Mining Abstract XML Data-Types

In XML Data Integration, data/metadata merging and query processing are indispensable. Specifically, merging integrates multiple disparate (heterogeneous and autonomous) input data sources together for further usage, while query processing is one main reason why the data need to be integrated in the first place. Besides, when supported with appropriate user feedback techniques, queries can also provide contexts in which conflicts among the input sources can be interpreted and resolved. The flexibility of XML structure provides opportunities for alleviating some of the difficulties that other less flexible data types face in the presence of uncertainty; yet, this flexibility also introduces new challenges in merging multiple sources and query processing over integrated data. In this chapter, the authors discuss two alternative ways XML data/schema can be integrated: conflict-eliminating (where the result is cleaned from any conflicts that the different sources might have with each other) and conflict-preserving (where the resulting XML data or XML schema captures the alternative interpretations of the data). They also present techniques for query processing over integrated, possibly imprecise, XML data, and cover strategies that can be used for resolving underlying conflicts.

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D-Cinema — XML Data Types — Amendment 1

10.5594/smpte.st433.2008am1.2011 ◽

2011 ◽

Keyword(s):

Data Types ◽

Xml Data

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D-Cinema — XML Data Types

10.5594/smpte.st433.2008 ◽

2008 ◽

Keyword(s):

Data Types ◽

Xml Data

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Digital cinema (D-cinema). XML Data types

10.3403/30191002u ◽

2015 ◽

Keyword(s):

Data Types ◽

Xml Data ◽

Digital Cinema

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Intelligent Search on XML Data

10.1007/978-3-540-45194-5 ◽

2003 ◽

Cited By ~ 9

Keyword(s):

Xml Data ◽

Intelligent Search

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Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

10.31234/osf.io/54cj3 ◽

2018 ◽

Author(s):

Prathiba Natesan ◽

Smita Mehta

Keyword(s):

Behavioral Intervention ◽

Effect Size ◽

Rate Ratio ◽

Visual Analysis ◽

Single Case ◽

Small Sample ◽

Statistical Analyses ◽

Data Types ◽

Ratio Effect ◽

Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

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