logic will be difficult to handle and it will create serious problems in later stages of software development. Furthermore, stakeholders are typically not able to understand mathematical logic. Hence, this solution does not look feasible. Another possible way of addressing above discussed ambiguity problem is the use of controlled natural languages (CNL). It can work as a bridge between NL and formal representation. Since Requirement Analysis is based on communication and the analyst’s experience, it can be modeled up to a certain limit. This limit gives birth to controlled language. If the document is written in a controlled language, it will be feasible for the development team to use a simpler and less costly linguistic tool. The CNLs are syntactically unambiguous, semantically consistent and, controlled. Several CNLs could be found in literature such as ACE, PENG, CPL, Formalized-English, and Semantics of Business Vocabulary and Rules (SBVR), etc. We aim to use an SBVR based CNL to capture stakeholder’s requirements and prepare an SRS document using SBVR. Such software requirements will not only be syntactically clear but also semantically consistent. [ABSTRACT FROM AUTHOR]

The article examines the use of regulatory technology (Regtech) in financial technology regulation (FinReg). Topics discussed include the possible impact of the growth of large technology companies (BigTech) and their movement into the financial sector, significance of the technological support initiative Project Innovation in Great Britain and the use of Regulatory Sandboxes, and potential of RegTech 3.0 to promote a new relationship between law and technology and code and regulation.

Generally, building information modelling (BIM) models contain multiple dimensions of building information, including building design data, construction information, and maintenance-related contents, which are related with different engineering stakeholders. Efficient extraction of BIM data is a necessary and vital step for various data analyses and applications, especially in large-scale BIM projects. In order to extract BIM data, multiple query languages have been developed. However, the use of these query languages for data extraction usually requires that engineers have good programming skills, flexibly master query language(s), and fully understand the Industry Foundation Classes (IFC) express schema or the ontology expression of the IFC schema (ifcOWL). These limitations have virtually increased the difficulties of using query language(s) and raised the requirements on engineers' essential knowledge reserves in data extraction. In this paper, we develop a simple method for automatic SPARQL (SPARQL Protocol and RDF Query Language) query generation to implement effective data extraction. Based on the users' data requirements, we match users' requirements with ifcOWL ontology concepts or instances, search the connected relationships among query keywords based on semantic BIM data, and generate the user-desired SPARQL query. We demonstrate through several case studies that our approach is effective and the generated SPARQL queries are accurate. [ABSTRACT FROM AUTHOR]

Featured Application: The main application of the developed solution lies in the area of systems design, where model-to-model transformations are being utilized to their full extent. The presented extension enhances the previously developed solution with certain natural language processing capabilities delivering more features and flexibility. The current version of the complete solution can be applied to model transformations based on Unified Modeling Language (UML) or any other modeling language implemented as a UML profile. Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to this purpose by providing a flexible, reusable, customizable, and relatively easy-to-use transformation method and tool support. The solution uses model-based transformation specifications triggered by user-initiated drag-and-drop actions within the model deployed in a computer-aided software engineering (CASE) tool environment. The transformations are called partial M2M transformations, meaning that a specific user-defined fragment of the source model is being transformed into a specific fragment of the target model and not running the whole model-level transformation. In this paper, in particular, we present the main aspects of the developed extension to that M2M transformation method, delivering a set of natural language processing (NLP) techniques on both the conceptual and implementation level. The paper addresses relevant developments and topics in the field of natural language processing and presents a set of operators that can be used to satisfy the needs of advanced textual preprocessing in the scope of M2M transformations. Also in this paper, we describe the extensions to the previous M2M transformation metamodel necessary for enabling the solution's NLP-related capabilities. The usability and actual benefits of the proposed extension are introduced by presenting a set of specific partial M2M transformation use cases where natural language processing provides actual solutions to previously unsolvable situations when using the previous M2M transformation development. [ABSTRACT FROM AUTHOR]

Personal data is increasingly positioned as a valuable asset. While individuals generate and expose ever-expanding volumes of personal information online, certain tech companies have built their business models on the personal data they gather. In this context, lawmakers are revising data protection regulations in order to provide individuals with enhanced rights and set new rules regarding the way corporations collect, manage, and share personal information. We argue that recent data protection regulatory frameworks such as the European Union's General Data Protection Regulation (gdpr) or the California Consumer Privacy Act (ccpa) are fundamentally about data management. Yet, there have been no attempts to analyze the regulations in terms of their implications on the data life cycle. In this paper, we systematically analyze the gdpr and the ccpa, and identify their implications on the data life cycle. To synthesize our findings, we propose a semi-formal notation of the resulting changes on the personal data life cycle, in the form of a process and data model governed by business rules, consolidated in a reference personal data life cycle model for data protection. To the best of our knowledge, this study represents one of the first attempts to provide a data-centric view on data protection regulatory requirements. [ABSTRACT FROM AUTHOR]

The article outlines the background to the changes in financial markets and growth of big technology (BigTech), data technology (DataTech) and stablecoin technology (StableTech). It discusses the nature, structure, and operation of Facebook's Libra digital coin with specific reference to Libra, the Libra Foundation, Novi digital wallet, Libra reserve pool, and early regulatory reaction. The core legal and regulatory issues, along with technical difficulties, are explored.