Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 49 tok/s
Gemini 2.5 Pro 53 tok/s Pro
GPT-5 Medium 19 tok/s Pro
GPT-5 High 16 tok/s Pro
GPT-4o 103 tok/s Pro
Kimi K2 172 tok/s Pro
GPT OSS 120B 472 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

INSQ: An Influential Neighbor Set Based Moving kNN Query Processing System (1602.00363v3)

Published 1 Feb 2016 in cs.DB

Abstract: We revisit the moving k nearest neighbor (MkNN) query, which computes one's k nearest neighbor set and maintains it while at move. Existing MkNN algorithms are mostly safe region based, which lack efficiency due to either computing small safe regions with a high recomputation frequency or computing larger safe regions but with a high cost for each computation. In this demonstration, we showcase a system named INSQ that adopts a novel algorithm called the Influential Neighbor Set (INS) algorithm to process the MkNN query in both two-dimensional Euclidean space and road networks. This algorithm uses a small set of safe guarding objects instead of safe regions. As long as the the current k nearest neighbors are closer to the query object than the safe guarding objects are, the current k nearest neighbors stay valid and no recomputation is required. Meanwhile, the region defined by the safe guarding objects is the largest possible safe region. This means that the recomputation frequency is also minimized and hence, the INS algorithm achieves high overall query processing efficiency.

Citations (5)

Summary

We haven't generated a summary for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.