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This article focuses on testing a path-oriented querying approach to hierarchical data in relational databases. The authors execute a user study to compare the path-oriented approach and traditional SQL from two perspectives: correctness of queries and time spent in querying. They also analyze what kinds of errors are typical in path-oriented SQL. Path-oriented query languages are popular in the context of object-orientation and XML. However, relational databases are the most common paradigm for storing data and SQL is most common for manipulating data. When querying hierarchical data in SQL, the user must specify join conditions explicitly between hierarchy levels. Path-oriented SQL is a new alternative for expressing hierarchical queries in relational databases. In the authors' study, the users spent significantly less time in writing path-oriented SQL queries and made fewer errors in query formulation. [ABSTRACT FROM AUTHOR]

In this study, we examine the applicability of association rules for analysing high-dimensional data concerning age-related hearing impairment (ARHI). The ARHI data of the study contain hundreds of variables concerning phenotype, genotype and environmental factors. The number of association rules produced from the data is too large for manual exploration in the raw and furthermore, the rules are overlapping. Thus, the focus of our study is to develop an approach to cluster association rules into subsets and to summarise and represent the found rule subsets for easier exploration of rules. The results show that it is possible to efficiently extract rules representing interesting environmental factor-gene or gene-gene interactions. Finding suitable parameters for the association rule mining and the possibility to post-process the mined rules is essential. The developed approach facilitates rule exploration by grouping rules with items concerning the same phenomenon to the same subset and byrevealing overlapping rules. [ABSTRACT FROM AUTHOR]

We studied how the splitting of a multi-class classification problem into multiple binary classification tasks, like One-vs-One (OVO) and One-vs-All (OVA), affects the predictive accuracy of disease classes. Classifiers were tested with an otoneurological data using 10-fold cross-validation 10 times with k-Nearest Neighbour (k-NN) method and Support Vector Machines (SVM). The results showed that the use of multiple binary classifiers improves the classification accuracies of disease classes compared to one multi-class classifier. In general, OVO classifiers worked out better with this data than OVA classifiers. Especially, the OVO with k-NN yielded the highest total classification accuracies. [ABSTRACT FROM AUTHOR]