Inference from hospital patient records is difficult because data collection is done at arbitrary (not evenlyspaced) time intervals, and key clinical information is recorded only in unstructured form (as free text in doctors’ notes). We present remind, a framework for performing inference from patient records based upon continuous-time Markov models and Bayesian networks. We empirically justify...

Trend analysis of time series is an important problem since trend identification enables the prediction of the near future. In streaming time series the problem is more challenging due to the dynamic nature of the data. In this paper, we propose a method to continuously clustering a number of streaming time series based on their trend characteristics. Each streaming time series is transformed t...

Real data often contains a mixture of discrete and continuous variables, but many Bayesian network structure learning and inference algorithms assume all random variables are discrete. Continuous variables are often discretized, but the choice of discretization policy has significant impact on the accuracy, speed, and interpretability of the resulting models. This paper introduces a principled ...

We present a method for assessing categorical perception from continuous discrimination data. Until recently, categorical perception of speech has exclusively been measured by discrimination and identification experiments with a small number of repeatedly presented stimuli. Experiments by Rogers and Davis [1] have shown that using non-repeating stimuli along a densely-sampled phonetic continuum...

Grouped data occur frequently in practice, either because of limited resolution of instruments, or because data have been summarized in relatively wide bins. A combination of the composite link model with roughness penalties is proposed to estimate smooth densities from such data in a Bayesian framework. A simulation study is used to evaluate the performances of the strategy in the estimation o...

We study processing and authentication of long-running queries on outsourced data streams. In this scenario, a data owner (DO) constantly transmits its data to a service provider (SP), together with additional authentication information. Clients register continuous range queries to the SP. Whenever the data change, the SP must update the results of all affected queries and inform the clients ac...

This paper presents novel methodologies for the analysis of continuous cellular tower data from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of existing community detection methodologies to identify salient locations based on the network generated by tower transitions. The tower groupings from these unsupervised clustering techniques are subsequently validat...

In this study we present a scheme called two-dimensional BubbleUp (2DB) to manage parallel disks for continuous media data. Its goal is to reduce initial latency for interactive multimedia applications, while balancing disk loads to maintain high throughput. The 2DB scheme consists of a data placement and a request scheduling policy. The data placement policy replicates frequently accessed data...

context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...

Two major issues for continuous data delivery architectures on the Internet are scalabil-ity and data continuity. Scalability is addressed by providing multiple redundant stream sources. One solution to the data continuity problem is adaptive client migration. Adaptive client migration is the ability of a client to switch from one redundant stream source to another when it experiences discontin...