Managing human resources in Asia-Pacific

This book presents an HRM scenario in a number of South-East Asian and Pacific Rim countries. It highlights the growth of the personnel/HR function, the dominant HRM system(s) in the area, the influence of different factors on HRM, and the challenges faced by HR functions in these nations. An excellent addition to this subject area, each chapter has been written by an area specialist. As the most topical and up-to-date book in its field, this outstanding book is suitable for both academics and practitioners in the field.

Managing human resources in the Middle-East

Managing Human Resources in the Middle East provides the reader with an understanding of the dynamics of HRM in this important region. Systematic analysis highlights the main factors and variables dictating HRM policies and practices within each country. Diverse and unique cultural, institutional and business environment factors which play a significant role in determining HRM systems in the region are also elaborated upon. The text moves from a general overview of HRM in the Middle-East to an exploration of the current status, role and strategic importance of the HR function in a wide-range of country-specific chapters, before highlighting the emerging HRM models and future challenges for research, policy and practice. This text is invaluable reading for academics, students and practitioners alike.

Managing human resources in Asia-Pacific

Given the enormous economic and developmental changes being experienced by nations in the Asia-Pacific region, and the related movement of people between and across countries, it is critical that we better understand the HRM policies and practices of these nations. The latest instalment in the Global HRM series, Managing Human Resources in Asia-Pacific (2E) presents the HRM situations in a number of South-East Asian and Pacific Rim countries, highlighting the growth of the personnel and HR function, the dominant HRM system(s) in the area, the influence of different factors on HRM, and the challenges faced by HR functions in these nations. This edition extends its coverage to Cambodia, Fiji, Indonesia, and the Philippines; a new chapter discusses HR research challenges in the region, such as the transferability of western constructs, problems with data collection, and the emergence of MNEs from Asia Pacific. © 2014 Taylor & Francis.

Probabilistic resource space model for managing resources in cyber-physical society

Classification is the most basic method for organizing resources in the physical space, cyber space, socio space and mental space. To create a unified model that can effectively manage resources in different spaces is a challenge. The Resource Space Model RSM is to manage versatile resources with a multi-dimensional classification space. It supports generalization and specialization on multi-dimensional classifications. This paper introduces the basic concepts of RSM, and proposes the Probabilistic Resource Space Model, P-RSM, to deal with uncertainty in managing various resources in different spaces of the cyber-physical society. P-RSM’s normal forms, operations and integrity constraints are developed to support effective management of the resource space. Characteristics of the P-RSM are analyzed through experiments. This model also enables various services to be described, discovered and composed from multiple dimensions and abstraction levels with normal form and integrity guarantees. Some extensions and applications of the P-RSM are introduced.

Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

An incremental tri-partite approach to ontology learning

In this paper we present a new approach to ontology learning. Its basis lies in a dynamic and iterative view of knowledge acquisition for ontologies. The Abraxas approach is founded on three resources, a set of texts, a set of learning patterns and a set of ontological triples, each of which must remain in equilibrium. As events occur which disturb this equilibrium various actions are triggered to re-establish a balance between the resources. Such events include acquisition of a further text from external resources such as the Web or the addition of ontological triples to the ontology. We develop the concept of a knowledge gap between the coverage of an ontology and the corpus of texts as a measure triggering actions. We present an overview of the algorithm and its functionalities.