A Graph Algebra for Scalable Visual Analytics


Visual analytics (VA), which combines analytical techniques with advanced visualization features, is fast turning into a normal tool for extracting information from graph information. Researchers have developed many tools for this purpose, suggesting a would like for formal methods to guide these tools' creation. Increased information demands on computing requires redesigning VA tools to contemplate performance and reliability in the context of research of exascale datasets. Furthermore, visual analysts want a manner to document their analyses for reuse and results justification. A VA graph framework encapsulated in a very graph algebra helps address these wants. Its atomic operators embrace choice and aggregation. The framework employs a visible operator and supports dynamic attributes of information to enable scalable visual exploration of knowledge.

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