Accounting for Item Variance in Large-scale Databases

Accounting for Item Variance in Large-scale Databases

ard analysis of variance (ANOVA) of the database. This method is fast, accurate, and it provides suitable confidence limits for the ICC estimate. The other method is of Monte Carlo type. It is based on a permutation resampling procedure, which is computationally more demanding and more sensitive to missing data than the ANOVA method. However, this approach is distribution free and much more fle...

Probabilistic Reasoning for Large Scale Databases

The complexity of probabilistic reasoning prohibits its application on a large scale of data. In order to reduce the complexity, implementations of modeling approaches restrict themselves with respect to expressive power or relax on the underlying probability theory. We present the implementation aspects of a probabilistic extension of stratified Datalog. This probabilistic deductive system is ...

Clustering Algorithm for Large-Scale Databases

Clustering systems can discover intentional structures in data and extract new knowledge from a database. Many incremental and non-incremental clustering algorithms have been proposed, but they have some problems. Incremental algorithms work very efficiently, but their performance is strongly affected by the input order of instances. On the other hand, non-incremental algorithms are independent...

DynPart: Dynamic Partitioning for Large-Scale Databases

Efficient Name Disambiguation for Large-Scale Databases

Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters p...