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This approach represents ontology-based data integration. On the other hand, the problem of combining research results from different bioinformatics repositories requires bench-marking of the similarities, computed from different data sources, on a single criterion such as positive predictive value.
There are three main architectures that are implemented in ontology‑based data integration applications, [1] namely, Single ontology approach A single ontology is used as a global reference model in the system. This is the simplest approach as it can be simulated by other approaches. [1] SIMS [6] a prominent example
Underpinning this pedagogical approach is the data - information - knowledge paradigm (see DIKW pyramid). It is informed by a pattern-based approach to grammar and vocabulary, and a lexicogrammatical approach to language in general. Thus the basic task in DDL is to identify patterns at all levels of language.
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]
While data-driven design does prevent coupling of data and functionality, in some cases, data-driven programming has been argued to lead to bad object-oriented design, especially when dealing with more abstract data. This is because a purely data-driven object or entity is defined by the way it is represented. Any attempt to change the ...
Data-driven instruction is an educational approach that relies on information to inform teaching and learning. The idea refers to a method teachers use to improve instruction by looking at the information they have about their students.
A conceptual data model is developed based on the data requirements ... Information technology engineering is a methodology that embraces this approach. Data ...
Data-centric computing. Data-centric computing is an approach that merges innovative hardware and software to treat data, not applications, as the permanent source of value. [8] Data-centric computing aims to rethink both hardware and software to extract as much value as possible from existing and new data sources.
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