Modeling sparse data such as microbiome and transcriptomics (RNA-seq) is very challenging due to the exceeded number of zeros skewness distribution. Many probabilistic models have been used for modeling data, including Poisson, negative binomial, zero-inflated binomial models. One way identify most appropriate or hurdle based on p-value Kolmogorov-Smirnov (KS) test. The main challenge identifyi...

This paper describes the conceptual integration and computer-based support of two important groups of conceptual data models, Entity Relationship Models and Object Role Models (e.g. NIAM). We perform conceptual integration using the conceptual data modelling language CoCoA to specify separate data models of individual notations. We then merge these into an integrated conceptual data model for b...

We propose a methodology for providing clear and consistent integration of the process and data logic in the analysis stage of information systems’ development lifecycle. While our proposed approach is applicable across a variety of data and process modeling schemas, in this paper we discuss it in the context of UML use cases for process modeling and ER diagrams for data modeling. We illustrate...

The design of business processes involves the usage of modeling languages, tools and methodologies. In this paper we highlight and address a relevant limitation of the Business Process Modeling Notation (BPMN): its weak data representation capabilities. In particular, we extend it with data-specific constructs derived from existing data modeling notations and adapted to blend gracefully into BP...