Znaleziono wyników: 9
Liczba wyników na stronie
Wyniki wyszukiwania
prognozowanie wydajności serwisów WWW na drodze eksperymentów symulacyjnych. Przedyskutowano specyfikę obciążenia biznesowych serwisów webowych, a następnie przedstawiono proponowany model obciążenia, opracowany na podstawie dostępnych w literaturze wyników badań charakteryzujących obciążenie rzeczywistych serwisów WWW. Zaproponowano połączenie modelu sesji użytkownika na witrynie B2C z modelem obciążenia na poziomie żądań HTTP, co pozwala na generowanie ruchu webowego charakterystycznego dla witryn B2C o dużej zmienności natężenia, co potwierdzają przedstawione wyniki badań symulacyjnych.
performance prediction and evaluation through simulation experiments. First, a specificity of business Web server system workload is discussed, followed by the proposal of a workload model, based on up-to-date results on real Web server workload characteristics. We propose combining a model of a user session at a B2C Web site with HTTP-level workload models both for business and non-business Web servers. Simulation results have shown that the generated Web traffic is highly variable and bursty.
scheduling scheme for the e-commerce server aiming at the integration of the server system efficiency with e-business profitability.
elektronicznego biznesu.
oferowaniu wyższej jakości usług dla bardziej wartościowych klientów. Do identyfikacji i oceny wartości kluczowych klientów zaproponowano zastosowanie analizy RFM (ang. Recency-Frequency-Monetary value analysis). Przedyskutowano nowy algorytm kontroli przyjęć i szeregowania żądań, realizujący sterowanie zgodnie z przyjętymi celami "biznesowymi".
and site abandonment. Such situations are detrimental to e-business conducted over the Internet, especially in the highly competitive Business-to-Consumer (B2C) e-commerce environment. In the paper, a novel request service method for a Web server system hosting a B2C Web site is proposed. The method aims at ensuring high revenue achieved by an online-retailer through successfully processed purchase transactions, as well as offering higher QoWS for more valued customers. To identify and evaluate values of key customers, RFM (Recency-Frequency-Monetary value) analysis has been applied to the method. A new admission control and scheduling algorithm realizing request service control according to business-oriented goals is discussed.
omówiono technologie Answer Wizard i Office Assistant, zastosowane w systemie pomocy dla użytkowników aplikacji biurowych. Przedstawiono wady i zalety sieci Bayesa w kontekście zastosowań praktycznych.
different fields. As an example, two technologies are discussed: Answer Wizard and Office Assistant. Furthermore, advantages and disadvantages of Bayesian Networks in the context of practical use are presented.
to automatically compute customer values in a Web store, and using these values in a new admission control and scheduling algorithm for a Web server system. The proposed method and algorithm are discussed, and simulation results of its efficacy compared to the First-In-First-Out (FIFO) scheduling are presented.
ultimately, to increase Web site's conversion rate, i.e. to turn more visitors into customers. The sensitivity of the algorithm to changes in its basic parameter values was analyzed by using a simulation-based approach. Special attention was paid to evaluation of the parameter impact on conventional and business-related system performance metrics.
także podstawowe pojęcia. Zarysowano problemy związane z budowaniem modeli sieci, przedstawiono obszary zastosowań tej metody wnioskowania oraz narzędzia służące do tworzenia i modelowania sieci Bayesa. W celu zilustrowania omawianych zagadnień autorzy zaprojektowali przykładową sieć Bayesa i wykorzystali program MSBN do przeprowadzenia wnioskowania dla tej sieci.
simple causal network. Furthermore, authors outline problems connected with building network models, they present fields of BN applications and the most popular tools for building and modeling Bayesian Networks. In order to illustrate the discussed issues, authors designed a hypothetical Bayesian Network and they used a program called MSBN to carry out the process of inferring for that network.
classification methods based on decision trees and apply them to the direct marketing campaigns data of a Portuguese banking institution. We discuss and compare the following classification methods: decision trees, bagging, boosting, and random forests. A classification problem in our approach is defined in a scenario where a bank’s clients make decisions about the activation of their deposits. The obtained results are used for evaluating the effectiveness of the classification rules.
sessions, some information describing active sessions may be observed and used to assess the probability of making a purchase. The authors formulate the problem of predicting buying sessions in a Web store as a supervised classification problem where there are two target classes, connected with the fact of finalizing a purchase transaction in session or not, and a feature vector containing some variables describing user sessions. The presented approach uses the k-Nearest Neighbors (k-NN) classification. Based on historical data obtained from online bookstore log files a k-NN classifier was built and its efficiency was verified for different neighborhood sizes. A 11-NN classifier was the most effective both in terms of buying session predictions and overall predictions, achieving sensitivity of 87.5% and accuracy of 99.85%.
Ograniczanie wyników