Concept-Oriented Model

In order to represent and analyze data from different data sources a unified model is needed. One such model, called the concept-oriented model (COM), is described in this article. COM (2009a) is a general purpose unified data model aimed at describing many existing views and patterns of thoughts currently used in data modeling. As a unified model, its main goal is to significantly decrease differences and incongruities between various approaches to data modeling such as transactional, analytical (Savinov, 2011b), multidimensional (Savinov, 2005a), object-oriented (Savinov, 2011a), conceptual and semantic (Savinov, 2012c). The motivation behind COM and its practical benefits are similar to those for the Business Intelligence Semantic Model (BISM) (Russo, Ferrari, & Webb, 2012) introduced in Microsoft SQL Server 2012. Like BISM, COM also aims at creating a unified model for all user experiences by significantly simplifying typical analysis tasks and facilitating self-service analytics. The creation of this model was motivated by the following problems which are difficult to solve within traditional approaches: Domain-specific identities. Identification in most existing models is based on two approaches: primitive references like oids or surrogates, and identifying properties like primary keys. These approaches do not provide a mechanism for defining strong domain-specific identities with arbitrary structure, and the focus in existing models is made on describing entities. The goal of COM is to make identities and entities equal elements of the model both having some structure. Hierarchical address spaces. Most models focus on describing properties of elements without providing containment relation as a basic construct. A typical solution consists in modeling spaces and containment like any other domain-specific relationship. The goal of COM is to model containment as a core notion of the model by assuming that elements cannot exist outside of any space, domain or context. Multidimensionality. There exist numerous approaches to multidimensional modeling which are intended for analytical processing. The problem is that analytical and transactional models rely on different assumptions and techniques. The goal of COM in this context is to rethink dimensions as a first-class construct of the data model which plays a primary role for describing both transactional and analytical aspects. Semantics. Existing models represent data semantics separately from data itself. Normally semantics is supported at the level of conceptual model or using some semantic model. Logical data models have rather limited possibilities for representing semantic relationships and it is a strong limiting factor for the effective use of data. The goal of COM here is to make semantics integral part of the logical data model so that the database can be directly used for reasoning about data.