Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.

Survival analysis of time-to-event data in respiratory health research studies

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

Battlefield tourism in Turkey : an empirical analysis of British ex-colonial event attendance

This paper explores the influence of visitor satisfaction on intention to recommend event attendance. The Anzac Day commemoration at Gallipoli, Turkey, an event that has become increasingly popular in recent years and provides the backdrop for the current study. Some 20,000 people travel to attend this event. Data was collected from 331 attendees while they were in transit from Gallipoli to Istanbul on 25 April 2007. The analysis of this data was undertaken using factor analysis as a basis for identifying model constructs to be tested using structural equation modelling. Findings suggest that the constructs relating to the ceremonies held at Anzac Cove and Lone Pine and the experiential and emotional aspects of the event were significant predictors of event satisfaction and subsequent recommending behaviour. The implications of these findings for events in general and the success of the Anzac Day commemorations at Gallipoli are discussed.

Predictive mapping of abalone fishing grounds using remotely-sensed LiDAR and commercial catch data

Defining the geographic extent of suitable fishing grounds at a scale relevant to resource exploitation for commercial benthic species can be problematic. Bathymetric light detection and ranging (LiDAR) systems provide an opportunity to enhance ecosystem-based fisheries management strategies for coastally distributed benthic fisheries. In this study we define the spatial extent of suitable fishing grounds for the blacklip abalone (Haliotis rubra) along 200 linear kilometers of coastal waters for the first time, demonstrating the potential for integration of remotely-sensed data with commercial catch information. Variables representing seafloor structure, generated from airborne bathymetric LiDAR were combined with spatially-explicit fishing event data, to characterize the geographic footprint of the western Victorian abalone fishery, in south-east Australia. A MaxEnt modeling approach determined that bathymetry, rugosity and complexity were the three most important predictors in defining suitable fishing grounds (AUC = 0.89). Suitable fishing grounds predicted by the model showed a good relationship with catch statistics within each sub-zone of the fishery, suggesting that model outputs may be a useful surrogate for potential catch.

Is optimistic bias influenced by control or delay?

Optimistic bias is a commonly observed but poorly explained phenomenon. Our aim was to determine whether optimistic bias varied according to the nature of the event. Two event characteristics were explored: control and delay. A sample of 100 participants aged 18–30 years was randomly selected from the local residential telephone directory. Respondents were interviewed over the telephone. The highly structured interview schedule assessed respondents' perceptions of their own risk, and the risk of an average person of their age and sex for experiencing four negative life events: developing skin cancer, being involved in a serious car accident as the driver, being involved in a serious car accident as a passenger and having to wear a hearing aid. It also assessed respondents' perceptions of control and delay for each event. Data analysis using a repeated-measures MANOVA showed that optimistic bias occurred for all four events. Optimistic bias was significantly greater for the two events high in control (skin cancer and accident as the driver) than for those low in control (accident as a passenger and hearing aid). Delay was not related to the magnitude of optimistic bias. These findings have implications for health promotion campaigns and self-protective behaviors.

Parametric conditional frailty models for recurrent cardiovascular events in the lipid study

Background Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals.Methods We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females. Results A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR¼0.71, 95% CI0.60–0.83). However, the treatment effect was not significant in women due to smaller sample size (HR¼0.75, 95% CI 0.51–1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p¼0.24 for men and p¼0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6–4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4–13.6). Limitations The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors.Conclusions The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.

Olanzapine or chlorpromazine plus lithium in first episode psychotic mania: An 8-week randomised controlled trial

BACKGROUND: Treatment strategies for mental disorders may vary according to illness stage. However no data currently exist to guide treatment in first episode psychotic mania. The aim of this study was to compare the safety and efficacy profile of chlorpromazine and olanzapine, as add-on to lithium, in patients with a first episode of psychotic mania, expecting better safety profile and adherence to olanzapine but similar efficacy for both treatments. METHODS: Data from 83 patients were collected in an 8-week randomised controlled trial on clinical variables, side effects, vital signs, and weight. Analyses of treatment differences over time were based on intent-to-treat principles. Kaplan-Meier estimated survival curves were used to analyse time-to-event data and mixed effects models repeated measures analysis of variance were used to determine treatment group differences over time on safety and efficacy measures. RESULTS: Ethics committee approval to delay informed consent procedure until recovery from the acute episode allowed the inclusion of 83 patients highly representative of those treated in the public sector. Contrary to our hypotheses, safety profile of both medications was similar. A signal for higher rate (P=.032) and earlier occurrence (P=.043) of mania remission was observed in the olanzapine group which did not survive correction for multiple comparisons. CONCLUSIONS: Olanzapine and chlorpromazine have a similar safety profile in a uniquely representative cohort of patients with first episode psychotic mania. The possibility for a greater impact of olanzapine on manic symptoms leading to earlier remission of the episode needs exploration in a large sample.

Conducting event studies with Asia-Pacific security market data

We investigate the effectiveness of several well-known parametric and non-parametric event study test statistics with security price data from the major Asia-Pacific security markets. Extensive Monte Carlo simulation experiments with actual daily security returns data reveal that the parametric test statistics are prone to misspecification with Asia-Pacific returns data. Two non-parametric tests, a rank test [Corrado and Zivney (Corrado, C.J., Zivney, T.L., 1992, The specification and power of the sign test in event study hypothesis tests using daily stock returns, Journal of Financial and Quantitative Analysis 27(3), 465-478)] and a sign test [Cowan (Cowan, A.R., 1992, Non-parametric event study tests, Review of Quantitative Finance and Accounting 1(4), 343–358)] were the best performers overall with market model excess returns computed using an equal weight index.

Efficient detection of emergency event from moving object data streams

The advance of positioning technology enables us to online collect moving object data streams for many applications. One of the most significant applications is to detect emergency event through observed abnormal behavior of objects for disaster prediction. However, the continuously generated moving object data streams are often accumulated to a massive dataset in a few seconds and thus challenge existing data analysis techniques. In this paper, we model a process of emergency event forming as a process of rolling a snowball, that is, we compare a size-rapidly-changed (e.g., increased or decreased) group of moving objects to a snowball. Thus, the problem of emergency event detection can be resolved by snowball discovery. Then, we provide two algorithms to find snowballs: a clustering-and-scanning algorithm with the time complexity of O(n 2) and an efficient adjacency-list-based algorithm with the time complexity of O(nlogn). The second method adopts adjacency lists to optimize efficiency. Experiments on both real-world dataset and large synthetic datasets demonstrate the effectiveness, precision and efficiency of our algorithms © 2014 Springer International Publishing Switzerland.

Reliable event transfer in wireless sensor networks deployed for emergency response

Sensor Networks have applications in diverse fields. They can be deployed for habitat modeling, temperature monitoring and industrial sensing. They also find application in battlefield awareness to sense chemicals and other gases used in chemical and biological warfare. An application that has become increasingly attractive in the post 9/11 era is the use of wireless sensor networks for emergency (first) response in mass casualty incidents. Reliability of data and event transfer is of critical importance to emergency response applications. In this work we propose a reliable event transfer mechanism making use of an overlay network of relay nodes. The overlay network removes the burden of data relaying from the sensor nodes and results in increasing the lifetime of the network. Simulation results prove the benefits of such an architecture. Reliability is increased 10-30% with reduction in event traffic of 60-80%.

Lease based addressing for event-driven wireless sensor networks

Sensor Networks have applications in diverse fields. They can be deployed for habitat modeling, temperature monitoring and industrial sensing. They also find applications in battlefield awareness and emergency (first) response situations. While unique addressing is not a requirement of many data collecting applications of wireless sensor networks it is vital for the success of applications such as emergency response. Data that cannot be associated with a specific node becomes useless in such situations. In this work we propose an addressing mechanism for event-driven wireless sensor networks. The proposed scheme eliminates the need for network wide Duplicate Address Detection (DAD) and enables reuse of addresses.