Modeling, Querying, and Mining Uncertain XML Data

Kharlamov, Evgeny; Senellart, Pierre

doi:10.4018/978-1-4666-2455-9.ch034

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…in probabilistic XML documents. Kharlamov and Senellart [40] discuss how some mining tasks can be answered using techniques of probabilistic XML querying. Nevertheless, it is to be explored whether other techniques (e.g., based on ordinary frequent itemset discovery) can provide more effective mining.…”

Section: Open Problemsmentioning

confidence: 99%

Probabilistic XML: Models and Complexity

Kimelfeld

Senellart

2013

Advances in Probabilistic Databases for Uncertain Information Management

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Abstract. Uncertainty in data naturally arises in various applications, such as data integration and Web information extraction. Probabilistic XML is one of the concepts that have been proposed to model and manage various kinds of uncertain data. In essence, a probabilistic XML document is a compact representation of a probability distribution over ordinary XML documents. Various models of probabilistic XML provide different languages, with various degrees of expressiveness, for such compact representations. Beyond representation, probabilistic XML systems are expected to support data management in a way that properly reflects the uncertainty. For instance, query evaluation entails probabilistic inference, and update operations need to properly change the entire probability space. Efficiently and effectively accomplishing data-management tasks in that manner is a major technical challenge. This chapter reviews the literature on probabilistic XML. Specifically, this chapter discusses the probabilistic XML models that have been proposed, and the complexity of query evaluation therein. Also discussed are other data-management tasks like updates and compression, as well as systemic and implementation aspects.

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Section: Open Problemsmentioning

confidence: 99%

Probabilistic XML: Models and Complexity

Kimelfeld

Senellart

2013

Advances in Probabilistic Databases for Uncertain Information Management

Self Cite

View full text Add to dashboard Cite

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Use of attribute association error probability estimates to evaluate quality of medical record geocodes

Klaus¹,

Carrasco²,

Goldberg

et al. 2015

Int J Health Geogr

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BackgroundThe utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends.MethodsA series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research.ResultsWe present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations.ConclusionsThe effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics.

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Modeling, Querying, and Mining Uncertain XML Data

Cited by 2 publications

References 47 publications

Probabilistic XML: Models and Complexity

Probabilistic XML: Models and Complexity

Use of attribute association error probability estimates to evaluate quality of medical record geocodes

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