Knowledge-based Engineering in Product Development Processes - Process, IT and Knowledge Management perspectives

Product development as a field of practice and research has significantly changed due to the general trends of globalization changing the enterprise landscapes in which products are realized. The access to partners and suppliers with high technological specialization has also led to an increased specialization of original equipment manufacturers (OEMs). Furthermore, the products are becoming increasingly complex with a high functional and technological content and many variants. Combined with shorter lifecycles which require reuse of technologies and solutions, this has resulted in an overall increased knowledge intensity which necessitates a more explicit approach towards knowledge and knowledge management in product development. In parallel, methods and IT tools for managing knowledge have been developed and are more accessible and usable today. One such approach is knowledge-based engineering (KBE), a term that was coined in the mid-1980s as a label for applications which automate the design of rule-driven geometries. In this thesis the term KBE embraces the capture and application of engineering knowledge to automate engineering tasks, regardless of domain of application, and the thesis aims at contributing to a wider utilization of KBE in product development (PD). The thesis focuses on two perspectives of KBE; as a process improvement IT method and as a knowledge management (KM) method. In the first perspective, the lack of explicit regard for the constraints of the product lifecycle management (PLM) architecture, which governs the interaction of processes and IT in PD, has been identified to negatively affect the utilization of KBE in PD processes. In the second perspective, KM theories and models can complement existing methods for identifying potential for KBE applications.Regarding the first perspective, it is concluded that explicit regard for the PLM architecture decreases the need to develop and maintain software code related to hard coded redundant data and functions in the KBE application. The concept of service oriented architecture (SOA) has been found to enable an the explicit regard for the PLM architecture.. Regarding the second perspective, it is concluded that potential for KBE applications is indicated by: 1.) application of certain types of knowledge in PD processes 2.) high maturity and formalization of the applied knowledge 3.) a codification strategy for KM and 4.) an agreement and transparency regarding how the knowledge is applied, captured and transferred. It is also concluded that the formulation of explicit KM strategies in PD should be guided by knowledge application and its relation to strategic objectives focusing on types of knowledge, their role in the PD process and the methods and tools for their application. These, in turn, affect the methods and tools deployed for knowledge capture in order for it to integrate with the processes of knowledge origin. Finally, roles and processes for knowledge transfer have to be transparent to assure the motivation of individuals to engage in the KM strategy

Knowledge-based Engineering in Product Development Processes - Process, IT and Knowledge Management perspectives

Product development as a field of practice and research has significantly changed due to the general trends of globalization changing the enterprise landscapes in which products are realized. The access to partners and suppliers with high technological specialization has also led to an increased specialization of original equipment manufacturers (OEMs). Furthermore, the products are becoming increasingly complex with a high functional and technological content and many variants. Combined with shorter lifecycles which require reuse of technologies and solutions, this has resulted in an overall increased knowledge intensity which necessitates a more explicit approach towards knowledge and knowledge management in product development. In parallel, methods and IT tools for managing knowledge have been developed and are more accessible and usable today. One such approach is knowledge-based engineering (KBE), a term that was coined in the mid-1980s as a label for applications which automate the design of rule-driven geometries. In this thesis the term KBE embraces the capture and application of engineering knowledge to automate engineering tasks, regardless of domain of application, and the thesis aims at contributing to a wider utilization of KBE in product development (PD). The thesis focuses on two perspectives of KBE; as a process improvement IT method and as a knowledge management (KM) method. In the first perspective, the lack of explicit regard for the constraints of the product lifecycle management (PLM) architecture, which governs the interaction of processes and IT in PD, has been identified to negatively affect the utilization of KBE in PD processes. In the second perspective, KM theories and models can complement existing methods for identifying potential for KBE applications.Regarding the first perspective, it is concluded that explicit regard for the PLM architecture decreases the need to develop and maintain software code related to hard coded redundant data and functions in the KBE application. The concept of service oriented architecture (SOA) has been found to enable an the explicit regard for the PLM architecture.. Regarding the second perspective, it is concluded that potential for KBE applications is indicated by: 1.) application of certain types of knowledge in PD processes 2.) high maturity and formalization of the applied knowledge 3.) a codification strategy for KM and 4.) an agreement and transparency regarding how the knowledge is applied, captured and transferred. It is also concluded that the formulation of explicit KM strategies in PD should be guided by knowledge application and its relation to strategic objectives focusing on types of knowledge, their role in the PD process and the methods and tools for their application. These, in turn, affect the methods and tools deployed for knowledge capture in order for it to integrate with the processes of knowledge origin. Finally, roles and processes for knowledge transfer have to be transparent to assure the motivation of individuals to engage in the KM strategy

Evolution of a Cognitive Architecture for Social Robots: Integrating Behaviors and Symbolic Knowledge

[EN] This paper presents the evolution of a robotic architecture intended for controlling autonomous social robots. The first instance of this architecture was originally designed according to behavior-based principles. The building blocks of this architecture were behaviors designed as a finite state machine and organized in an ethological inspired way. However, the need of managing explicit symbolic knowledge in human–robot interaction required the integration of planning capabilities into the architecture and a symbolic representation of the environment and the internal state of the robot. A major contribution of this paper is the description of the working memory that integrates these two approaches. This working memory has been implemented as a distributed graph. Another contribution is the use of behavior trees instead of state machine for implementing the behavior-based part of the architecture. This late version of the architecture has been tested in robotic competitions (RoboCup or European Robotics League, among others), whose performance is also discussed in this paper.SIEuropean Horizon 2020 research and innovation program under grant agreement No 732410.Ministerio de Ciencia, Innovación y Universidade

Knowledge and Metadata Integration for Warehousing Complex Data

With the ever-growing availability of so-called complex data, especially on the Web, decision-support systems such as data warehouses must store and process data that are not only numerical or symbolic. Warehousing and analyzing such data requires the joint exploitation of metadata and domain-related knowledge, which must thereby be integrated. In this paper, we survey the types of knowledge and metadata that are needed for managing complex data, discuss the issue of knowledge and metadata integration, and propose a CWM-compliant integration solution that we incorporate into an XML complex data warehousing framework we previously designed.Comment: 6th International Conference on Information Systems Technology and its Applications (ISTA 07), Kharkiv : Ukraine (2007

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Changing the Architectural Profession - Evidence-Based Design, the New Role of the User and a Process-Based Approach

The construction industry is characterised by ever-changing projects that constantly involve new clients, teams and people. This results in the need to build up new sets of relationships each time. Within these relationships the perspective of the users of space is mostly neglected, partly due to the ephemeral nature of the industry, but partly also because of the character and culture of the architectural profession. In contrast, this paper argues that the architectural profession needs to make a double turn: firstly, the needs and wishes of the user need to be in the centre of the architectural business. Secondly, the whole industry may change from a project-centred one into a process-based one where the process of finding out what the client needs, of engaging the users, proposing a design solution, managing the project, and evaluating its use and appropriation in the end in order to learn from it, is nearly as important as aesthetics, form and function. This involves a lot more intelligence and research about cultures and characteristics of the client, may it be a private person, a city council or a corporation, hence architectural and organisational research may play a new role in the architectural professional culture

Enabling Distributed Knowledge Management: Managerial and Technological Implications

In this paper we show that the typical architecture of current KM systems re.ects an objectivistic epistemology and a traditional managerial control paradigm. We argue that such an objectivistic epistemology is inconsistent with many theories on the nature of knowledge, in which subjectivity and sociality are taken as essential features of knowledge creation and sharing. We show that adopting such a new epistemological view has dramatic consequences at an architectural, managerial and technological level

MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation

An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour

A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area