Editorial

The article presents an introduction to the vol. 11 of the "Journal of Hospitality and Tourism Management." The author feels the need for a platform to publish articles and contemporary research in the areas of hospitality, travel, tourism, leisure and event management. The article presents brief information about some of the articles published in the issue. The first article is by Tim Lockyer entitled "Weekend Accommodation-The Challenge: What are the Guests Looking for?," it reports on the means of improving weekend occupancy in hotels. The second article is by Tim Lockyer and M. Tsai titled "Dimensions of Chinese Culture Values in Relation to the Hotel Dining Experience." In this article the authors examine their dining experience in a 5-star hotel in Taiwan. Another article is "Predicting Job Retention of Hourly Employees in the Lodging Industry," by Ady Milman and Peter Ricci. This article focuses on the data of hourly paid employees working in small or medium sized hotels in the United States.

The transform likelihood ratio method for rare event simulation with heavy tails

We present a novel method, called the transform likelihood ratio (TLR) method, for estimation of rare event probabilities with heavy-tailed distributions. Via a simple transformation ( change of variables) technique the TLR method reduces the original rare event probability estimation with heavy tail distributions to an equivalent one with light tail distributions. Once this transformation has been established we estimate the rare event probability via importance sampling, using the classical exponential change of measure or the standard likelihood ratio change of measure. In the latter case the importance sampling distribution is chosen from the same parametric family as the transformed distribution. We estimate the optimal parameter vector of the importance sampling distribution using the cross-entropy method. We prove the polynomial complexity of the TLR method for certain heavy-tailed models and demonstrate numerically its high efficiency for various heavy-tailed models previously thought to be intractable. We also show that the TLR method can be viewed as a universal tool in the sense that not only it provides a unified view for heavy-tailed simulation but also can be efficiently used in simulation with light-tailed distributions. We present extensive simulation results which support the efficiency of the TLR method.

Multidisciplinary strategies for the management of heart failure patients at high risk for admission - A systematic review of randomized trials

OBJECTIVES The aim of this study was to determine whether multidisciplinary strategies improve outcomes for heart failure (HF) patients. BACKGROUND Because the prognosis of HF remains poor despite pharmacotherapy, there is increasing interest in alternative models of care delivery for these patients. METHODS Randomized trials of multidisciplinary management programs in HF were identified by searching electronic databases and bibliographies and via contact with experts. RESULTS Twenty-nine trials (5,039 patients) were identified but were not pooled, because of considerable heterogeneity. A priori, we divided the interventions into homogeneous groups that were suitable for pooling. Strategies that incorporated follow-up by a specialized multidisciplinary team (either in a clinic or a non-clinic setting) reduced mortality (risk ratio [RR] 0.75, 95% confidence interval [CI] 0.59 to 0.96), HF hospitalizations (RR 0.74, 95% CI 0.63 to 0.87), and all-cause hospitalizations (RR 0.81, 95% CI 0.71 to 0.92). Programs that focused on enhancing patient self-care activities reduced HF hospitalizations (RR 0.66, 95% CI 0.52 to 0.83) and all-cause hospitalizations (RR 0.73, 95% CI 0.57 to 0.93) but had no effect on mortality (RR 1.14, 95% CI 0.67 to 1.94). Strategies that employed telephone contact and advised patients to attend their primary care physician in the event of deterioration reduced HF hospitalizations (RR 0.75, 95% CI 0.57 to 0.99) but not mortality (RR 0.91, 95% CI 0.67 to 1.29) or all-cause hospitalizations (RR 0.98, 95% CI 0.80 to 1.20). In 15 of 18 trials that evaluated cost, multidisciplinary strategies were cost-saving. CONCLUSIONS Multidisciplinary strategies for the management of patients with HF reduce HF hospitalizations. Those programs that involve specialized follow-up by a multidisciplinary team also reduce mortality and all-cause hospitalizations. (C) 2004 by the American College of Cardiology Foundation.

Finding negative event oriented patterns in long temporal sequences

Pattern discovery in a long temporal event sequence is of great importance in many application domains. Most of the previous work focuses on identifying positive associations among time stamped event types. In this paper, we introduce the problem of defining and discovering negative associations that, as positive rules, may also serve as a source of knowledge discovery. In general, an event-oriented pattern is a pattern that associates with a selected type of event, called a target event. As a counter-part of previous research, we identify patterns that have a negative relationship with the target events. A set of criteria is defined to evaluate the interestingness of patterns associated with such negative relationships. In the process of counting the frequency of a pattern, we propose a new approach, called unique minimal occurrence, which guarantees that the Apriori property holds for all patterns in a long sequence. Based on the interestingness measures, algorithms are proposed to discover potentially interesting patterns for this negative rule problem. Finally, the experiment is made for a real application.

Finding temporal features of event-oriented patterns

A major task of traditional temporal event sequence mining is to predict the occurrences of a special type of event (called target event) in a long temporal sequence. Our previous work has defined a new type of pattern, called event-oriented pattern, which can potentially predict the target event within a certain period of time. However, in the event-oriented pattern discovery, because the size of interval for prediction is pre-defined, the mining results could be inaccurate and carry misleading information. In this paper, we introduce a new concept, called temporal feature, to rectify this shortcoming. Generally, for any event-oriented pattern discovered under the pre-given size of interval, the temporal feature is the minimal size of interval that makes the pattern interesting. Thus, by further investigating the temporal features of discovered event-oriented patterns, we can refine the knowledge for the target event prediction.