Identifying information from data using an organizational goals ontology: a case of the Australian economy / Tengku Adil Tengku Izhar and Torab Torabi.

Organizational data is important to assist domain experts and entrepreneurs for decision making process in relation to the organizational goals. The trustworthiness of organizational data in relation to achieving the organizational goals is often questioned because of the vast amount of organizational data. This paper proposes a methodology to evaluate organizational data that relates to the organizational goals. This refers to the importance of assisting the organization to utilize relevance of organizational data from the vast amount of datasets for decision-making in relation to the organizational goals. The aim of this paper is to evaluate the level of the organizational goals achievement. In order to achieve this aim, we identify the dependency relationship between organizational data and organizational goals. Based on this dependency relationship, we define a metrics to analyse organizational data to be considered relevant to the organizational goals achievement. The case study is present to test the applicability of the methodology to measure the level of the Australian economy. The results show the dependency relationship between the case study goal and its variables. The contribution of this paper will serve as a new approach in evaluating the level of the organizational goals achievement

PRACTICAL PROCESS NON-CONFORMANCE DETECTION

This paper represents the culmination of a thorough investigation into the existing technological methodologies to solve process non-conformance detection. We explore how to develop this knowledge into an approach that offers a useable, practical solution. Process non-conformance detection research has been explored fairly sparingly and sporadically from around 1995 until now. The work presented in the literature, while technically effective at detecting non-conformance seemed to be practically unviable for a variety of reasons. In this paper we address why the detection of process non-conformance is valuable for process improvement, we examine the practical shortcomings in the existing solutions, explain why these shortcomings exist and how these approaches have failed to provide practical value in various ways. We explore the issues currently related to this research area that withhold process non-conformance detection from being practically useful, and present our own approach that addresses these issues. Our approach involves a generic and practical method of specifying rules that describe how enactment data is to be checked for any non-conformance instances that may have occurred. The approach is designed to cater for practical considerations such as timely detection, minimal interference, and minimal required effort while remaining separate from the process specification. We support this claim via two case studies that test our assertions and demonstrate how process non-conformance detection is valuable for process improvement. Furthermore, we show how a process may be improved by the approach using the result data from several hundred process enactments done both before and after the approach was integrated. The result data from these two phases is provided and discussed

Placesense: a tool for sensing communities

Abstract One challenge in the context-aware environment is to detect users' presence in a natural way and to process such information in order to provide suitable services for users. This paper introduces PlaceSense, an application using bluetooth technology, building on top of the Place-based Virtual Community architecture that can detect users presence for triggering services automatically. The paper also discusses the different use-cases of bluetooth technology for community sensing