String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Suffix trees and their applications in string algorithms

Abstract: The suffix tree is a compacted trie that stores all suffixes of a given text string. This data structure has been intensively employed in pattern matching on strings and trees, with a wide range of applications, such as molecular biology, data processing, text editing, term rewriting, interpreter design, information retrieval, abstract data types and many others. In this paper, we survey some applications of suffix trees and some algorithmic techniques for their construction. Special emphasis is given to the most recent developments in this area, such as parallel algorithms for suffix tree construction and generalizations of suffix trees to higher dimensions, which are important in multidimensional pattern matching. Work partially supported by the ESPRIT BRA ALCOM II under contract no. 7141 and by the Italian MURST Project “Algoritmi, Modelli di Calcolo e Strutture Informative”. Part of this work was done while the author was visiting AT&T Bell Laboratories. Email: grossi@di.unipi.it Work supported in part by the Commission of the European Communities under ESPRIT LTR Project no. 20244 (ALCOM–IT), by the Italian MURST Project “Efficienza di Algoritmi e Progetto di Strutture Informative”, and by a Research Grant from University of Venice “Ca’ Foscari”. Part of this work was done while at University of Salerno. Email: italiano@dsi.unive.it. URL: http://www.dsi.unive.it/∼italiano.

Image retrieval apparatus and method

Abstract: An image feature amount extraction unit segments an image into a plurality of blocks and calculates the feature amount of each block. A feature amount label string generation unit adds a label to each block in accordance with the feature amount acquired for the block and arranges the labels on the basis of a predetermined block sequence to generate a label string. In retrieval, the similarity between the label string of a specified image and that of an image to be compared is calculated, and an image whose similarity exceeds a predetermined value is output as a retrieval result. In this way, similar image retrieval can be performed in consideration of the arrangement of feature amounts of images, and simultaneously, similar image retrieval can be performed while absorbing any difference due to a variation in photographing condition or the like.

Robust and adaptive boundary control of a stretched string on a moving transporter

Abstract: Suppression of vibration is an important engineering problem. In this note, control problem of a flexible system that includes a stretched string supported on a transporter is defined and solved. Such a system may be encountered in device manufacturing and process automation. Robust and adaptive control is designed to damp out transverse oscillation of the string via compensating for possible uncertainties in string dynamics and transporter motion. Standard robust control design based on a straightforward Lyapunov argument commonly seen in control design for rigid-body systems is extended to the flexible system. Asymptotically/exponentially and robustly stabilizing controls are found.

New dynamic SPT algorithm based on a ball-and-string model

Abstract: A key functionality in today's widely used interior gateway routing protocols such as OSPF and IS-IS involves the computation of a shortest path tree (SPT). In many existing commercial routers, the computation of an SPT is done from scratch following changes in the link states of the network. As there may coexist multiple SPTs in a network with a set of given link states, such recomputation of an entire SPT not only is inefficient but also causes frequent unnecessary changes in the topology of an existing SPT and creates routing instability. This paper presents a new dynamic SPT algorithm that makes use of the structure of the previously computed SPT. This algorithm is derived by recasting the SPT problem into an optimization problem in a dual linear programming framework, which can also be interpreted using a ball-and-string model. In this model, the increase (or decrease) of an edge weight in the tree corresponds to the lengthening (or shortening) of a string. By stretching the strings until each node is attached to a tight string, the resulting topology of the model defines an (or multiple) SPT(s). By emulating the dynamics of the ball-and-string model, we can derive an efficient algorithm that propagates changes in distances to all affected nodes in a natural order and in a most economical way. Compared with existing results, our algorithm has the best-known performance in terms of computational complexity as well as minimum changes made to the topology of an SPT. Rigorous proofs for correctness of our algorithm and simulation results illustrating its complexity are also presented.

Evolving Readable String Test Inputs Using a Natural Language Model to Reduce Human Oracle Cost

Abstract: The frequent non-availability of an automated oracle means that, in practice, checking software behaviour is frequently a painstakingly manual task. Despite the high cost of human oracle involvement, there has been little research investigating how to make the role easier and less time-consuming. One source of human oracle cost is the inherent unreadability of machine-generated test inputs. In particular, automatically generated string inputs tend to be arbitrary sequences of characters that are awkward to read. This makes test cases hard to comprehend and time-consuming to check. In this paper we present an approach in which a natural language model is incorporated into a search-based input data generation process with the aim of improving the human readability of generated strings. We further present a human study of test inputs generated using the technique on 17 open source Java case studies. For 10 of the case studies, the participants recorded significantly faster times when evaluating inputs produced using the language model, with medium to large effect sizes 60% of the time. In addition, the study found that accuracy of test input evaluation was also significantly improved for 3 of the case studies.