Abstract
The analysis and integration of big data highlight some issues in the identification and resolution of data inconsistencies and knowledge incompleteness. This paper presents an overview of data inconsistencies and a review of approaches to resolve various levels of data inconsistencies. Moreover, we discuss some issues related to incompleteness and stability of known knowledge over specific time periods, and the implication for the decision-making process. In addition, the use of a Bayesian network model in inconsistency resolution in data analysis and knowledge engineering will also be considered.
| Original language | English |
|---|---|
| Pages (from-to) | 714-724 |
| Number of pages | 11 |
| Journal | International Journal of Grid and Utility Computing |
| Volume | 11 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2 Oct 2020 |
Keywords
- Bayesian networks
- Big data
- Data inconsistencies
- NLP
- Brute force scheduling algorithm
- Greedy task scheduling algorithm
- Partial dependency
- Turnaround time
- Random task graph
- Task scheduling
- Fragmentation
- Grid utilisation
- Standard task graphs
- Computational grids
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