A Comparison of Query Execution Speeds for Large Amounts of Data Using Various DBMS Engines Executing on Selected RAM and CPU Configurations

In modern economies, most important business decisions are based on detailed analysis of available data. In order to obtain a rapid response from analytical tools, data should be pre-aggregated over dimensions that are of most interest to each business. Sometimes however, important decisions may require analysis of business data over seemingly less important dimensions which have not been pre-aggregated during the ETL process. On these occasions, the ad-hoc "online" aggregation is performed whose execution time is dependent on the overall DBMS performance. This paper describes how the performance of several commercial and non-commercial DBMSs was tested by running queries designed for data analysis using "ad-hoc" aggregations over large volumes of data. Each DBMS was installed on a separate virtual machine and was run on several computers, and two amounts of RAM memory were allocated for each test. Measurements of query execution times were recorded which demonstrated that, as expected, column-oriented databases out-performed classical row-oriented database systems

Application of nonlinear regression in recognizing distribution of signals in wireless channels

In many applications, it is important to recognise the distribution of empirical data in almost real time. One of the specific applications is the identification of statistical models for fading in wireless systems of the base station receivers. This is one of the most important problems in spatial diversity. In this paper, we describe the methodology and the results of a nonlinear regression approach for recognising the distribution of the input signal with the values of its parameters. Furthermore, the proposed approach could be used for the real-time recognition of the probability distributions without any prior knowledge about the input signal. To prove its performance, the LevenbergâMarquardt nonlinear least-squares algorithm is tested on a large set of randomly generated signals with the Gamma, Rayleigh, Rician, Nakagami-m, and Weibull distributions. The experimental results demonstrate that this approach is accurate in recognizing statistical distributions from the signal