Graph databases underlie several modern applications such as social networks and the Semantic Web. In those scenarios, integrating and exchanging data is very common, which leads to proliferation of incomplete graph data. However, the well developed models of incompleteness of data do not apply to graph data. This is mainly due to the fact that standard graph query languages concentrate on grap...

Model selection in complete data is a common task for the applied researcher. However, in many scenarios data are incomplete which further complicates the task of model selection. In this talk, we will specify the problem of model selection in incomplete data and discuss several possible solutions using multiple imputation. First, we will define a new general measure for the correct model selec...

Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology evaluating the association between these data. The existing models, however, have not sufficiently addressed problem of missing data, which are commonly encountered in studies. In this paper, we introduce novel joint model with shared random effects incomplete Our propos...

Although the survival benefit of complete revascularization after bypass surgery is well documented, the importance of opening all stenotic or occluded vessels during percutaneous coronary intervention (PCI) is less certain. On the contrary, much data support targeting only the culprit vessel during PCI. A strategy of “ischemic-driven revascularization” is often the standard of care. Through a ...

Information integration is the task of aggregating data from multiple heterogeneous data sources. The understandings of semantics and context knowledge of data sources are often the keys to challenging problems in information integration such as schema alignments and inconsistency resolution. Context logic provides a unified framework for the modeling of data sources; nevertheless, the acquisit...

Mining frequent patterns is essential in many data mining methods. Frequent patterns lead to the discovery of association rules, strong rules, sequential episodes, and multi-dimensional patterns. Patterns should be discovered in a time and space efficient manner. Vertical mining algorithms key advantage is that they can outperform their horizontal counterparts in terms of both time and space ef...

We give explicit analytical formulas for finding a signal with the known compact support from its spectrum known on a finite frequency band.

In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classificat...