Partial-match retrieval for dynamic files

This paper studies file designs for answering partial-match queries for dynamic files. A partial-match query is a specification of the value of zero or more fields in a record. An answer to a query consists of a listing of all records in the file satisfying the values specified.The main contribution is a general method whereby certain primary key hasing schemes can be extended to partial-match retrieval schemes. These partial-match retrieval designs can handle arbitrarily dynamic files and can be optimized with respect to the number of page faults required to answer a query.We illustrate the method by considering in detail the extension of two recent dynamic primary key hashing schemes.     

Multi-attribute hashing with multiple file copies for high performance partial-match retrieval

The average cost for answering partial-match queries can be dramatically reduced by storing multiple copies of the data, each with a different clustering. We analyse the cost benefits (in terms of page accesses) of this arrangement and present heuristic algorithms for determining a near-minimal-cost file organisation for a given probability distribution of queries. We also show how constraining the range of values for specific attributes affects the usefulness of maintaining multiple copies.     

Wavelet-based fingerprint image retrieval

This paper presents a novel approach for personal identification based on a wavelet-based fingerprint retrieval system which encompasses three image retrieval tasks, namely, feature extraction, similarity measurement, and feature indexing.     

Projections and phase retrieval

We characterize collections of orthogonal projections for which it is possible to reconstruct a vector from the magnitudes of the corresponding projections. As a result we are able to show that in an M-dimensional real vector space a vector can be reconstructed from the magnitudes of its projections onto a generic collection of $N\ge 2M?1$ subspaces. We also show that this bound is sharp when $N=2^k+1$. The results of this paper answer a number of questions raised in [5].     

Partial match retrieval

The study of partial match file designs is continued. This study is concerned with a class of file designs which properly contains theABD designs of Rivest. A new family of partial match files is presented, the worst case performance is determined, and the implementation of these files is discussed.     

Possibilistic networks for information retrieval

This paper proposes an information retrieval (IR) model based on possibilistic directed networks. The relevance of a document w.r.t a query is interpreted by two degrees: the necessity and the possibility. The necessity degree evaluates the extent to which a given document is relevant to a query, whereas the possibility degree evaluates the reasons of eliminating irrelevant documents. This new interpretation of relevance led us to revisit the term weighting scheme by explicitly distinguishing between informative and non-informative terms in a document. Experiments carried out on three standard TREC collections show the effectiveness of the model.     

Partial match retrieval using recursive linear hashing

Recursive linear hashing is a hashing technique proposed for files which can grow and shrink dynamically. The scheme is an extension of linear hashing, a method originally proposed by Litwin, but unlike Litwin's scheme, it does not require conventional overflow pages. In this paper, we investigate the application of recursive linear hashing to partial match retrieval problems. Consistent with the results for primary key retrieval, recursive linear hashing performs better than the conventional scheme on these problems, especially at high load factors.     

Fuzzy measures for color image retrieval

In this paper we present a scheme for fuzzy similarity based strategy to retrieve an image from a library of color images. Color features are among the most important features used in image database retrieval. Due to its compact representation and low complexity, direct histogram comparison is the most commonly used technique in measuring color similarity of images. A gamma membership function, derived from the Gamma distribution, has been proposed to find the membership values of the gray levels of the histogram. We present here an image retrieval scheme with some popular vector fuzzy distance measures using a gamma membership function for finding the membership values of the gray levels and evaluate the matching function to select the appropriate retrieval mechanism.     

Phase retrieval from local measurements: Improved robustness via eigenvector-based angular synchronization

We improve a phase retrieval approach that uses correlation-based measurements with compactly supported measurement masks [30]. Our approach admits deterministic measurement constructions together with a robust, fast recovery algorithm that consists of solving a system of linear equations in a lifted space, followed by finding an eigenvector (e.g., via an inverse power iteration). Theoretical reconstruction error guarantees from [30] are improved as a result for the new and more robust reconstruction approach proposed herein. Numerical experiments demonstrate robustness and computational efficiency that compete with other approaches on large problems. Along the way, we show that this approach also trivially extends to phase retrieval problems based on windowed Fourier measurements.     

Lanczos and the Riemannian SVD in information retrieval applications

Variations of the latent semantic indexing (LSI) method in information retrieval (IR) require the computation of singular subspaces associated with the k dominant singular values of a large m × n sparse matrix A, where k?min(m,n). The Riemannian SVD was recently generalized to low‐rank matrices arising in IR and shown to be an effective approach for formulating an enhanced semantic model that captures the latent term‐document structure of the data. However, in terms of storage and computation requirements, its implementation can be much improved for large‐scale applications. We discuss an efficient and reliable algorithm, called SPK‐RSVD‐LSI, as an alternative approach for deriving the enhanced semantic model. The algorithm combines the generalized Riemannian SVD and the Lanczos method with full reorthogonalization and explicit restart strategies. We demonstrate that our approach performs as well as the original low‐rank Riemannian SVD method by comparing their retrieval performance on a well‐known benchmark document collection. Copyright 2004 John Wiley & Sons, Ltd.     

Content-based image retrieval using visually significant point features

This paper presents a new image retrieval scheme using visually significant point features. The clusters of points around significant curvature regions (high, medium, and weak type) are extracted using a fuzzy set theoretic approach. Some invariant color features are computed from these points to evaluate the similarity between images. A set of relevant and non-redundant features is selected using the mutual information based minimum redundancy-maximum relevance framework. The relative importance of each feature is evaluated using a fuzzy entropy based measure, which is computed from the sets of retrieved images marked relevant and irrelevant by the users. The performance of the system is evaluated using different sets of examples from a general purpose image database. The robustness of the system is also shown when the images undergo different transformations.     

Backward shift invariant subspaces with applications to band preserving and phase retrieval problems

The band preserving and phase retrieval problems have long been interested and studied. In this paper, we, for the first time, give solutions to these problems in terms of backward shift invariant subspaces. The backward shift method among other methods seems to be direct and natural. We show that a function , with , that makes the band of fg to be within that of f if and only if g divided by an inner function related to f, belongs to some backward shift invariant subspace in relation to f. By the construction of backward shift invariant space, the solution g is further explicitly represented through the span of the rational function system whose zeros are those of the Laplace transform of f. As an application, we also use the backward shift method to give a characterization for the solutions of the phase retrieval problem. Copyright © 2015 John Wiley & Sons, Ltd.     

Hyperbolic 0–1 programming and query optimization in information retrieval

Unconstrained hyperbolic 0–1 programming can be solved in linear time when the numerator and the denominator are linear and the latter is always positive. It is NP-hard, and finding an approximate solution with a value equal to a positive multiple of the optimal one is also NP-hard, if this last hypothesis does not hold. Determining the optimal logical form of a query in information retrieval, given the attributes to be used, can be expressed as a parametric hyperbolic 0–1 program and solved in O(n logn) time, wheren is the number of elementary logical conjunctions of the attributes. This allows to characterize the optimal queries for the Van Rijsbergen synthetic criterion.This research was done in part during a visit of the first author to the Pontifical Catholic University of Rio de Janeiro in July and August 1987, sponsored by CNPq. It was also supported in part by grants 0271 and 0066 of the AFOSR to Rutgers University. The second author was with Centro de Análise de Sistemas Navais, Rio de Janeiro.     

Travel time models for automated warehouses with aisle transferring storage and retrieval machine

This paper presents analytical travel time models for the computation of travel time for automated warehouses with the aisle transferring S/R machine (in continuation multi-aisle AS/RS). These models consider the operating characteristics of the storage and retrieval machine such as acceleration and deceleration and the maximum velocity. Assuming uniform distributed storage rack locations and pick aisles and using the probability theory, the expressions of the cumulative distribution functions with which the mean travel time is calculated, have been determined. The computational models enable the calculation of the mean travel time for the single and dual command cycles, from which the performance of multi-aisle AS/RS can be evaluated. A simulation model of multi-aisle AS/RS has been developed to compare the performances of the proposed analytical travel time models. The analyses show that regarding all examined types of multi-aisle AS/RS, the results of proposed analytical travel time models correlate with the results of simulation models of multi-aisle AS/RS.