Missing Events Research Articles

The Anomalously Old Bush Stream Rock Avalanche and Its Implications for Landslide Inventories in Dynamic Landscapes

Previous dating of rock slope failures in most glaciated mountain chains has revealed almost exclusively young, mostly Holocene ages. In this study, a rock avalanche in the glaciated Rangitata Basin in Canterbury, New Zealand is mapped, described, and dated, revealing a pre-Holocene age of failure. The geomorphology and characteristics of the rock avalanche, named here as the Bush Stream Rock Avalanche, were assessed from field mapping and photogrammetric analyses. To assess the age of the rock avalanche, in-situ cosmogenic 10Be exposure dating was applied to boulders on the deposit. The geomorphological mapping shows that the morphology of the head scarp and deposit of the rock avalanche are distinct from the surrounding landscape, much of which appears to be glacial in origin. The rock avalanche travelled about 4 km, with a volume of 50-100 M m3, and appears to have temporarily blocked Bush Stream. The dated boulders suggest an age of 15-20 ka, making it the oldest reported alpine rock avalanche in New Zealand, and one of the oldest worldwide. Deep depressions, possibly kettle holes, in the deposit are indicative of runout over a glacier (or associated dead ice), but any glacier present at the time must have been small and probably decaying. The excellent preservation was likely favoured by a small catchment located on the dry lee side of the Two Thumb Range which dampened glacial and fluvial activity. The study confirms that rock avalanches were being produced in the Southern Alps early in the deglaciation, but that evidence for them likely exists only in the rare environments that have conditions favourable for preservation. Preservation potential in most of the Southern Alps is low, with older deposits readily buried or eroded by New Zealand’s high rates of erosion, aggradation, and dynamic processes. Unless methods can be developed to identify missing older events, we are hampered in our ability to understand the frequency, and therefore causes, of large slope failures in the Southern Alps and in other highly dynamic alpine landscapes.

Downward Counterfactual Search for Extreme Events

An event catalog is a foundation of the risk analysis for any natural hazard. Especially if the catalog is comparatively brief relative to the return periods of possible events, it may well be deficient in extreme events that are of special importance to risk stakeholders. It is common practise for quantitative risk analysts to construct ensembles of future scenarios that include extreme events that are not in the event catalog. But past poor experience for many hazards shows that these ensembles are still liable to be missing crucial unknown events. An explicit systematic procedure is proposed here for searching for these key missing events. This procedure starts with the historical catalog events, and explores alternative realizations of them where things turned for the worse. These are termed downward counterfactuals. By repeatedly exploring ways in which the event loss might have been incrementally worse, missing events can be discovered that may take risk analysts, and risk stakeholders, by surprise. The downward counterfactual search for extreme events is illustrated with examples drawn from a variety of natural hazards. Attention is drawn to the problem of overfitting to the historical record, and the value of stochastic modeling of the past.

Mesoproterozoic burial of the Kaapvaal craton, southern Africa during Rodinia supercontinent assembly from (U-Th)/He thermochronology

We present THe and ZHe data for basement samples across the Kaapvaal craton to investigate the cratonic burial and erosion history during the assembly and breakup of the Rodinia supercontinent. THe and ZHe dates vary from 1187 to 135 Ma and 997 to 32 Ma, respectively. Both are negatively correlated with effective U concentration, consistent with lower He retentivity at higher radiation damage dose. The oldest THe dates, combined with volcanic rocks that document the basement was at the surface at ∼1.4 Ga, require heating to ≥130°C at ∼1.0-1.2 Ga to cause complete He loss from the titanites. We attribute this Mesoproterozoic heating event to widespread burial of the craton during Rodinia supercontinent assembly by sediments shed from the Namaqua-Natal orogen on the craton's margin. Later Neoproterozoic-Paleozoic erosion re-exhumed the basement to the surface. Spatial variability in ZHe data patterns can be explained by geologically-reasonable differences in Phanerozoic Karoo basin burial and erosion. The Mesoproterozoic and Karoo burial phases are analogous in that their detritus was derived from similarly-located orogens during two different supercontinent cycles. Mesoproterozoic deposition across the Kaapvaal craton may be part of a more global signal of burial and erosion during Rodinia assembly and breakup. Our dataset illustrates the power of THe and ZHe thermochronology for detecting missing Proterozoic depositional events in the rock record.

Discovering process models for the analysis of application failures under uncertainty of event logs

Computer applications, such as servers, databases and middleware, ubiquitously emit execution traces stored in log files. The use of logs for the analysis of application failures is known since the early days of computers. Field data studies have shown that application logs are fraught with uncertainty, i.e., missing or noisy events in the logs. A body of research that has dealt successfully with uncertainty in event logs is process mining from the business process management community, specifically by discovering process models. The literature has shown the value of process mining across several domains, but as yet there is no study that quantifies possible improvements from using process models, and the impact of uncertainty in the context of application failures. This work addresses the use of process mining for detecting failures from application logs. First, process models are discovered from logs; then conformance checking is used to detect deviations from the models. We contribute to knowledge engineering research with a systematic measurement study that quantifies the failure detection capability of conformance checking in spite of missing events, and its accuracy with respect to process models obtained from noisy logs. Analysis is done with a dataset of 55,462 execution traces from three independent real-life applications. We obtain a mixed answer depending on the application under test; our measurements provide insights into the use of process mining for failure analysis.

Autoencoders for improving quality of process event logs

Low quality of business process event logs, as determined by anomalous and missing values, is often unavoidable in practical contexts. The output of process analysis that uses event logs with missing and anomalous values is also likely to be of low quality, thus decreasing the quality of any decisions based on it. While previous work has focused on reconstructing missing events in an event log or removing anomalous traces, in this paper we focus on detecting anomalous values and reconstructing missing values at the level of attributes in event logs. We propose methods based on autoencoders, which are a class of neural networks that can reconstruct their own input and are particularly suitable to learn a model of the complex relationships among attribute values in an event log. These methods do not rely on any a-priori knowledge about the business process that generated an event log and are evaluated using real world and artificially-generated event logs. The paper also discusses a qualitative analysis of the impact of event log cleaning and reconstruction on the output of process discovery. The proposed approach shows remarkable performance regarding activity labels and timestamps in artificial event logs. The performance in the case of real world event logs, in particular timestamp anomaly detection, is lower, which may be due to high variability of attribute values in the chosen event logs. Process models discovered from reconstructed event logs are characterised by lower variability of allowed behaviour and, therefore, are more usable in practice.

A New Timing Error Cost Function for Binary Time Series Prediction.

The ability to make predictions is central to the artificial intelligence problem. While machine learning algorithms have difficulty in learning to predict events with hundreds of time-step dependencies, animals can learn event timing within tens of trials across a broad spectrum of time scales. This suggests strongly a need for new perspectives on the forecasting problem. This paper focuses on binary time series that can be predicted within some temporal precision. We demonstrate that the sum of squared errors (SSE) calculated at every time step is not appropriate for this problem. Next, we look at the advantages and shortcomings of using a dynamic time warping (DTW) cost function. Then, we propose the squared timing error (STE) that uses DTW on the event space and applies SSE on the timing error instead of at each time step. We evaluate all three cost functions on different types of timing errors, such as phase shift, warping, and missing events, on synthetic and real-world binary time series (heartbeats, finance, and music). The results show that STE provides more information about timing error, is differentiable, and can be computed online efficiently. Finally, we devise a gradient descent algorithm for STE on a simplified recurrent neural network. We then compare the performance of the STE-based algorithm to SSE- and logit-based gradient descent algorithms on the same network architecture. The results in real-world binary time series show that the STE algorithm generally outperforms all the other cost functions considered.

Additive-multiplicative hazards regression models for interval-censored semi-competing risks data with missing intermediate events

BackgroundIn clinical trials and survival analysis, participants may be excluded from the study due to withdrawal, which is often referred to as lost-to-follow-up (LTF). It is natural to argue that a disease would be censored due to death; however, when an LTF is present it is not guaranteed that the disease has been censored. This makes it important to consider both cases; the disease is censored or not censored. We also note that the illness process can be censored by LTF. We will consider a multi-state model in which LTF is not regarded as censoring but as a non-fatal event.MethodsWe propose a multi-state model for analyzing semi-competing risks data with interval-censored or missing intermediate events. More precisely, we employ the additive and multiplicative hazards model with log-normal frailty and construct the conditional likelihood to estimate the transition intensities among states in the multi-state model. Marginalization of the full likelihood is accomplished using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through the iterative quasi-Newton algorithm.ResultsSimulation is performed to investigate the finite-sample performance of the proposed estimation method in terms of the relative bias and coverage probability of the regression parameters. The proposed estimators turned out to be robust to misspecifications of the frailty distribution. PAQUID data have been analyzed and yielded somewhat prominent results.ConclusionsWe propose a multi-state model for semi-competing risks data for which there exists information on fatal events, but information on non-fatal events may not be available due to lost to follow-up. Simulation results show that the coverage probabilities of the regression parameters are close to a nominal level of 0.95 in most cases. Regarding the analysis of real data, the risk of transition from a healthy state to dementia is higher for women; however, the risk of death after being diagnosed with dementia is higher for men.

Hypoxic changes to the urothelium as a bystander of end-stage bladder disease

Urothelial cells harvested from benign diseased bladders have a compromised capacity to propagate or differentiate invitro, potentially limiting their application in autologous tissue engineering approaches. The causative pathways behind this altered phenotype are unknown. The hypothesis is that hypoxic damage to the urothelium occurs as a bystander to chronic or recurrent episodes of infection and inflammation. The aim of this study was to assess immunohistochemically detected nuclear hypoxia-inducible factor 1 alpha (HIF-1α) and vascular endothelial growth factor in the urothelium when exposed to hypoxia. Human bladder sections from a total of 29 adult and paediatric patients, representing a variety of different pathologies including neuropathy (n=15), were analysed. Tissues from adults with bladder outlet obstruction secondary to prostatic disease (n=1), urothelial carcinoma (n=1) and tonsil (n=1) were used as positive tissue controls for immunohistochemistry. Hypoxia-inducible factor 1 alpha-labelled sections were scanned using a Zeiss AxioScan Z1 slide scanner. Analysis of urothelial nuclear HIF-1α labelling was performed using HistoQuest image analysis software (TissueGnostics). Comparison of nuclear HIF-1α labelling between neuropathic and non-neuropathic sections was performed using one-way analysis of variance with the post hoc Tukey honestly significant difference (HSD) test. Patient urodynamic studies performed before tissue sample harvest were evaluated and correlated to the HIF-1α intensity using Spearman's rank correlation. Hypoxia-inducible factor 1 alpha appeared more intense in the urothelial compartment from neuropathic bladder samples (n=15) than in the control tissues, including non-obstructedsamples (n=9). Image analysis supported this; median nuclear HIF-1α labelling was 29.98±3.10 (standard deviation [SD]) (n=9) in controls and 74.29±7.55 (SD) in neuropathic samples (n=15). A statistically significant difference between the control and neuropathic tissue groups was shown (P<0.05). Of the 15 neuropathic samples, 11 had traceable urodynamic studies. Both initial and maximum detrusor pressures indicated a positive relationship when plotted against HIF-1α labelling. Spearman's rank correlation, with no missing events, confirmed significant correlations between both initial or maximum detrusor pressure and nuclear HIF-1α labelling intensity (median score); P≤0.046 and P≤0.05, respectively. The null hypothesis was accordingly rejected. This study indicates that urothelial nuclear HIF-1α may be a biomarker of hypoxia and a common feature in end-stage bladder disease associated with high-pressure systems.

Constraints on a light leptophobic mediator from LEP data

We apply data taken at the e+e− collider LEP in the 1990’s at center-of-mass energy up to 209 GeV to constrain Dark Matter models with a light leptophobic spin−1 mediator R. We assume that the dark sector particle (DSP) is a spin−1/2 fermion χ. This scenario is well studied in the context of LHC searches for mediator mass from 100 GeV to several TeV. Emission of the mediator off a quark or antiquark at LEP gives rise to di-jet plus missing energy and 4−jet signatures, which we use to limit the relevant couplings. We focus on scenarios with 2mχ > mR, which are poorly constrained by LHC data. We recast published searches by the ALEPH collaboration. For mχ ≲ 20 GeV the best bounds result from an analysis at sqrt{s} ≃ MZ of di-jet plus missing energy events. For heavier DSP but mR ≲ 70 GeV meaningful bounds can be derived from a four jet analysis at sqrt{s}=183 GeV. Unfortunately published searches using four jet final states at sqrt{s} ≃ MZ use only a small fraction of the total data sample. Moreover, all published searches for di-jet plus missing energy final states at sqrt{s}ge 130 GeV have poor efficiency for our model; we therefore design new cuts that combine good background rejection with higher efficiency. Re-analyzing the higher energy data using our new cuts, and an analysis of the complete four jet data sample taken at sqrt{s} ≃ MZ, can explore new regions of parameter space.

A Statistical Model for Earthquake Recurrence Based on the Assimilation of Paleoseismicity, Historic Seismicity, and Instrumental Seismicity

AbstractPaleoearthquakes and historic earthquakes are the most important source of information for the estimation of long‐term earthquake recurrence intervals in fault zones, because corresponding sequences cover more than one seismic cycle. However, these events are often rare, dating uncertainties are enormous, and missing or misinterpreted events lead to additional problems. In the present study, I assume that the time to the next major earthquake depends on the rate of small and intermediate events between the large ones in terms of a “clock change” model. Mathematically, this leads to a Brownian passage time distribution for recurrence intervals. I take advantage of an earlier finding that under certain assumptions the aperiodicity of this distribution can be related to the Gutenberg‐Richter b value, which can be estimated easily from instrumental seismicity in the region under consideration. In this way, both parameters of the Brownian passage time distribution can be attributed with accessible seismological quantities. This allows to reduce the uncertainties in the estimation of the mean recurrence interval, especially for short paleoearthquake sequences and high dating errors. Using a Bayesian framework for parameter estimation results in a statistical model for earthquake recurrence intervals that assimilates in a simple way paleoearthquake sequences and instrumental data. I present illustrative case studies from Southern California and compare the method with the commonly used approach of exponentially distributed recurrence times based on a stationary Poisson process.

The hidden costs: Identification of indirect costs associated with acute gastrointestinal illness in an Inuit community.

BackgroundAcute gastrointestinal illness (AGI) incidence and per-capita healthcare expenditures are higher in some Inuit communities as compared to elsewhere in Canada. Consequently, there is a demand for strategies that will reduce the individual-level costs of AGI; this will require a comprehensive understanding of the economic costs of AGI. However, given Inuit communities’ unique cultural, economic, and geographic contexts, there is a knowledge gap regarding the context-specific indirect costs of AGI borne by Inuit community members. This study aimed to identify the major indirect costs of AGI, and explore factors associated with these indirect costs, in the Inuit community of Rigolet, Canada, in order to develop a case-based context-specific study framework that can be used to evaluate these costs.MethodsA mixed methods study design and community-based methods were used. Qualitative in-depth, group, and case interviews were analyzed using thematic analysis to identify and describe indirect costs of AGI specific to Rigolet. Data from two quantitative cross-sectional retrospective surveys were analyzed using univariable regression models to examine potential associations between predictor variables and the indirect costs.Results/SignificanceThe most notable indirect costs of AGI that should be incorporated into cost-of-illness evaluations were the tangible costs related to missing paid employment and subsistence activities, as well as the intangible costs associated with missing community and cultural events. Seasonal cost variations should also be considered. This study was intended to inform cost-of-illness studies conducted in Rigolet and other similar research settings. These results contribute to a better understanding of the economic impacts of AGI on Rigolet residents, which could be used to help identify priority areas and resource allocation for public health policies and programs.

A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing

Intermittent missingness in repeated duration analysis is common in applied studies, but has not been rigorously considered in statistics. Under intermittent missingness, whether any missing events exist between two observed events is unknown. In other words, the missing indicators are never observed. Thus, if there exist any missing events between two observed events, researchers observe only the cumulative duration of the two or more events. A quasi-Bayes estimation method that utilizes population-level information is used to appropriately estimate the parameters under unobserved intermittent missingness. The proposed model consists of the following: (1) a latent variable model, (2) a latent missing indicator model separating the true and composite durations, (3) mixtures of duration models, and (4) moment restriction from population-level information to deal with nonignorable intermittent missingness. A new estimation procedure is used to simultaneously combine likelihood and the objective function of GMM with the latent variables; this is called Bayesian data combination. The model is applied to the interpurchase duration in database marketing using the purchase history data of Japan; these data capture the purchase incidences and stores.

A Noise Filtering Algorithm for Event-Based Asynchronous Change Detection Image Sensors on TrueNorth and Its Implementation on TrueNorth.

Asynchronous event-based sensors, or “silicon retinae,” are a new class of vision sensors inspired by biological vision systems. The output of these sensors often contains a significant number of noise events along with the signal. Filtering these noise events is a common preprocessing step before using the data for tasks such as tracking and classification. This paper presents a novel spiking neural network-based approach to filtering noise events from data captured by an Asynchronous Time-based Image Sensor on a neuromorphic processor, the IBM TrueNorth Neurosynaptic System. The significant contribution of this work is that it demonstrates our proposed filtering algorithm outperforms the traditional nearest neighbor noise filter in achieving higher signal to noise ratio (~10 dB higher) and retaining the events related to signal (~3X more). In addition, for our envisioned application of object tracking and classification under some parameter settings, it can also generate some of the missing events in the spatial neighborhood of the signal for all classes of moving objects in the data which are unattainable using the nearest neighbor filter.

Paleoseismic observations along the Langshan range-front fault, Hetao Basin, China: Tectonic and seismic implications

The Langshan range-front fault (LRF) is an active Holocene normal fault that borders Langshan Mountain and the Hetao Basin, northwest of the Ordos Plateau, China. In this study, paleoseismic trenching was undertaken at three sites (North–South): Dongshen village (TC1), Qingshan (TC2), and Wulanhashao (TC3). The paleoevents ED1, ED2, ED3 from TC1 were constrained to 6.0 ± 1.3, 9.6 ± 2.0, and 19.7 ± 4.2 ka, respectively. The single paleoevent (EQ1) from TC2 was constrained to about 6.7 ± 0.1 ka, and the paleoevents EW1, EW2, and EW3 from TC3 were constrained to 2.3 ± 0.4, 6.0 ± 1.0, and before 7.0 ka, respectively. With reference to previous research, the Holocene earthquake sequence of the LRF can be established as 2.30–2.43 (E1), 3.06–4.41 (E2), 6.71–6.80 (E3), 7.60–9.81 (E4), and 19.70 ± 4.20 (E5) ka BP. Events E1, E3, and E4 might have been caused by events with magnitudes of Mw 7.6–7.8 that ruptured the entire LRF. Event E2 might have been smaller magnitude, about M7.0, and ruptured only a portion of the fault. The vertical slip rate of the LRF at the Qingshan site is inferred as 0.9 or 1.4–1.6 mm/year in the last 6.8 ka. The slip rate at Wulanhashao is considered to have been close to, but not <0.66 mm/year since 15 ka BP. We deem the LRF is an unsegmented active fault. Although the possibility of missing events in Late Pleistocene can not be dismissed, we argue the Holocene paleoearthquake history is complete, indicating an average recurrence interval of ~2500 years.

A Multivariate Hawkes Process With Gaps in Observations

Given a collection of entities (or nodes) in a network and our intermittent observations of activities from each entity, an important problem is to learn the hidden edges depicting directional relationships among these entities. Here, we study causal relationships (excitations) that are realized by a multivariate Hawkes process (MHP). The MHP and its variations (spatio-temporal point processes) have been used to study contagion in earthquakes, crimes, neural spiking activities, the stock and foreign exchange markets, and so on. In this paper, we consider the MHP with gaps (MHPG) in observations. We propose a variational problem for detecting sparsely hidden relationships with an MHP that takes into account the gaps from each entity. We bypass the problem of dealing with a large amount of missing events by introducing a small number of unknown boundary conditions. In the case where our observations are sparse (e.g., from 10% to 30%), we show through numerical simulations that robust recovery with MHPG is still possible even if the lengths of the observed intervals are small but they are chosen accordingly. The numerical results also show that the knowledge of gaps and imposing the right boundary conditions are very crucial in discovering the underlying patterns and hidden relationships.

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Abstract. In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965–2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

Probabilistic cleaning over trajectory events of mobile RFID objects

Cleaning up the trajectory events of mobile RFID objects should take the uncertainty of location and unreliability of event detection into account at the same time. In the paper, we first discuss the rules to distinguish false detection events in RFID object trajectories. Then, as a unified cleaning framework, we establish a probabilistic region connection graph to represent region detection features, region connection relationships, and region transition probabilities between neighboring physical regions. Focusing on interpolating the missing events, we suggest two path-based probabilistic cleaning strategies, namely, the Most Likely Path (MLP) strategy and the Highest Weighting Probability Path (HWPP) strategy.

Chronic Heart Failure Care Model for home Monitoring of Patients

Chronic heart failure (CHF), often life-threatening, affects over 95,000 Australian and over 23 million people worldwide. Despite advances in modern medicine, patients with CHF are dismally impacted by poor five-year survival rates, poor health related quality of life, and high 30-day readmission rates. To improve the CHF management, an innovative telemonitoring enhanced home care model has been developed. In this enhanced model of care, patients are provided with a wireless (Bluetooth) weight scale and tablet computer to perform daily weight monitoring at home. Measured weight entries are automatically transferred from the scale to the tablet, and subsequently uploaded to a clinical portal. An application monitors uploaded entries in real time to detect missing weight-monitoring events, and abnormal weight changes according to the guidelines, such as rapid weight gains and unintentional weight loss. The detected events are then classified to generate different types of alerts for interventions needed in usual care. To minimise clinical workload, some alerts automatically trigger questionnaires preloaded in the tablet to assist nurses to triage patients. Finally, the generated alerts are distributed to project nurses and/or the MEPACS call centre, integrated in the care model. Nurses and call operators respectively monitor their alerts on a daily basis to provide three levels of interventions: 1) reminder calls to help patients monitor body weight daily, 2) clinical support on the CHF action plan, including emergency calls, and 3) nurse follow-ups to help identify symptoms of acute decompensated heart failure and other health issues for early intervention. Nurses also regularly review risks of alert and patients’ health conditions and engage with the patients and their care teams. To evaluate this care model, a multicentre RCT is being conducted. The trial started in Jan 2015, and is expected to be completed in Dec 2017. Patients with CHF (n=300) will be recruited from Victoria and Western Australia. Patients consented will be randomized to receive either the enhanced care (n=150) or usual care (n=150) for a minimum of 6 months. All the patients will be assessed at baseline, 6-month, and 12-month (if available) points. The aim of the enhanced model of care is to improve adherence (compliance) to the CHF management including daily weight monitoring, as well as health outcomes (health related quality of life, risk factors, functional capacity, frailty, and psychological states), and economic outcomes (risks of hospital readmissions, GP/ED visits, and cost benefits). Effective management of CHF in primary care remains a clinical challenge globally. This project will evaluate the effectiveness of an innovative telemonitoring enhanced home care model to assist patients in managing CHF. The evaluation will provide clinical evidence to integrate telemonitoring innovations to business as usual healthcare services for improvement of CHF care. At present over 80 patients have been recruited for the RCT form a large group of screened patients. Recruitment will continue until mid-2017 to capture close as possible to the trial target of 300 patients. Early results from the trial will be presented including clinical trial protocol, trial design, recruitment, challenges and compliance.

Quantifying the Effect of Data Quality on the Validity of an eMeasure.

Objective The objective of this study was to demonstrate the utility of a healthcare data quality framework by using it to measure the impact of synthetic data quality issues on the validity of an eMeasure (CMS178—urinary catheter removal after surgery). Methods Data quality issues were artificially created by systematically degrading the underlying quality of EHR data using two methods: independent and correlated degradation. A linear model that describes the change in the events included in the eMeasure quantifies the impact of each data quality issue. Results Catheter duration had the most impact on the CMS178 eMeasure with every 1% reduction in data quality causing a 1.21% increase in the number of missing events. For birth date and admission type, every 1% reduction in data quality resulted in a 1% increase in missing events. Conclusion This research demonstrated that the impact of data quality issues can be quantified using a generalized process and that the CMS178 eMeasure, as currently defined, may not measure how well an organization is meeting the intended best practice goal. Secondary use of EHR data is warranted only if the data are of sufficient quality. The assessment approach described in this study demonstrates how the impact of data quality issues on an eMeasure can be quantified and the approach can be generalized for other data analysis tasks. Healthcare organizations can prioritize data quality improvement efforts to focus on the areas that will have the most impact on validity and assess whether the values that are reported should be trusted.

Efficient Alignment Between Event Logs and Process Models

The aligning of event logs with process models is of great significance for process mining to enable conformance checking, process enhancement, performance analysis, and trace repairing. Since process models are increasingly complex and event logs may deviate from process models by exhibiting redundant, missing, and dislocated events, it is challenging to determine the optimal alignment for each event sequence in the log, as this problem is NP-hard. Existing approaches utilize the cost-based A* algorithm to address this problem. However, scalability is often not considered, which is especially important when dealing with industrial-sized problems. In this paper, by taking advantage of the structural and behavioral features of process models, we present an efficient approach which leverages effective heuristics and trace replaying to significantly reduce the overall search space for seeking the optimal alignment. We employ real-world business processes and their traces to evaluate the proposed approach. Experimental results demonstrate that our approach works well in most cases, and that it outperforms the state-of-the-art approach by up to 5 orders of magnitude in runtime efficiency.