Multi-index hashing for information retrieval

Greene, Derek; Parnas, Michal; Yao, Fei

doi:10.1109/sfcs.1994.365720

Cited by 72 publications

(61 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in the nearduplicate Web page detection system in [19], solutions for k = 3 are sufficient. For arbitrary but large k, two solutions have been proposed in [12], [13]; however, they are inefficient for problems with small k.…”

Section: Limitations Of Prior Artmentioning

confidence: 99%

See 1 more Smart Citation

Large scale Hamming distance query processing

Liu

Shen

Torng

2011

2011 IEEE 27th International Conference on Data Engineering

View full text Add to dashboard Cite

Abstract-Hamming distance has been widely used in many application domains, such as near-duplicate detection and pattern recognition. We study Hamming distance range query problems, where the goal is to find all strings in a database that are within a Hamming distance bound k from a query string. If k is fixed, we have a static Hamming distance range query problem. If k is part of the input, we have a dynamic Hamming distance range query problem. For the static problem, the prior art uses lots of memory due to its aggressive replication of the database. For the dynamic range query problem, as far as we know, there is no space and time efficient solution for arbitrary databases. In this paper, we first propose a static Hamming distance range query algorithm called HEngine s , which addresses the space issue in prior art by dynamically expanding the query on the fly. We then propose a dynamic Hamming distance range query algorithm called HEngine d , which addresses the limitation in prior art using a divide-and-conquer strategy. We implemented our algorithms and conducted side-by-side comparisons on large real-world and synthetic datasets. In our experiments, HEngine s uses 4.65 times less space and processes queries 16% faster than the prior art, and HEngine d processes queries 46 times faster than linear scan while using only 1.7 times more space.

show abstract

Section: Limitations Of Prior Artmentioning

confidence: 99%

“…k is large: Two solutions have been proposed in [12], [13]; however, both are inefficient for problems where k is small.…”

Section: A Static Hamming Distance Range Querymentioning

confidence: 99%

Large scale Hamming distance query processing

Liu

Shen

Torng

2011

2011 IEEE 27th International Conference on Data Engineering

View full text Add to dashboard Cite

show abstract

“…Experimental results can validate the claims and verify the algorithm's optimality. In fact, many questions can only be answered after the application of the algorithm on data [14,15,16], such as:…”

Section: Future Workmentioning

confidence: 99%

An Efficient DNA Molecule Clustering using GCC Algorithm

Kholood

Alnowaiser

2015

GSTF J Comput

View full text Add to dashboard Cite

“…Given the high volume of data there is need for fast access and retrieval of required or relevant data. Several of the existing data structures are hashing [1,2,3,4,5,6], search trees [7,8], and clustering [9]. Hashing is a technique that utilizes a hash function to convert large values into hash values and maps similar large values to the same hash values or keys in a hash table.…”

mentioning

confidence: 99%

Clustering: Applied to Data Structuring and Retrieval

Iloanusi¹,

Osuagwu²

2011

IJACSA

View full text Add to dashboard Cite

Abstract-Clustering is a very useful scheme for data structuring and retrieval behuhcause it can handle large volumes of multidimensional data and employs a very fast algorithm. Other forms of data structuring techniques include hashing and binary tree structures. However, clustering has the advantage of employing little computational storage requirements and a fast speed algorithm. In this paper, clustering, k-means clustering and the approaches to effective clustering are extensively discussed. Clustering was employed as a data grouping and retrieval strategy in the filtering of fingerprints in the Fingerprint Verification Competition 2000 database 4(a). An average penetration of 7.41% obtained from the experiment shows clearly that the clustering scheme is an effective retrieval strategy for the filtering of fingerprints.

show abstract

Multi-index hashing for information retrieval

Cited by 72 publications

References 12 publications

Large scale Hamming distance query processing

Large scale Hamming distance query processing

An Efficient DNA Molecule Clustering using GCC Algorithm

Clustering: Applied to Data Structuring and Retrieval

Contact Info

Product

Resources

About