efficient algorithms to monitor continuous constrained k nearest neighbor queries
DESCRIPTION
Efficient Algorithms to Monitor Continuous Constrained k Nearest Neighbor Queries. Presented by: Mahady Hasan Joint work with Muhammad Aamir Cheema , Wenyu Qu, Xuemin Lin. University of New South Wales, Australia. Outline of the Presentation. Introduction Related Work (Motivation) - PowerPoint PPT PresentationTRANSCRIPT
Efficient Algorithms to Monitor Continuous Constrained k Nearest Neighbor Queries
Presented by: Mahady Hasan
Joint work withMuhammad Aamir Cheema, Wenyu Qu, Xuemin Lin
University of New South Wales, Australia
Wednesday, April 19, 2023 Presented by: Mahady Hasan2
Outline of the Presentation
• Introduction
• Related Work (Motivation)
• GridTree Approach
• ArcTrip Approach
• Continuous monitoring
• Experiments
• Conclusion
Wednesday, April 19, 2023 Presented by: Mahady Hasan3
What is NN?
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Finding kNN objects. Let k=3Finding contrained kNN objects. Let k=3
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What is Constrained NN?
Wednesday, April 19, 2023 Presented by: Mahady Hasan4
Outline of the Presentation
• Introduction
• Related Work (Motivation)
• GridTree Approach
• ArcTrip Approach
• Continuous monitoring
• Experiments
• Conclusion
Wednesday, April 19, 2023 Presented by: Mahady Hasan5
Related works
• Constrained kNN queries:– H. Ferhatosmanoglu et al. first introduce the
constrained kNN queries (SSTD 2001). – Gao et. al find k-nearest trajectories in a
constrained region. (DASFAA 2008)
• Continuous k NN queries:– YPK-CNN( Yu et al. ICDE 2005)– SEA-CNN (Xiong et al. ICDE 2005)– CPM (Mouratidis et al. SIGMOD 2005)
Wednesday, April 19, 2023 Presented by: Mahady Hasan6
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Related work: CPM
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Finding one Nearest Neighbor
We check an cell or entry before we insert in the heap that it intersects with the given constrained region or not.
Wednesday, April 19, 2023 Presented by: Mahady Hasan7
Motivation
We have observed that in case of our problem setting CPM needs to check lots of cells before it inserts the cell in the cellin the heap.
So CPM becomes expensive in terms of computational time.
At the same time CPM needs more space to store the heap and visit lists to updatethe data efficiently.
So we use some other access methods that are more naturalwith our problem setting
Wednesday, April 19, 2023 Presented by: Mahady Hasan8
Outline of the Presentation
• Introduction
• Related Work (Motivation)
• GridTree Approach
• ArcTrip Approach
• Continuous monitoring
• Experiments
• Conclusion
Wednesday, April 19, 2023 Presented by: Mahady Hasan9
Concept of Grid-Tree Structure
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Intermediate Entries
Grid Cells
Wednesday, April 19, 2023 Presented by: Mahady Hasan10
Grid-Tree based NN search algorithm
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Wednesday, April 19, 2023 Presented by: Mahady Hasan11
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Grid-Tree Based constrained NN Algorithm
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Wednesday, April 19, 2023 Presented by: Mahady Hasan12
Outline of the Presentation
• Introduction
• Related Work (Motivation)
• GridTree Approach
• ArcTrip Approach
• Continuous monitoring
• Experiments
• Conclusion
Wednesday, April 19, 2023 Presented by: Mahady Hasan13
Concept of ArcTrip
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Output:•cells that intersect the arc with in θstart and θend
with radius r from the query point q
Input:•Radius r•Angle range θstart , θendθstart θend
r
Returned values are c22, c32, c33
Wednesday, April 19, 2023 Presented by: Mahady Hasan14
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ArcTrip Based contained NN Algorithm
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Finding 1 constrained NN
Wednesday, April 19, 2023 Presented by: Mahady Hasan15
Outline of the Presentation
• Introduction
• Related Work (Motivation)
• GridTree Approach
• ArcTrip Approach
• Continuous monitoring
• Experiments
• Conclusion
Wednesday, April 19, 2023 Presented by: Mahady Hasan16
Continuous monitoring
• Phase 1: receive object and query updates.– Find affected queries.– Change in the queries based on the update below.
• Internal update (dist(oold,q)≤q.distk Λ dist(onew,q)≤q.distk)
– Arrange the order in q.CkNN
• Incoming update (dist(oold,q)>q.distk Λ dist(onew,q)<q.distk)
– Insert object in q.CkNN
• Outgoing update (dist(oold,q)≤q.distk Λ dist(onew,q)>q.distk)
– Remove object from q.CkNN
Wednesday, April 19, 2023 Presented by: Mahady Hasan17
Continuous monitoring …
• Phase 2: Check the status of each query one by one– If query moved then
• Execute the initial algorithm.
– If q.CkNN > k then • Keep top k objects and remove rest of the objects.
– If q.CkNN < k then • Expand the search area by visiting more cells
Wednesday, April 19, 2023 Presented by: Mahady Hasan18
Outline of the Presentation
• Introduction
• Related Work (Motivation)
• GridTree Approach
• ArcTrip Approach
• Continuous monitoring
• Experiments
• Conclusion
Wednesday, April 19, 2023 Presented by: Mahady Hasan19
Experiment Setup
Parameter Range
Grid size 162, 322, 642, 1282, 2562, 5122
Object cardinality 20k , 40k, 60k, 80k, 100k
Query cardinality 100, 200, 500, 1000, 2500, 5000
Values of k 2, 4, 8, 16, 32, 64, 128
Object/query speed Slow, Medium, Fast
Object/query agility 10%, 30%, 50%, 70%, 90%
Brinkhoff data generator; Oldenburg city (Germany).
Wednesday, April 19, 2023 Presented by: Mahady Hasan20
Grid cardinality effect
16 3.5480.0003.5483.54803.5483.5480.2293.778 .1323.5500.0003.5503.55003.5503.5500.2973.847 .6643.5580.0003.5583.55803.5583.5580.7164.274 1.11283.5730.0003.5733.57303.5733.5732.2275.800 1.62563.6190.0003.6193.61903.6193.6198.17111.790 2.15123.7430.0003.7433.74303.7433.74330.72934.472 2.6
Need to check too many objects.
Need to check many empty cells.
Wednesday, April 19, 2023 Presented by: Mahady Hasan21
Grid Memory Effect
CPM stores the heap and the visit list.
Wednesday, April 19, 2023 Presented by: Mahady Hasan23
Cardinality Effect
Updating in CPM become expensive
Wednesday, April 19, 2023 Presented by: Mahady Hasan24
Speed Effect
In CPM paper it was showed that speed has no affect.
Wednesday, April 19, 2023 Presented by: Mahady Hasan25
Agility effect
With increase in query agility CPM needsto compute the results from the scratch.
Object agility results more updates so the computation cost increases.
Wednesday, April 19, 2023 Presented by: Mahady Hasan26
Outline of the Presentation
• Introduction
• Related Work (Motivation)
• GridTree Approach
• ArcTrip Approach
• Continuous monitoring
• Experiments
• Conclusion
Wednesday, April 19, 2023 Presented by: Mahady Hasan27
Conclusion
• We proposed two novel Grid access methods.
• We devise two algorithms to compute the constrained k nearest neighbors.
• Our experimental results show our algorithms performs much better than the existing algorithm in terms of memory space and run time.