LayerLSH: Rebuilding Locality-Sensitive Hashing Indices by Exploring Density of Hash Values

Ding, Jun; Liu, Zhuojin; Zhang, Yanfeng; Gong, Shufeng; Yu, Ge

doi:10.1109/access.2022.3182802

Cited by 1 publication

(1 citation statement)

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“…In addition to using a Log‐Structured Merge (LSM) tree, KVRangeDB, 19 on the other hand, reduces compaction I/O and merges keys to optimize performance, which is essential for managing large data sets. LayerLSH 20 improves the scalability and accuracy of LSH by partitioning data points into multiple layers and applying different LSH functions to each layer, which is useful for high‐dimensional data. The new hash index structure minimizes the number of write operations required by traditional hash index structures, making it well‐suited for flash‐based storage systems 21 .…”

Section: Related Workmentioning

confidence: 99%

Anomalous vehicle recognition in smart cities using persistent bloom filter: Memory efficient and intelligent monitoring

Kumar,

Singh

2023

Trans Emerging Tel Tech

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Globally, there is a rapid and significant change in the distribution of the urban population. Concerning which there is an increase in traffic rule violations in smart cities, resulting in traffic congestion and accidents. Anomalous vehicle recognition in smart cities is a critical issue that demands prompt and real‐time responsive solutions to address such situations. This article proposes an effective technique utilizing a persistent bloom filter to store and retrieve vehicle data, to improve road safety in urban locations. The technique employs distinct bloom filters to store vehicle data based on the hour, day, month, and year, which enables effective temporal membership queries on vast data. The proposed approach of anomalous vehicle recognition in smart cities using a persistent bloom filter is a memory‐efficient and intelligent monitoring technique. According to experimental analysis on large datasets such as “Montgomery County 911 Calls", “EMS and Fire Calls", “Seattle Fire Dept: 911", and “Traffic Violations in Montgomery County", the approach has the potential to boost query efficiency by up to 80% in comparison to conventional methods, in addition to decreasing storage space demands by up to 60%. The proposed approach has the potential to bridge the gap between research and practice in the field of urban safety and to enhance road safety while considering the dynamic changes in the road network. Employing traditional approaches to detect violations and monitor real‐time changes in a vast dataset of vehicles can be a laborious undertaking. The proposed technique provides a reliable and efficient approach to handling big data and improving traffic safety in smart cities.

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Section: Related Workmentioning

confidence: 99%