How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Problems and Strategies in Services Marketing

Semantic interoperability in the Internet of Things: An overview from the INTER-IoT perspective

Business Process Management (BPM) includes methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. It can be considered as an extension of classical Workflow Management (WFM) systems and approaches. Although the practical relevance of BPM is undisputed, a clear definition of BPM and related acronyms such as BAM, BPA, and STP are missing. Moreover, a clear scientific foundation is missing. In this paper, we try to demystify the acronyms in this domain, describe the state-of-the-art technology, and argue that BPM could benefit from formal methods/languages (cf. Petri nets, process algebras, etc.).

Business Process Management: A Survey

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM),A semi-systematic approach has been used in this review It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate,The review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together,Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations

A review of machine learning applications in human resource management

Automatically calculated metrics are needed in order to evaluate ontology quality. Otherwise, major resources are required in order to manually assess certain Quality Criteria of an ontology. While there is rule based support for the detection modelling errors and the violation of ontology modelling guidelines, there is a lack of support for calculating ontology metrics. However, metrics can serve as indicators for possible quality problems that are not covered rulebased ontology evaluation. Many metrics have been proposed that correlate for example with ontology characteristics like Readability, Adaptability, and Reusability. However, there is a lack of tool support. OntoMetrics provides free access to metric definition and calculation. Furthermore it fosters the development of knowledge regarding the application of Ontology Metrics. This paper provides theoretical background and usage scenarios for the OntoMetrics on-line platform.

OntoMetrics: Putting Metrics into Use for Ontology Evaluation

The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. The digitization of software-intensive products and services is enabled basically by four megatrends: Cloud Computing, Big Data Mobile Systems, and Social Technologies. This disruptive change interacts with all information processes and systems that are important business enablers for the current digital transformation. The Internet of Things, Social Collaboration Systems for Adaptive Case Management, Mobility Systems and Services for Big Data in Cloud Services environments are emerging to support intelligent user-centered and social community systems. Modern enterprises see themselves confronted with an ever growing design space to engineer business models of the future as well as their IT support, respectively. The decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures (EA), is duly needed. With the advent of intelligent user-centered and social community systems, the challenging decision processes can be supported in more flexible and intuitive ways. Tapping into these systems and techniques, the engineers and managers of the enterprise architecture become part of a viable enterprise, i.e. a resilient and continuously evolving system that develops innovative business models.

Leveraging Analytics for Digital Transformation of Enterprise Services and Architectures

A lot of work has been done regarding ontology evaluation in the recent years. Automatically calculated indicators are needed in order to assess ontology quality. Manual evaluation of ontologies would be very time consuming. While there is tool support for the detection modelling errors and the violation of ontology modelling guidelines, there is a lack of support for calculating ontology metrics. Many metrics have been proposed that correlate for example with ontology characteristics like Readability, Adaptability, and Reusability. However, no tools have been created or tools are no longer maintained and bound to certain ontology editors. OntoMetrics fills this gap by providing an on-line platform for ontology metric calculation.This paper presents the current status of OntoMetrics, use cases and planned future developments.

/pdf/ontometrics-application-of-on-line-ontology-metric-1zxji2609z.pdf

OntoMetrics: Application of On-line Ontology Metric Calculation.

The SPARQL query language has been proposed as a simple language for querying graph-structured data on the Semantic Web. However, users have to write queries that must conform to the SPARQL syntax. ...

/pdf/sparql-query-builders-overview-and-comparison-5iiybiqs5y.pdf

Birger Lantow

Papers

OntoMetrics: Putting Metrics into Use for Ontology Evaluation

Leveraging Analytics for Digital Transformation of Enterprise Services and Architectures

OntoMetrics: Application of On-line Ontology Metric Calculation.

SPARQL Query Builders: Overview and Comparison.

Towards Adaptive Business Models for Intelligent Information Logistics in Transportation