Showing papers on "Feature hashing published in 1995"

New hashing techniques and their application to a protein structure database system

Abstract: We have devised novel methods to evaluate the structural similarity of proteins and we compare them. In each method, a hash vector is associated with each fixed length fragment of three dimensional protein structure. Then, we analyze the similarity between fragments by evaluating the difference between true hash vectors. The novel aspect of the methods is that the following property is proved theoretically: the root mean square deviation between two fragments is small, so the distance between the hash vectors associated with the fragments is small. The methods were compared with the previous methods using PDB data, and were shown to be much faster. One of the new hashing methods is already included in PROTEIX, a database management system for protein structures. The features of PROTEIX are described. >

The effect of clustering on the performance of hashing algorithms

Abstract: Hashing algorithms are search procedures commonly used, among other applications, in the solution of logic synthesis and formal hardware verification problems. Purpose of this paper is to analyze the performance of hashing algorithms. In particular, we are interested in studying the effect of clustering on hashing methods.

Random access from compressed datasets with perfect value hashing

Abstract: A representation technique is presented allowing for quick access of individual records from a static compressed dataset. Given a collection of key-record pairs, the representation allows the appropriate short record to be returned for any given key. The approach is a generalization of perfect address hashing. The new approach, called perfect value hashing, uses a carefully chosen pseudo-random number generator to directly produce the correct record for any key in the dataset. This contrasts with address hashing where the random number provides an address which is then used to recover the record from a separate table. Value hashing doesn't have the theoretical limitations of address hashing, and in practice is more space efficient for records of size less than 36 bits. Value hashing has the added benefit (important when the records are encoded for compression) that variable length records can be represented without an increase in the size of the encoded records. This new technique was used to provide random access from a highly compressed spelling dictionary.

A comparison of three strategies for computing letter oriented, minimal perfect hashing functions

Abstract: This paper will discuss improvements to Cichelli's method for computing the set of weights used for minimal perfect hashing functions[1]. The major modifications investigated here pertain to adding a "MOD number_of_keys" operation to the hashing function, and to the removal of unnecessary backtracking due to "guaranteed collisions".

Domain vector hashing for earth system data querying

Abstract: In this paper we propose a new method for processing earth science applications which involve join processing. We call this method the 'Domain Vector Pe$ect Hash Join Method' (DVPH). DVPH is a simple method which works well for processing applications with joins. The DVPH method improves the efficiency ana' response time and handles dynamic memory allocation. It ameliorates losses due to unnecessary page l/O, losses due to memory preemption from higher priori0 applications and losses due to inappropriate hash function selections. DVPH handles skewed data well. These advantages are gained at the expense of maintaining a domain vector and an extra index.