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Summary: Data structure description, conceptual modeling, and logic reasoning for knowledge discovery are three critical factors for the integration of information with heterogeneity. In particular, technologies of NoSQL databases and Internet of Things raise an urgent requirement for a uniform expression of heterogeneous data, and little attention has been paid to researches on the integration of NoSQL databases with traditional data models, as well as the semantic description of big data. To tackle these problems, in this paper, a concept‐and‐relation‐oriented grid data model called GODM model is first proposed based on the definitions of Monad, Compounder, Relation, etc. Then, the GODM model is utilized to uniformly describe traditional data models and NoSQL data models, which eliminates structure differences of heterogeneous data. Next, based on the GODM relation mechanism, an extendable semantic system is built up by choosing SHOIQ(D) description logic as the example to establish the correspondence with GODM grammar subset, providing a fundamental support for semantic integration and knowledge discovery of heterogeneous data. After that, comprehensive comparisons with GODM and other models are made, especially the distinctions between GODM and OWL on the aspects of relation mechanism, hybrid schema, description logic, grammatical constructors, etc. Besides, experimental evaluations and analyses on time and space efficiencies of some primary common data models are conducted after the proposal of a general evaluation model, with the results showing that the GODM model has great advantage on properties of expressiveness, flexibility, etc, particularly time and space efficiency. In summary, the GODM model describes heterogeneous data from both aspects of data structure and semantic relationship and realizes a hybrid schema reconciling the schemaful and schemaless data models, making it especially suitable for dynamic data integration and knowledge discovery from big data models. [ABSTRACT FROM AUTHOR]

This paper presents a novel list-based scheduling algorithm called Improved Predict Earliest Finish Time for static task scheduling in a heterogeneous computing environment. The algorithm calculates the task priority with a pessimistic cost table, implements the feature prediction with a critical node cost table, and assigns the best processor for the node that has at least 1 immediate successor as the critical node, thereby effectively reducing the schedule makespan without increasing the algorithm time complexity. Experiments regarding aspects of randomly generated graphs and real-world application graphs are performed, and comparisons are made based on the scheduling length ratio, robustness, and frequency of the best result. The results demonstrate that the Improved Predict Earliest Finish Time algorithm outperforms the Predict Earliest Finish Time and Heterogeneous Earliest Finish Time algorithms in terms of the schedule length ratio, frequency of the best result, and robustness while maintaining the same time complexity. [ABSTRACT FROM AUTHOR]