Access control and quality attributes of open data: Applications and techniques

Open Datasets provide one of the most popular ways to acquire insight and information about individuals, organizations and multiple streams of knowledge. Exploring Open Datasets by applying comprehensive and rigorous techniques for data processing can provide the ground for innovation and value for everyone if the data are handled in a legal and controlled way. In our study, we propose an argumentation and abductive reasoning approach for data processing which is based on the data quality background. Explicitly, we draw on the literature of data management and quality for the attributes of the data, and we extend this background through the development of our techniques. Our aim is to provide herein a brief overview of the data quality aspects, as well as indicative applications and examples of our approach. Our overall objective is to bring serious intent and propose a structured way for access control and processing of open data with a focus on the data quality aspects

Geometrical Variations Management 4.0: towards next Generation Geometry Assurance

Product realization processes are undergoing radical change considering the increasing digitalization of manufacturing fostered by cyber-physical production systems, the internet of things, big data, cloud computing, and the advancing use of digital twins. These trends are subsumed under the term “industry 4.0” describing the vision of a digitally connected manufacturing environment.The contribution gives an overview of future challenges and potentials for next generation geometry assurance and geometrical variations management in the context of industry 4.0. Particularly, the focus is set on potentials and risks of increasingly available manufacturing data and the use of digital twins in geometrical variations management

La gestión logística y la calidad de servicio de la empresa agua de mesa Shalom’s, Chimbote 2019

La presente investigación tuvo como objetivo general describir la gestión logística y la calidad de servicio de la empresa agua de mesa Shalom’s, Chimbote 2019. La confección de este trabajo se basó principalmente en la teoría de Cano et al. (2015) correspondiente a la variable gestión logística y la teoría de Parasuraman, Zeithaml & Berry (1988) sobre la variable calidad de servicio. El tipo de investigación fue un enfoque cuantitativo con un diseño no experimental – transversal; las poblaciones estuvieron compuestas por 10 trabajadores operativos y 1,150 clientes según la base de datos del año 2019 y en este caso la muestra solo se aplicó a los clientes generando como tamaño muestral un total de 288 clientes. El tipo de instrumento utilizado fue el cuestionario y como técnica la encuesta, la cual estuvo conformada por 15 preguntas referentes a la gestión logística y 17 preguntas con respecto a la calidad de servicio, ambos cuestionarios estuvieron acorde a una escala de Likert. Los resultados más importantes fueron que el 54% de los trabajadores operativos indicó que la gestión logística tiene un nivel malo, el 50% opinó que la distribución por parte de la empresa es malo; mientras que el 52% de los clientes señaló que la calidad de servicio tiene un nivel bueno, el 57% respondió que la fiabilidad tiene un nivel malo y el 58% mencionó que la seguridad es buena. Finalmente, se concluyó que la empresa agua de mesa Shalom’s debe llevar a cabo la propuesta planteada con el propósito de que solucione sus problemas dentro de su gestión logística y la calidad de servicio que entrega a sus clientes

Ontology based semantic engineering framework and tool for reconfigurable automation systems integration

Digital factory modelling based on virtual design and simulation is now emerging as a part of mainstream engineering activities, and it is typically geared towards reducing the product design cycle time. Reconfigurable manufacturing systems can benefit from reusing the existing knowledge in order to decrease the required skills and design time to launch new product generations. The various industrial simulation systems are currently integrating product design, matching processes and resource requirements to decrease the required skills and design time to launch new products. However, the main focus of current reconfigurable manufacturing systems has been modular production lines to support different manufacturing tasks. Additionally, the design data is not transferrable from various domain-specific software to a collaborative and intelligent platform, which is required to capture and reuse design knowledge. Product design is still dependent on the knowledge of designers and does not link to the existing knowledge on processes and resources, which are in separate domains. To address these issues, this research developed an integration method based on semantic technologies and product, process, resource and requirements (PPRR) ontologies called semantic-ontology engineering framework (SOEF). SOEF transferred original databases to an ontology-based automation data structure with a semantic analysis engine. A pre-defined semantic model is developed to recognise custom requirement and map existing knowledge with processing data in the automation assembly aspect. The main research contribution is using semantic technology to process automation documentation and map semantic data to the PPRR ontology structure. Furthermore, this research also contributes to the automatic modification of system simulation based on custom requirements. The SOEF uses a JAVA-based command-line user interface to present semantic analysis results and import ontology outputs to the vueOne system simulation tool for system evaluation

An Argumentation Reasoning Approach for Data Processing

Data-intensive environments enable us to capture information and knowledge about the physical surroundings, to optimise our resources, enjoy personalised services and gain unprecedented insights into our lives. However, to obtain these endeavours extracted from the data, this data should be generated, collected and the insight should be exploited. Following an argumentation reasoning approach for data processing and building on the theoretical background of data management, we highlight the importance of data sharing agreements (DSAs) and quality attributes for the proposed data processing mechanism. The proposed approach is taking into account the DSAs and usage policies as well as the quality attributes of the data, which were previously neglected compared to existing methods in the data processing and management field. Previous research provided techniques towards this direction; however, a more intensive research approach for processing techniques should be introduced for the future to enhance the value creation from the data and new strategies should be formed around this data generated daily from various devices and sources