Abstract
Massive scale data streaming is now prevalent and can be used to dynamically build large graphs which are then efficiently analyzable for insightful information. In situations where real-time analytics is required approximate outcomes within time bounds may be desirable. We have identified graph summarization and TCM sketching in particular as a good technique for graph summarization for streaming data. TCM sketching provides a set of metrics such as Average Relative Error, Number of Effective Queries, Effectiveness of Effective Queries and Confusion Matrix of queries on streaming graphs. We then propose extensions to the TCM model for automatic sketch creation while the graph is being constructed and evaluate the approach with different sketch creation policies and query combinations. The proposed query framework works well with streaming graphs with 80% to 90% query efficiency and ±3 deviations from exact results.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
