Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2013
Volume
Vol. 14
Number
No. 2
Description
Reviewed by: Maciej Twardy, Renata Słota, Chao Wang, Ewa Deelman, Grzegorz J. Nalepa, Bartłomiej Śnieżyński, Michał Korzycki, Fernando A. Mikic, Tadeusz Szymocha, Gabriele Pierantoni, John Walsh, Krzysztof Boryczko, Ernest Jamro, Anna Barnacka, Darin Nikolow, Włodzimierz Funika, Piotr Bała, Joanna Kocot, Andrzej Eilmes, Daniel Harezlak, Bartosz Baliś, Przemysław Majewski, Mirosław Gajer
Journal Volume
Computer Science
Vol. 14 (2013)
Projects
Pages
Articles
Data storage management using AI methods
(Wydawnictwa AGH, 2013) Funika, Włodzimierz; Szura, Filip
Data management and monitoring is an important issue in scientific computation. Scientists want to access their data as quickly as possible. Some experiments need to store a lot of data which have to be secure. By saying this we mean that this data can not disappear or be damaged also the data storage should be as cheap as possible. In this paper we present an approach to the automation of monitoring and management of data storage. We introduce a knowledge based system which is able to manage data, i.e., make decisions on migrating data, replicating or removing it. We discuss some of the existing solutions which are popular on the market. In this paper we aim to present our system which uses such AI techniques like fuzzy logic or a rule-based expert system to deal with data storage management. We exploit in this system a cost model to analyze the proposed solutions. The operations performed by our system are aimed to optimize the usage of the monitored infrastructure.
Unsupervised labeling of data for supervised learning and its application to medical claims prediction
(Wydawnictwa AGH, 2013) Ngufor, Che G.; Wojtusiak, Janusz
The task identifying changes and irregularities in medical insurance claim payments is a difficult process of which the traditional practice involves querying historical claims databases and flagging potential claims as normal or abnormal. Because what is considered as normal payment is usually unknown and may change over time, abnormal payments often pass undetected, only to be discovered when the payment period has passed. This paper presents the problem of on-line unsupervised learning from data streams when the distribution that generates the data changes or drifts over time. Automated algorithms for detecting drifting concepts in a probability distribution of the data are presented. The idea behind the presented drift detection methods is to transform the distribution of the data within a sliding window into a more convenient distribution. Then, a test statistics p-value at a given significance level can be used to infer the drift rate, adjust the window size and decide on the status of the drift. The detected concepts drifts are used to label the data, for subsequent learning of classification models by a supervised learner. The algorithms were tested on several synthetic and real medical claims data sets.
Web pages content analysis using browser-based volunteer computing
(Wydawnictwa AGH, 2013) Turek, Wojciech; Nawarecki, Edward; Dobrowolski, Grzegorz; Krupa, Tomasz; Majewski, Przemysław
Existing solutions to the problem of finding valuable information on the Web suffers from several limitations like simplified query languages, out-of-date in- formation or arbitrary results sorting. In this paper a different approach to this problem is described. It is based on the idea of distributed processing of Web pages content. To provide sufficient performance, the idea of browser-based volunteer computing is utilized, which requires the implementation of text processing algorithms in JavaScript. In this paper the architecture of Web pages content analysis system is presented, details concerning the implementation of the system and the text processing algorithms are described and test results are provided.
The application of a noise mapping tool deployed in grid infrastructure for creating noise maps of urban areas
(Wydawnictwa AGH, 2013) Szczodrak, Maciej; Kotus, Józef; Czyżewski, Andrzej; Kostek, Bożena
The concept and implementation of the system for creating dynamic noise maps in PL-Grid infrastructure are presented. The methodology of dynamic acoustical map screating is introduced. The concept of noise mapping, based on noise source and propagation models, was developed and employed in the system. The details of incorporation of the system to the PL-Grid infrastructure are presented. The results of simulations performed by the system prototype are depicted. The results in the form of noise maps obtained by a system are compared with some other solutions in order to investigate accuracy.
Accelerating SELECT WHERE and SELECT JOIN queries on a GPU
(Wydawnictwa AGH, 2013) Pietroń, Marcin; Russek, Paweł; Wiatr, Kazimierz
This paper presents implementations of a few selected SQL operations using the CUDA programming framework on the GPU platform. Nowadays, the GPU’s parallel architectures give a high speed-up on certain problems. Therefore, the number of non-graphical problems that can be run and sped-up on the GPU still increases. Especially, there has been a lot of research in data mining on GPUs. In many cases it proves the advantage of offloading processing from the CPU to the GPU. At the beginning of our project we chose the set of SELECT WHERE and SELECT JOIN instructions as the most common operations used in databases. We parallelized these SQL operations using three main mechanisms in CUDA: thread group hierarchy, shared memories, and barrier synchronization. Our results show that the implemented highly parallel SELECT WHERE and SELECT JOIN operations on the GPU platform can be significantly faster than the sequential one in a database system run on the CPU.