Self-organizing search lists using probabilistic back-pointers

A class of algorithms is given for maintaining self-organizing sequential search lists, where the only permutation applied is to move the accessed record of each search some distance towards the front of the list. During searches, these algorithms retain a back-pointer to a previously probed record in order to determine the destination of the accessed record's eventual move. The back-pointer does not traverse the list, but rather it is advanced occationally to point to the record just probed by the search algorithm. This avoids the cost of a second traversal through a significant portion of the list, which may be a significant savings when each record access may require a new page to be brought into primary memory. Probabilistic functions for deciding when to advance the pointer are presented and analyzed. These functions demonstrate average case complexities of measures such as asymptotic cost and convergence similar to some of the more common list update algorithms in the literature. In cases where the accessed record is moved forward a distance proportional to the distance to the front of the list, the use of these functions may save up to 50% of the time required for permuting the list

On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists

Author's accepted manuscript.This is a post-peer-review, pre-copyedit version of an article published in Evolving Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12530-020-09327-4.acceptedVersio

Self-organizing search lists using probabilistic back-pointers

A class of algorithms is given for maintaining self-organizing sequential search lists, where the only permutation applied is to move the accessed record of each search some distance towards the front of the list. During searches, these algorithms retain a back-pointer to a previously probed record in order to determine the destination of the accessed record's eventual move. The back-pointer does not traverse the list, but rather it is advanced occationally to point to the record just probed by the search algorithm. This avoids the cost of a second traversal through a significant portion of the list, which may be a significant savings when each record access may require a new page to be brought into primary memory. Probabilistic functions for deciding when to advance the pointer are presented and analyzed. These functions demonstrate average case complexities of measures such as asymptotic cost and convergence similar to some of the more common list update algorithms in the literature. In cases where the accessed record is moved forward a distance proportional to the distance to the front of the list, the use of these functions may save up to 50% of the time required for permuting the list

Samoupravující seznamy

Samoupravující seznamy Samoupravující seznamy jsou datové struktury sloužící k rychlému vyhledávání za předpokladu, že některé prvky v nich uložené jsou vyhledávány častěji než jiné, přičemž pravděpodobnosti přístupu k jednotlivým prvkům obecně nejsou předem známy. Efektivnějšího vyhledávání je dosaženo použitím různých permutačních pravidel, která průběžně mění uspořádání seznamu tak, aby častěji vyhledávané prvky byly blíže k jeho začátku. V této práci je uveden přehled známých algoritmů pro řešení tohoto problému (s uvedením teoretických výsledků o jejich složitosti, jsou-li známy) a experimentální studie o jejich chování (s využitím vlastních nebo volně dostupných implementací a programových prostředků pro generování vstupních dat, testování algoritmů a zpracování výsledků experimentů).Self-organizing linear lists Self-organizing linear lists are data structures for fast search, provided that certain elements stored in them are searched more frequently than others, while the probability of access to individual elements is generally not known in advance. Efficient search is achieved using different permutation rules that keep changing the list structure so that the more frequently searched elements are closer to the beginning. This thesis gives an overview of known algorithms for solving this problem (with the theoretical results about their complexity, if they are known), and experimental study of their behavior (using its own or freely available implementations and software for generating input data, testing algorithms and processing the results of experiments).Department of Distributed and Dependable SystemsKatedra distribuovaných a spolehlivých systémůFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Usage-Dependent Information Systems Design.

Usage-dependent phenomenon has been commonly observed in computer information systems (CIS). Since the performance of CIS greatly depends on the phenomenon, how to model it is an important CIS design issue. A usage process model (the Simon-Yule model) for modeling the usage--dependent phenomenon is proposed. The model is modified and successfully applied to the performance evaluation of self-organizing linear search heuristics. Analytical and empirical results indicate that the model provides a realistic performance evaluation of the heuristics and presents a solution to the research open problems which have been unsolved for more than two decades. Furthermore, the results lead to develop a self-organizing mechanism incorporating the usage process model for continuous speech recognition systems in the artificial intelligence arena