Maximum Physically Consistent Trajectories

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

Practical Dynamic Extension for Sampling Indexes

Rumbaugh,

Xie

2023

Proc. ACM Manag. Data

View full text Add to dashboard Cite

The execution of analytical queries on massive datasets presents challenges due to long response times and high computational costs. As a result, the analysis of representative samples of data has emerged as an attractive alternative; this avoids the cost of processing queries against the entire dataset, while still producing statistically valid results. Unfortunately, the sampling techniques in common use sacrifice either sample quality or performance, and so are poorly suited for this task. However, it is possible to build high quality sample sets efficiently with the assistance of indexes. This introduces a new challenge: real-world data is subject to continuous update, and so the indexes must be kept up to date. This is difficult, because existing sampling indexes present a dichotomy; efficient sampling indexes are difficult to update, while easily updatable indexes have poor sampling performance. This paper seeks to address this gap by proposing a general and practical framework for extending most sampling indexes with efficient update support, based on splitting indexes into smaller shards, combined with a systematic approach to the periodic reconstruction. The framework's design space is examined, with an eye towards exploring trade-offs between update performance, sampling performance, and memory usage. Three existing static sampling indexes are extended using this framework to support updates, and the generalization of the framework to concurrent operations and larger-than-memory data is discussed. Through a comprehensive suite of benchmarks, the extended indexes are shown to match or exceed the update throughput of state-of-the-art dynamic baselines, while presenting significant improvements in sampling latency.

show abstract

Maximum Physically Consistent Trajectories

Cited by 3 publications

References 20 publications

Noise-Tolerant Trajectory Distance Computation in the Presence of Inherent Noise for Video Surveillance Applications

Noise-Tolerant Trajectory Distance Computation in the Presence of Inherent Noise for Video Surveillance Applications

Understanding Movement in Context with Heterogeneous Data

Practical Dynamic Extension for Sampling Indexes

Contact Info

Product

Resources

About