The LH5 model for data mining

In the age of E-Business many companies faced with massive data sets that must be analysed for gaining a competitive edge. these data sets are in many instances incomplete and quite often not of very high quality. Although statistical analysis can be used to pre-process these data sets, this technique has its own limitations. In this paper we are presenting a system - and its underlying model - that can be used to test the integrity of existing data and pre-process the data into clearer data sets to be mined. LH5 is a rule-based system, capable of self-learning and is illustrated using a medical data set.

Reflections of professional practice: using electronic discourse analysis networks (EDANs) to examine embedded business emails

Emails have become a central genre in business communication, reflecting both how people communicate and how they go about their professional practices. This chapter examines embedded business emails as reflections of the professional practices of the regulatory and policy department of a multinational based in London, UK. It argues that the nature of online communication in international organisations, with its high levels of intertextuality and interdiscursivity, requires multidimensional analytical approaches that are capable of capturing its complexity and dynamics. To this end, the chapter introduces electronic discourse analysis networks (EDANs) as one example of such approaches. It begins with a brief review of the literature that has informed the study reported on here before it discusses EDANs as its analytical framework. Using a group of embedded emails and a number of networked data sets, the chapter shows how EDANs can be used to further our understanding of professional online communication.

"One of our hosts in another country": Challenges of data geolocation in cloud storage

Physical location of data in cloud storage is an increasingly urgent problem. In a short time, it has evolved from the concern of a few regulated businesses to an important consideration for many cloud storage users. One of the characteristics of cloud storage is fluid transfer of data both within and among the data centres of a cloud provider. However, this has weakened the guarantees with respect to control over data replicas, protection of data in transit and physical location of data. This paper addresses the lack of reliable solutions for data placement control in cloud storage systems. We analyse the currently available solutions and identify their shortcomings. Furthermore, we describe a high-level architecture for a trusted, geolocation-based mechanism for data placement control in distributed cloud storage systems, which are the basis of an on-going work to define the detailed protocol and a prototype of such a solution. This mechanism aims to provide granular control over the capabilities of tenants to access data placed on geographically dispersed storage units comprising the cloud storage.