Applicability of Process Mining Techniques in Business Environments

This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques. The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity. We concentrate on two possible scenarios: "batch" and "on-line" Process Mining. Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated. After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance. Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models. Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives. The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).

Extreme temperature events analyzed with Fast Fourier Transform

Extreme weather and climate events have received increased attention in the last few years, due to the often large loss of agriculture business and exponentially increasing costs associated with them and insurance planning. This increased attention raises the question as to whether extreme weather and climate events are truly increasing, whether this is only a perceived increase exacerbated by enhanced media coverage, or both. There are a number of ways extreme climate events can be defined, such as extreme daily temperatures, extreme daily rainfall amounts, and large areas experiencing unusually warm monthly temperatures, among others. In this study, we will focus our attention in frost and heatstroke events measuring it as the number of days under 0 ºC and number of days with daily maximum over 30ºC monthly respectively. We have studied the trends in these extreme events applying a Fast Fourier Transform to the series to clarify the tendency. Lack of long-term climate data suitable for analysis of extremes is the single biggest obstacle to quantifying whether extreme events have changed over the twentieth century, including high temporal and spatial resolution observations of temperatures. However, several series have been grouped in different ways: chosen the longest series independently, by provinces, by main watersheds and altitude. On the other hand, synthetic series generated by Luna and Balairón (AEMet) were also analyzed. The results obtained by different pooling data are discussed concluding the difficulties to assess the extreme events tendencies and high regional variation in the trends.

Calendar of events.

Mode of access: Internet.

Illinois calendar of events.

Issue for Oct. 1973/Mar. 1974 has title: Calendar of events.

Is Mindfulness Beneficial for Job Performance Following Positive and Negative Affective Events? The Attenuating Role of Mindfulness on Self-Serving Bias

Thesis (Ph.D.)--University of Washington, 2016-05

Experience as a moderator of involvement and satisfaction on brand loyalty in a business-to-business setting 02-314R

This paper examines the relative influence of two key antecedents of brand loyalty-satisfaction and involvement and the moderating role of experience, using a sample of business buyers. The central argument of this paper is that the strength of the effect of these variables on attitudinal brand loyalty will vary with the level of customer experience with purchasing the service. Building on previous research which examined low-risk, customer product settings [Kim, J., Lim, J.S., & Bhargava, M. (1998). The role of affect in attitude formation: A classical conditioning approach. Journal of the Academy of Marketing Science 26 (2): pp. 143-152; Shiv, B., & Fedorikhin, A. (1999). Heart and mind in conflict: The interplay of affect and cognition in consumer decision-making. Journal of Consumer Research 26: 278], this study shows that for a high-risk setting, involvement with the service category will be more dominant in its influence on brand loyalty than satisfaction with the preferred brand. Furthermore, it was found that experience moderated the influence of involvement and satisfaction on attitudinal brand loyalty for a high-risk business-to-business service. This study provides new insights into the theory and practice of buyer behavior and business-to-business brands. Crown Copyright (C) 2004 Published by Elsevier Inc. All rights reserved.