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

Abstract

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.Index Terms-Ontology, digital ecosystems, social behavioural patterns, health and medicine.4th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2010)

Digital ecosystems

“…According to various definitions of Digital Ecosystems, different types of Digital Ecosystems have been proposed and studied in various research projects and initiatives [19][20][21][22][23][24][25][26][27][28][29][30][31]. In this section, we discuss different categories of Digital Ecosystems and their applications from five perspectives, namely business, knowledge management, service, social networks and education.…”

“…This is a progress beyond composing independent services to fulfill complex tasks. In [27] the multi-dimensional ontology is proposed to model Human Digital Ecosystems affected by social behavioral data patterns. This is helpful in understanding human situations, behavioral patterns and social ecology.…”

On Robust Methodologies for Managing Public Health Care Systems

“…In this mapping process, diabetic clinical data, including results (data instances) are made connected through multiple domain ontologies in horizontally, laterally and vertically varying dimensions. In another procedure [ 12 , 11 ], such clinical tool measurements obtained, grouped across profiles ( line and region -profiles) of patients (at both local and global perspectives) and their associated food domain ontologies are connected and integrated, as described and narrated in different schemas in the following sections.…”

Surgery-Guided Removal of Ovarian Cancer Using Up-Converting Nanoparticles

“…Surgery is critical to patient outcomes with survival linked to the degree of tumor removed from the abdomen. [5][6][7][8][9] Currently, surgeons remove tumors by direct visual inspection and can generally remove tumors >0.5 cm in diameter; however smaller tumors can be very challenging to detect and remove. Therefore, there is a clinical need for more precise, real-time detection techniques during surgery that enable surgeons to identify and remove smaller tumors they would otherwise miss.…”

“…The most clinically advanced strategy for fluorescent detection of ovarian tumors (but not yet approved) is conjugating a fluorescent dye to folate which binds to folate receptors that are highly expressed in ovarian cancer cells. 8,10,11 However, over-expression of this receptor varies widely between different tumors, and it is present in lymph nodes and other normal tissue, leading to false-positive signals. [11][12][13][14][15] Some efforts to lower false-positive signals involve using near-infrared (NIR) fluorophores.…”