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Approximate Shortest Distance Computing: A Query-Dependent Local Landmark Scheme
2014
IEEE Transactions on Knowledge and Data Engineering
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Two optimization techniques on graph compression and graph online search are also proposed, with the goal of further reducing index size and improving query accuracy. ...
To answer a shortest distance query, the precomputed distances from the landmarks to the two query nodes are used to compute an approximate shortest distance based on the triangle inequality. ...
[26] proposes TEDI, an indexing and query processing scheme for the shortest path query based on tree decomposition. ...
doi:10.1109/tkde.2012.253
fatcat:msdm5edmnnaqrli5vh6my4txsm
Embedding Retrieval of Articulated Geometry Models
2012
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we propose an embedding retrieval framework to improve the practicability of these methods. It is based on a manifold learning technique, the Diffusion Map (DM). ...
Our work provides a robust retrieval framework for the matching of multimedia data that lie on manifolds. ...
ACKNOWLEDGEMENTS The authors would like to thank the anonymous reviewers for their helpful and constructive comments on this paper. ...
doi:10.1109/tpami.2012.17
pmid:22231592
fatcat:77qymtjjkbcwjhdsmoebaojjka
Dynamic authenticated index structures for outsourced databases
2006
Proceedings of the 2006 ACM SIGMOD international conference on Management of data - SIGMOD '06
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Existing solutions for this problem concentrate mostly on static scenarios and are based on idealistic properties for certain cryptographic primitives. ...
As servers might be untrusted or can be compromised, query authentication becomes an essential component of ODB systems. ...
The embedded tree based on B +trees has a total of Ni = f d k k −1 f k −1 nodes while, for example, a B-tree based embedded tree (recall that a B-tree is equivalent to a balanced multi-way search tree) ...
doi:10.1145/1142473.1142488
dblp:conf/sigmod/LiHKR06
fatcat:jphjgxjs3jegpiziw7hkzmdgma
Optimal Embedding for Shape Indexing in Medical Image Databases
[chapter]
2005
Lecture Notes in Computer Science
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An optimal shape space embedding is proposed for this purpose. Experimental results of indexing vertebral shapes in the NHANES II database are presented. ...
In this paper, we propose that 2-D shapes in medical image databases can be indexed by embedding them into a vector space and using efficient vector space indexing. ...
Introduction Content-based retrieval in medical image databases is critically dependent on efficient indexing techniques. ...
doi:10.1007/11566489_47
fatcat:xochvfwn7ncunki4bwyaik3s5m
Em-K Indexing for Approximate Query Matching in Large-scale ER
[article]
2021
arXiv
pre-print
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It is becoming increasingly important in real-world applications to develop ER solutions that produce prompt responses for entity queries on large-scale databases. ...
In this paper, we investigate the query matching problem in ER to propose an indexing method suitable for approximate and efficient query matching. ...
First, we propose an indexing approach based on landmarks as a motivating example to explore the basic building blocks needed for query matching. • We propose a landmarks based indexing technique for query ...
arXiv:2111.04070v1
fatcat:4ci3l6tczfdn5j32cv42lba2ai
Fast Approximate Matching of Programs for Protecting Libre/Open Source Software by Using Spatial Indexes
2007
Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007)
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These techniques allowed us to do exhaustive experiments that confirmed previous results on the subject. ...
The paper also introduces the novel idea of using information retrieval techniques for calculating the similarity of bags of program fragments. ...
We would like to thank our reviewers for all their extremely valuable input. ...
doi:10.1109/scam.2007.4362903
fatcat:jf5ewb4gvbefzbvyupyjbmr6ge
Fast Approximate Matching of Programs for Protecting Libre/Open Source Software by Using Spatial Indexes
2007
Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007)
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These techniques allowed us to do exhaustive experiments that confirmed previous results on the subject. ...
The paper also introduces the novel idea of using information retrieval techniques for calculating the similarity of bags of program fragments. ...
We would like to thank our reviewers for all their extremely valuable input. ...
doi:10.1109/scam.2007.15
dblp:conf/scam/MolinaS07
fatcat:mdyuglaq6jc4zp2rd77hb5jnbe
EmbAssi: Embedding Assignment Costs for Similarity Search in Large Graph Databases
[article]
2022
arXiv
pre-print
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We introduce methods for answering nearest neighbor and range queries regarding this distance efficiently for large databases with up to millions of graphs. ...
Highly effective bounds for this involve solving a linear assignment problem for each graph in the database, which is prohibitive in massive datasets. ...
Moreover, approximate lter techniques for the graph edit distance based on embeddings learned by graph neural networks were only recently proposed [28] . ...
arXiv:2111.07761v2
fatcat:2psp3up6bnaynmoxa4eaz2idnu
Learning supervised embeddings for large scale sequence comparisons
2020
PLoS ONE
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We also propose an hierarchical tree-based approach specifically designed for the sequence retrieval problem. ...
Similarity-based search of sequence collections is a core task in bioinformatics, one dominated for most of the genomic era by exact and heuristic alignment-based algorithms. ...
For protein classification tasks in most of the cases, our methods give an improvement of 2 − 5% over unsupervised embedding techniques. ...
doi:10.1371/journal.pone.0216636
pmid:32168338
pmcid:PMC7069636
fatcat:cwrxyfxdmngctln2rdq6kskfgi
A road network embedding technique for k-nearest neighbor search in moving object databases
2002
Proceedings of the tenth ACM international symposium on Advances in geographic information systems - GIS '02
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In this paper, we apply an embedding technique to transform a road network to a high dimensional space in order to utilize computationally simple Minkowski metrics for distance measurement. ...
Most of the current studies on the K-nearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. ...
Their algorithm is adapted to R-Tree and is suitable for distance browsing queries but does not provide a substantial improvement in performance over the current R-Tree based KNN algorithms. Tao et al ...
doi:10.1145/585147.585167
dblp:conf/gis/ShahabiKS02
fatcat:uaju4i6ryrbirgdozynimqqyem
A road network embedding technique for k-nearest neighbor search in moving object databases
2002
Proceedings of the tenth ACM international symposium on Advances in geographic information systems - GIS '02
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In this paper, we apply an embedding technique to transform a road network to a high dimensional space in order to utilize computationally simple Minkowski metrics for distance measurement. ...
Most of the current studies on the K-nearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. ...
Their algorithm is adapted to R-Tree and is suitable for distance browsing queries but does not provide a substantial improvement in performance over the current R-Tree based KNN algorithms. Tao et al ...
doi:10.1145/585163.585167
fatcat:lmhx6kap3jg5tg3sat2pcshs4e
Efficient and Faster Method for Query-Dependent Local Landmark Scheme
English
2015
International Journal of Innovative Research in Computer and Communication Engineering
English
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To get distance estimation correctness and rapid computation method we develop Efficient Structure for Query-Dependent Local Landmark Scheme to extent previously presented robust method with improved SPT ...
Which find local landmark reliant on queried node and then distance among these two queried nodes is calculated as sum of their shortest distance to local landmark which is earlier than global one. ...
For this reason LLS projected disk-based index on relational database because of its great indexing and query optimization mechanism. ...
doi:10.15680/ijircce.2015.0307013
fatcat:5hr6lgtqknevria6ruadjky7xq
Neo: A Learned Query Optimizer
[article]
2019
arXiv
pre-print
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Query optimization is one of the most challenging problems in database systems. ...
relies on deep neural networks to generate query executions plans. ...
Thus, while we have no evidence that our row vector embedding technique will work on every imaginable database, we argue that our analysis on the IMDB database (a database with significant correlations ...
arXiv:1904.03711v1
fatcat:sm5bmuavtvffrjkg5qno7pwaaq
Accelerating sequence searching: dimensionality reduction method
2008
Knowledge and Information Systems
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In this paper, a novel index structure, namely Sequence Embedding Multiset tree(SEM-tree), has been proposed to speed up the searching process over long sequences. ...
The multisets, obtained using sequence embedding algorithms, have the desirable property that they do not need to keep the character order in the sequence, i.e. shorter representation, but can reserve ...
Based on the sequence embedding technique, we know that the length of multiset (or submultiset) highly depends on the character set size τ . ...
doi:10.1007/s10115-008-0180-0
fatcat:vlegznlswvgg7gt2ylhtqda2ym
Learning supervised embeddings for large scale sequence comparisons
[article]
2019
bioRxiv
pre-print
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The SuperVec approach is extended further through H-SuperVec, a tree-based hierarchical method which learns embeddings across a range of feature spaces based on the class labels and their exclusive and ...
Experiments show that supervised learning of embeddings based on sequence labels using SuperVec and H-SuperVec provides a substantial improvement in retrieval performance over existing (unsupervised) RL-based ...
For embedding based methods, we address the retrieval problem by employing nearest neighbour search, where for a given query the sequences from a database are retrieved based on their cosine-distance to ...
doi:10.1101/620153
fatcat:awhslcijwjaolmv5e6dxsrknoi
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