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

    138263_138263.pdf (554.8Kb)
    Access Status
    Open access
    Authors
    Nimmagadda, Shastri
    Nimmagadda, Sashi
    Dreher, Heinz
    Date
    2010
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Nimmagadda, Shastri and Nimmagadda, Sashi and Dreher, Heinz. 2010. Multidimensional ontology modeling of human digital ecosystems affected by social behavioural data patterns, in Hussain, F. K. and Chang, E. (ed), 4th IEEE international conference on digital ecosystems and technologies, Apr 12 2010, pp. 498-503. Dubai, United Arab Emirates: IEEE.
    Source Title
    Proceedings of the 4th IEEE international conference on digital ecosystems and technologies
    Source Conference
    4th IEEE international conference on digital ecosystems and technologies
    ISBN
    9781424455539
    Faculty
    Curtin Business School
    School of Information Systems
    Remarks

    Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/6348
    Collection
    • Curtin Research Publications
    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 unknownrelationships 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.

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