Multidimensional ontology modeling of human digital ecosystems affected by social behavioural data patterns

Relational and hierarchical data modeling studies are carried out, using simple and explicit comparison based ontology. The comparison is basically performed on relationally and hierarchically structured data entities/dimensions. This methodology is adopted to understand the human ecosystem that is affected by human behavioural and social disorder data patterns. For example, the comparison may be made among human systems, which could be between male and female, fat and slim, disabled and normal (physical impairment), again normal and abnormal (psychological), smokers and non-smokers and among different age group domains. There could be different hierarchies among which, different super-type dimensions are conceptualized into several subtype dimensions and integrated them by connecting the interrelated several common data attributes. Domain ontologies are built based on the known-knowledge mining and thus unknown relationships are modeled that are affected by social behaviour data patterns. This study is useful in understanding human situations, behavioral patterns and social ecology that can facilitate health and medical practitioners, social workers and psychologists, while treating their patients and clients.