Adaptive Retrieval Time-Related Data Model for Tracking Factors Affecting Diabetes

In the last four decades several dozens of representing time-oriented data/knowledge bases have been presented. Some of these representations violate First Normal Form (1NF) by using Non-First Normal Form (N1NF) prototypes and temporal nested representations, while others simulated the concepts of temporal data with relational data representation without violating 1NF. In this article, a new interval-based knowledge representational data model with an optimized retrieval techniques are employed for modeling and optimality retrieve a biomedical time-varying data (factors/observations that affect the diabetes). The used time-related data model is more compact to represent time-varying data with less memory (capacity) storage with respect to the main representations in the literature, but which is as expressive as those representations (a transformation algorithms show that data represented in this model can be transferred to/from the representations in the literature with zero percent loss of information). A new data structure is defined with the optimal retrieval techniques to prove some basic properties of the used time-model and to ensure that the time-model is an extension and reduction of the main representations in the literature, namely TQuel and BCDM. The expressive power, reducibility, and easy implementation of the proposed model, especially for the legacy systems, are considered as advantages of the proposed model.

@article{AlBidewi2020,
  title     = {Adaptive Retrieval Time-Related Data Model for Tracking Factors Affecting Diabetes},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {12},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Ibrahim AlBidewi and Nashwan Alromema and Fahad Alotaibi},
  doi       = {10.14569/IJACSA.2020.0111252},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111252}
}