Using a Chen-Stein identity to obtain low variance simulation estimators

This paper is concerned with developing low variance simulation estimators of probabilities related to the sum of Bernoulli random variables. It shows how to utilize an identity used in the Chen-Stein approach to bounding Poisson approximations to obtain low variance estimators. Applications and numerical examples in such areas as pattern occurrences, generalized coupon collecting, system reliability, and multivariate normals are presented. We also consider the problem of estimating the probability that a positive linear combination of Bernoulli random variables is greater than some specified value, and present a simulation estimator that is always less than the Markov inequality bound on that probability.

Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset

Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occurrences may offer valuable information in knowledge data discovery process. The R-Eclat is a novel algorithm that determines infrequent patterns in the transaction database. The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. This paper proposes IF-Postdiffset as a new variant in R-Eclat algorithm. This paper also highlights the performance of infrequent mining pattern from the transaction database among different variants of the R-Eclat algorithm regarding its execution time.

A METHOD OF SEQUENTIAL SEARCHING OF OCCURANCES IN TEXT WITH THE ACCOUNT OF POSSIBLE COLLISIONS

One of the fundamental tasks of modern computer information systems is processing of symbol information, the amount of which prevails in the total amount of information. At present, rules-based approach is effectively applied to the tasks of processing symbol information. The paper deals with the peculiarities of text search applying rules-based approach. The main essence of the approach is to find pattern occurrences in the text and possible implementation of substitution (text modification). Meanwhile, when implementing search for occurrences, various kinds of collisions may arise. They should be taken into account to solve the set tasks correctly. For example, algorythms of sequential word matching can run into collisions which involve the possibility of skipping positions of pattern occurrence in a word with some structural peculiarities. The paper presents a method of searching taking into account possible collisions developed by the authors, as well as algorithmic and automatic models of the method. The developed method involves patterm markup and setting a sequence of its viewing in the form of algorithm diagram. Three algorythms (implementation variants) of the method have been developed. Algorithms differ in the possibility to carry out transition to this oк that position of the pattern and the text depending on the result of matching (equality or inequality of the current symbols of the patten and text). An automation model of the method has been developed. The proposed method of sequential matching with the pattern with collisions elimination increases the effectiveness of the computer system when implementing search procedures and symbol information processing. The method can be used in the systems of symbol information processing.

A Stitch in Time Saves Nine: Early Improving Code-First Web Services Discoverability

Web Services represent a number of standard technologies and methodologies that allow developers to build applications under the Service-Oriented Computing paradigm. Within these, the WSDL language is used for representing Web Service interfaces, while code-first remains the de facto standard for building such interfaces. Previous studies with contract-first Web Services have shown that avoiding a specific catalog of bad WSDL specification practices, or anti-patterns, can reward Web Service publishers as service understandability and discoverability are considerably improved. In this paper, we study a number of simple and well-known code service refactorings that early reduce anti-pattern occurrences in WSDL documents. This relationship relies upon a statistical correlation between common OO metrics taken on a service's code and the anti-pattern occurrences in the generated WSDL document. We quantify the effects of the refactorings — which directly modify OO metric values and indirectly alter anti-pattern occurrences — on service discovery. All in all, we show that by applying the studied refactorings, anti-patterns are reduced and Web Service discovery is significantly improved. For the experiments, a dataset of real-world Web Services and an academic service registry have been employed.