Abstract

Data stream is a new data model that has recently attracted attentions in numerous applications. Considering the continuity, limitlessness of streaming data, this paper proposes a novel method to build a synopsis data structure, Nord_Histogram, for storing streaming data summary and a one-pass approximate algorithm, NHQC, for quantile computation. The algorithm implements quantile queries over data stream with the time and space requirements being linear with the number of buckets, beating several previous synopsis structure and algorithms in terms of time and space costs which grow at least logarithmically with the length of data stream. The correlation between computation error and main memory requirement is also analyzed. Experiment results show the good performance of NHQC with desirable accuracy and efficient time and space requirements.

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 call

Disclaimer: 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.