Distributed data mining in grid computing environments

The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper

Perceived derived attributes of online customer reviews

The influence of online customer reviews (OCRs) on customers' purchase intention has recently gained considerable attention, in both academic and business communities. Technology allows customers to freely and easily post their comments and opinions online about any product or service; this type of customer review can have a significant effect on customers' purchase decisions. Previous studies, however, have mainly focused on the influence of the virtual attributes of OCRs such as volume and valence on consumers' intentions, while limited attention has been paid to understanding the effects of the derived attributes. This study, thus, aims to understand the impact of the perceived derived attributes of OCRs on customer trust and intention. This study develops a – Perceived Derived Attributes (PDA) - model, based on the inclusion of perceived control from the Theory of Planned Behaviour (TPB) with the Technology Acceptance Model (TAM), in order to investigate the effects of OCRs on customers’ purchasing intention. A total of 489 responses to a survey were collected from users of amazon.com. The findings from this study suggest that customer trust in an e-vendor and their intention to shop online are significantly affected by perceived usefulness, perceived ease of use and perceived enjoyment of OCRs. Furthermore, the sense of control derived from OCRs significantly affects customer intention and significantly affects customer trust in e-vendors, particularly for customers who frequently check OCRs before making a purchase. Clearly, those attributes of OCRs are linked to the development of the shopping environment, which consequently can affect sales

The effect of web advertising visual design on online purchase intention: An examination across gender

With web advertising growing to be a huge industry, it is important to understand the effectiveness of web advertisement. In this study we investigate the effects of web advertising visual design (WAVD) purchasing intention within the framework of an integrated model. Nine hypotheses were developed and tested on a dataset of 316 observations collected via a questionnaire survey. The results of structural equation modeling (SEM) indicate that while web advertising visual cues influence consumers' purchasing intention through advertising attitudes and brand attitudes, they do not have direct effects on purchasing intention. Further results on the moderating role of gender suggest that web advertising visual cues have direct effect on consumers' purchasing intention for male groups but not for female groups. This study contributes to the understanding the role of visual dimensions in forming online purchase intentions