PROJECT TITLE :

A Graph Algebra for Scalable Visual Analytics

ABSTRACT:

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.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Graph Attention Spatial-Temporal Network with Collaborative Global-Local Learning for Citywide Mobile Traffic Prediction ABSTRACT: It is becoming increasingly important for proactive network service provisioning
PROJECT TITLE : mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding ABSTRACT: As a result of the fact that heterogeneous information networks (HIN) contain nodes and edges that
PROJECT TITLE : Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting ABSTRACT: It is essential to have accurate traffic forecasting in order to improve the safety, stability, and overall effectiveness
PROJECT TITLE : Graph Neural Network for Fraud Detection via Spatial-temporal Attention ABSTRACT: Card fraud is a significant problem that results in significant financial losses for cardholders as well as the banks that issue
PROJECT TITLE : Oversampled Graph Laplacian Matrix for Graph Filter Banks ABSTRACT: Using an oversampled graph Laplacian matrix, we describe a method for oversampling signals that are defined on a weighted graph. Because of the

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry