Semantic modeling method for configurable enterprise information systems

Business practices vary from one company to another and business practices often need to be changed due to changes of business environments. To satisfy different business practices, enterprise systems need to be customized. To keep up with ongoing business practice changes, enterprise systems need to be adapted. Because of rigidity and complexity, the customization and adaption of enterprise systems often takes excessive time with potential failures and budget shortfall. Moreover, enterprise systems often drag business behind because they cannot be rapidly adapted to support business practice changes. Extensive literature has addressed this issue by identifying success or failure factors, implementation approaches, and project management strategies. Those efforts were aimed at learning lessons from post implementation experiences to help future projects. This research looks into this issue from a different angle. It attempts to address this issue by delivering a systematic method for developing flexible enterprise systems which can be easily tailored for different business practices or rapidly adapted when business practices change. First, this research examines the role of system models in the context of enterprise system development; and the relationship of system models with software programs in the contexts of computer aided software engineering (CASE), model driven architecture (MDA) and workflow management system (WfMS). Then, by applying the analogical reasoning method, this research initiates a concept of model driven enterprise systems. The novelty of model driven enterprise systems is that it extracts system models from software programs and makes system models able to stay independent of software programs. In the paradigm of model driven enterprise systems, system models act as instructors to guide and control the behavior of software programs. Software programs function by interpreting instructions in system models. This mechanism exposes the opportunity to tailor such a system by changing system models. To make this true, system models should be represented in a language which can be easily understood by human beings and can also be effectively interpreted by computers. In this research, various semantic representations are investigated to support model driven enterprise systems. The significance of this research is 1) the transplantation of the successful structure for flexibility in modern machines and WfMS to enterprise systems; and 2) the advancement of MDA by extending the role of system models from guiding system development to controlling system behaviors. This research contributes to the area relevant to enterprise systems from three perspectives: 1) a new paradigm of enterprise systems, in which enterprise systems consist of two essential elements: system models and software programs. These two elements are loosely coupled and can exist independently; 2) semantic representations, which can effectively represent business entities, entity relationships, business logic and information processing logic in a semantic manner. Semantic representations are the key enabling techniques of model driven enterprise systems; and 3) a brand new role of system models; traditionally the role of system models is to guide developers to write system source code. This research promotes the role of system models to control the behaviors of enterprise.

The Long-Term Irrelevance of Trust in Outsourcing Decisions for New Technologies: Insights from an Agent-Based Model

Significant empirical data from the fields of management and business strategy suggest that it is a good idea for a company to make in-house the components and processes underpinning a new technology. Other evidence suggests exactly the opposite, saying that firms would be better off buying components and processes from outside suppliers. One possible explanation for this lack of convergence is that earlier research in this area has overlooked two important aspects of the problem: reputation and trust. To gain insight into how these variables may impact make-buy decisions throughout the innovation process, the Sporas algorithm for measuring reputation was added to an existing agent-based model of how firms interact with each other throughout the development of new technologies. The model�s results suggest that reputation and trust do not play a significant role in the long-term fortunes of an individual firm as it contends with technological change in the marketplace. Accordingly, this model serves as a cue for management researchers to investigate more thoroughly the temporal limitations and contingencies that determine how the trust between firms may affect the R&D process.

The use of semantic priming as a paradigm for the detection of faking on self-report psychological tests

It is recognised that individuals do not always respond honestly when completing psychological tests. One of the foremost issues for research in this area is the inability to detect individuals attempting to fake. While a number of strategies have been identified in faking, a commonality of these strategies is the latent role of long term memory. Seven studies were conducted in order to examine whether it is possible to detect the activation of faking related cognitions using a lexical decision task. Study 1 found that engagement with experiential processing styles predicted the ability to fake successfully, confirming the role of associative processing styles in faking. After identifying appropriate stimuli for the lexical decision task (Studies 2A and 2B), Studies 3 to 5 examined whether a cognitive state of faking could be primed and subsequently identified, using a lexical decision task. Throughout the course of these studies, the experimental methodology was increasingly refined in an attempt to successfully identify the relevant priming mechanisms. The results were consistent and robust throughout the three priming studies: faking good on a personality test primed positive faking related words in the lexical decision tasks. Faking bad, however, did not result in reliable priming of negative faking related cognitions. To more completely address potential issues with the stimuli and the possible role of affective priming, two additional studies were conducted. Studies 6A and 6B revealed that negative faking related words were more arousing than positive faking related words, and that positive faking related words were more abstract than negative faking related words and neutral words. Study 7 examined whether the priming effects evident in the lexical decision tasks occurred as a result of an unintentional mood induction while faking the psychological tests. Results were equivocal in this regard. This program of research aligned the fields of psychological assessment and cognition to inform the preliminary development and validation of a new tool to detect faking. Consequently, an implicit technique to identify attempts to fake good on a psychological test has been identified, using long established and robust cognitive theories in a novel and innovative way. This approach represents a new paradigm for the detection of individuals responding strategically to psychological testing. With continuing development and validation, this technique may have immense utility in the field of psychological assessment.

Borders, mobility and technologies of control

Territorial borders are taking on a new significance, the implications of which are relatively unexplored within the discipline of criminology. This book presents the first systematic attempt to develop a critical criminology of the border and offers a unique treatment of the impact of globalisation and mobility. It focuses on borders and the significance of the activities which take place on and around them. For many the border is an everyday reality, a space in which to live, a land necessary to cross. For states the border space increasingly requires protection and defence; is at the centre of state ideology and performance; is the site for investing significant political and material resources, and is ultimately ungovernable. Providing a wealth of case material from Australia, Europe and North America, it is for students, academics, and practitioners working in the areas of criminology, migration, human geography, international law and politics, globalisation, sociology and cultural anthropology.

Constructing tag ontology from folksonomy based on WordNet

With the emergence of Web 2.0, Web users can classify Web items of their interest by using tags. Tags reflect users’ understanding to the items collected in each tag. Exploring user tagging behavior provides a promising way to understand users’ information needs. However, free and relatively uncontrolled vocabulary has its drawback in terms of lack of standardization and semantic ambiguity. Moreover, the relationships among tags have not been explored even there exist rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach to construct tag ontology based on the widely used general ontology WordNet to capture the semantics and the structural relationships of tags. Ambiguity of tags is a challenging problem to deal with in order to construct high quality tag ontology. We propose strategies to find the semantic meanings of tags and a strategy to disambiguate the semantics of tags based on the opinion of WordNet lexicographers. In order to evaluate the usefulness of the constructed tag ontology, in this paper we apply the extracted tag ontology in a tag recommendation experiment. We believe this is the first application of tag ontology for recommendation making. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.

What happened to the education revolution? : new media technologies and the Australian curriculum : constraints and possibilities

This chapter investigates the place of new media in Queensland in the light of the Australian curriculum. ‘Multimodal texts’ in English are being defined as largely electronically ‘created’ and yet restricted access to digital resources at the chalkface may preclude this work from happening. The myth of the ‘digital native’ (Prensky, 2007), combined with the reality of the ‘digital divide’ coupled with technophobia amongst some quite experienced teachers, responsible for implementing the curriculum, paints a picture of constraints. These constraints are due in part to protective state bans in Queensland on social networking sites and school bans on mobile phone use. Some ‘Generation next’ will have access to digital platforms for the purpose of designing texts at home and school, and others will not. Yet without adequate Professional Development for teachers and substantially increased ICT infrastructure funding for all schools, the way new media and multimodal opportunities are interpreted at state level in the curriculum may leave much to be desired in schools. This chapter draws on research that I recently conducted on the professional development needs of beginning teachers, as well as a critical reading of the ACARA policy documents.

Trading spaces : on the lore and limitations of latent semantic analysis

Two decades after its inception, Latent Semantic Analysis(LSA) has become part and parcel of every modern introduction to Information Retrieval. For any tool that matures so quickly, it is important to check its lore and limitations, or else stagnation will set in. We focus here on the three main aspects of LSA that are well accepted, and the gist of which can be summarized as follows: (1) that LSA recovers latent semantic factors underlying the document space, (2) that such can be accomplished through lossy compression of the document space by eliminating lexical noise, and (3) that the latter can best be achieved by Singular Value Decomposition. For each aspect we performed experiments analogous to those reported in the LSA literature and compared the evidence brought to bear in each case. On the negative side, we show that the above claims about LSA are much more limited than commonly believed. Even a simple example may show that LSA does not recover the optimal semantic factors as intended in the pedagogical example used in many LSA publications. Additionally, and remarkably deviating from LSA lore, LSA does not scale up well: the larger the document space, the more unlikely that LSA recovers an optimal set of semantic factors. On the positive side, we describe new algorithms to replace LSA (and more recent alternatives as pLSA, LDA, and kernel methods) by trading its l2 space for an l1 space, thereby guaranteeing an optimal set of semantic factors. These algorithms seem to salvage the spirit of LSA as we think it was initially conceived.

Social implications of radical technology adoption within the livestock industry - a design investigation: Innovating disruptive technologies in traditional marketplaces

This thesis presents a design investigation into how traditional technology-orientated markets can use design led innovation (DLI) strategies in order to achieve better market penetration of disruptive products. In a review of the Australian livestock industry, considering historical information and present-day trends, a lack of socio-cultural consideration was identified in the design and implementation of products and systems, previously been taken to market. Hence the adoption of these novel products has been documented as extremely slow. Classical diffusion models have typically been used in order to implement these products. However, this thesis poses that it is through the strategic intent of design led innovation, where heavily technology-orientated markets (such as the Australian livestock industry), can achieve better final adoption rates. By considering a range of external factors (business models, technology and user needs), rather than focusing design efforts solely on the technology, it is argued that using DLI approach will lead to disruptive innovations being made easier to adopt in the Australian livestock industry. This thesis therefore explored two research questions: 1. What are the social inhibitors to the adoption of a new technology in the Australian livestock industry? 2. Can design be used to gain a significant feedback response to the proposed innovation? In order to answer these questions, this thesis used a design led innovation approach to investigate the livestock industry, centring on how design can be used early on in the development of disruptive products being taken to market. This thesis used a three stage data collection programme, combining methods of design thinking, co-design and participatory design. The first study found four key themes to the social barriers of technology adoption; Social attitudes to innovation, Market monitoring, Attitude to 3D imaging and Online processes. These themes were built upon through a design thinking/co-design approach to create three ‘future scenarios’ to be tested in participant workshops. The analysis of the data collection found four key socio-cultural barriers that inhibited the adoption of a disruptive innovation in the Australian livestock industry. These were found to be a lack of Education, a Culture of Innovation, a Lack of Engagement and Communication barriers. This thesis recommends five key areas to be focused upon in the subsequent design of a new product in the Australian livestock industry. These recommendations are made to business and design managers looking to introduce disruptive innovations in this industry. Moreover, the thesis presents three design implications relating to stakeholder attitudes, practical constraints and technological restrictions of innovations within the industry.

Using turning technologies to engage tutors and facilitate effective marking and moderation in large classes

Audience Response Systems (ARS) have been successfully used by academics to facilitate student learning and engagement, particularly in large lecture settings. However, in large core subjects a key challenge is not only to engage students, but also to engage large and diverse teaching teams in order to ensure a consistent approach to grading assessments. This paper provides an insight into the ways in which ARS can be used to encourage participation by tutors in marking and moderation meetings. It concludes that ARS can improve the consistency of grading and the quality of feedback provided to students.

An information accountability framework for shared eHealth policies

Privacy issues have hindered the evolution of e-health since its emergence. Patients demand better solutions for the protection of private information. Health professionals demand open access to patient health records. Existing e-health systems find it difficult to fulfill these competing requirements. In this paper, we present an information accountability framework (IAF) for e-health systems. The IAF is intended to address privacy issues and their competing concerns related to e-health. Capabilities of the IAF adhere to information accountability principles and e-health requirements. Policy representation and policy reasoning are key capabilities introduced in the IAF. We investigate how these capabilities are feasible using Semantic Web technologies. We discuss with the use of a case scenario, how we can represent the different types of policies in the IAF using the Open Digital Rights Language (ODRL).

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.