OSLC‐KM: A knowledge management specification for OSLC‐based resources

Int. J. Soft. Eng. Knowl. Eng.

Mendieta

Moreno

et al. 2020

Self Cite

This paper introduces a mechanism to recover traceability links between the requirements and logical models in the context of critical systems development. Currently, lifecycle processes are covered by a good number of tools that are used to generate different types of artifacts. One of the cornerstone capabilities in the development of critical systems lies in the possibility of automatically recovery traceability links between system artifacts generated in different lifecycle stages. To do so, it is necessary to establish to what extent two or more of these work products are similar, dependent or should be explicitly linked together. However, the different types of artifacts and their internal representation depict a major challenge to unify how system artifacts are represented and, then, linked together. That is why, in this work, a concept-based representation is introduced to provide a semantic and unified description of any system artifact. Furthermore, a traceability function is defined and implemented to exploit this new semantic representation and to support the recovery of traceability links between different types of system artifacts. In order to evaluate the traceability function, a case study in the railway domain is conducted to compare the precision and recall of recovery traceability links between text-based requirements and logical model elements. As the main outcome of this work, the use of a concept-based paradigm to represent that system artifacts are demonstrated as a building block to automatically recover traceability links within the development lifecycle of critical systems.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic Recovery of Traceability Links between System Artifacts

Int. J. Soft. Eng. Knowl. Eng.

Mendieta

Moreno

et al. 2020

Self Cite

“…As a result of previous studies (Jose Maria Alvarez-Rodríguez et al 2015), an evolution of the preliminary OSLC KM (Knowledge Management) specification has been proposed (Jose María Alvarez-Rodríguez et al 2018) to define a common shape for any type of system artifact (considered a knowledge asset) that must be represented, stored, shared or exchanged between tools in a development process. On the other hand, the OSLC initiative is making a strong commitment to apply the principles of Linked Data, RDF (Resource Description Framework) and REST (Representational State Transfer) to boost toolchain interoperability.…”

Section: Oslc Knowledge Management (Km): Data and Operations Descriptionmentioning

confidence: 99%

“…an RDF vocabulary, that defines the entities and relationships in the SRL representation model to make this specification publicly available and to enable the expression of any piece of knowledge using SRL. Since a huge amount of data, services and endpoints based on RDF and the Linked Data principles are already publicly available, a mapping between any RDF vocabulary and SRL is completely necessary to support backward compatibility and to be able to import any piece of RDF data into SRL (Jose Maria Alvarez-Rodríguez et al 2015). In this case, considering the guidelines and definitions of the OSLC Core specification, the data shape for knowledge management will conform the next basic OSLC definitions: The key concepts of the SRL metamodel are the Artifact and Relationships classes.…”

Section: Oslc Knowledge Management (Km): Data and Operations Descriptionmentioning

confidence: 99%

Elevating the meaning of data and operations within the development lifecycle through an interoperable toolchain

INCOSE International Symp

Zúñiga²,

Lloréns

2019

Self Cite

The use of different engineering methods and tools is a common practice to cover all stages in the systems development lifecycle, generating a very good number of system artifacts. Moreover, these artifacts are commonly encoded in different formats and can only be accessed, in most cases, through proprietary and non‐standard protocols. In this context, the OSLC (Open Services for Lifecycle Collaboration) initiative pursues the creation of public specifications (data shapes) to exchange any artifact generated during the development lifecycle. In this paper, authors extend and apply the OSLC KM (Knowledge Management) specification as a solution with a two‐folded objective: 1) representation of any kind of system artifact and 2) extension of OSLC mechanisms to support the notion of delegated operation. In this manner, it is possible to enhance the exchange of data items and to reuse existing operations within the toolchain as it is demonstrated through a case study.

“…In this sense, the Open Services for Lifecycle Collaboration (OSLC) initiative applying the principles of Linked Data and REST and the Model-Based Systems Engineering approach are two of the main approaches to create a uniform engineering environment relying on existing standards. Approaches such as the OSLC Knowledge Management (KM) specification and repository [165] or the SysML 2.0 standard are defined under the assumption of providing standardized APIs (application programming interfaces) to access any kind of system artifact (in the case of OSLC KM) or model (in the case of SysML 2.0) meaning that the exchange of system artifacts is not anymore a single and isolated file but a kind of service. In general, engineering environments are already fueled by interconnected data shifting the paradigm of data silos to a kind of industrial knowledge graph.…”

Section: Data-intensive Engineering Environments and Scientificmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.