Lukasz Warchal

Using Oracle 11.2g Database Server in Social Network Analysis Based on Recursive SQL

The article describes the possibility of using Oracle 11.2g database server in social networks analysis. Nowadays, when data about human’s relationships, thanks to Internet technologies, are often gathered, it is becoming more and more interesting to process that data. Authors show how to compute some metrics (degree centrality, local clustering) describing networks based on data stored in a relational database. The use of recursive SQL resulted in compact formulas that can be process at the database server side in an efficient way.

GPU-Accelerated Method of Query Selectivity Estimation for Non Equi-Join Conditions Based on Discrete Fourier Transform

Selectivity factor is obtained by database query optimizer for estimating the size of data that satisfy a query condition. This allows to choose the optimal query execution plan. In this paper we consider the problem of selectivity estimation for inequality predicates based on two attributes, therefore the proposed solution allows to estimate the size of data that satisfy theta-join conditions. The proposed method is based on Discrete Fourier Transform and convolution theorem. DFT spectrums are used as representations of distribution of attribute values. We compute selectivity either performing Inverse DFT (for an inequality condition based on two attributes) or avoiding it (for a single-attribute range one). Selectivity calculation is a time-critical operation performed during an on-line query preparing phase. We show that by applying parallel processing capabilities of Graphical Processing Unit, the implementation of the method satisfies the assumed time constraint.

Metrics-Based Auto Scaling Module for Amazon Web Services Cloud Platform

One of the key benefits of moving an application to the cloud is the ability to easy scale horizontally when the workload increases. Many cloud providers offer a mechanism of auto scaling which dynamically adjusts the number of virtual server instances, on which given system is running, according to some basic resource-based metrics like CPU utilization. In this work, we propose a model of auto scaling which is based on timing statistics: a high order quantile and a mean value, which are calculated from custom metrics, like execution time of a user request, gathered on application level. Inputs to the model are user defined values of those custom metrics. We developed software module that controls a number of virtual server instances according to both auto scaling models and conducted experiments that show our model based on custom metrics can perform better, while it uses less instances and still maintains assumed time constraints.

GPU-Accelerated Query Selectivity Estimation Based on Data Clustering and Monte Carlo Integration Method Developed in CUDA Environment

Query selectivity is a parameter that allows to estimate the size of data satisfying a query condition. For complex range query condition it may be defined as multi integral over a multivariate probability density function (PDF). It describes a multidimensional attribute value distribution and may be estimated using the known approach based on a superposition of Gaussian clusters. But there is the problem of an efficient integration of the multivariate PDF. This may be solved by applying Monte Carlo (MC) method which exposes its advantages for high dimensions. To satisfy the time constraint of selectivity calculation, the parallelized MC integration method was proposed in the paper. The implementation of the method is based on CUDA technology. The paper also describes the application designated for obtaining the time-optimal parameter values of the method.

Applying Task-Aggregating Wrapper to CUDA-Based Method of Query Selectivity Calculation Using Multidimensional Kernel Estimator

Query selectivity is a parameter which is used by a query optimizer to estimate size of the data satisfying given query condition. It helps to find the optimal method of query execution. For complex range queries, selectivity calculation requires an estimator of multidimensional PDF of attribute values distribution. Selectivity calculation task is performed during time critical on-line query processing. Applying parallel threads mechanisms available in GPUs may satisfy the time requirements. In the paper we propose the multidimensional kernel-estimator-based method which uses CUDA technology.We also propose the version of this method which may process selectivity calculations for many query conditions at once. This minimizes the time required to transfer between CPU and GPU memory. This is realized by the proposed task-aggregating wrapper module which provides a workload consolidation.

A Performance Comparison of Several Common Computation Tasks Used in Social Network Analysis Performed on Graph and Relational Databases

NoSQL databases are more and more popular, because they fill the gap where traditional relational model of data does not fit. Social network analysis can be an example of an area, where a particular kind of NoSQL database - the graph one seems to be a natural choice. However, relational databases are developed for many years, they include advanced algorithms for indexing, query optimization etc. This raises the question, whether at the field of performance graph database and relation one are competitive. This article tries to give an answer to this question, by comparing performance of two leading databases from both sides: Neo4j and Oracle 11g.

Density-based Community Identification and Visualisation

Community can be generally defined as a sub graph where nodes are more densely connected with each other than with the rest of a network. Such definition makes application of density-based clustering methods to community identification justified and natural. Moreover, density-based methods have many extensions enabling their application to complex data analysis. Therefore, the analysis of the characteristics of density-based clustering methods in application to community identification is important and valuable. The article presents and evaluates new similarity measures that can be utilised by the approaches to density-based community identification. Several experiments on real life and generated networks are performed to show and explain the differences between these measures and to compare them with other methods. The results show that the new measures improve the quality of analysis and that density-based clustering algorithms can be valuable community identification methods.

Maintenance of Custom Applications in the Grid Environment – On Basis of Oracle Enterprise Manager Grid Control and Logback Logging Utility

The article presents functionality and areas of application in complex, corporate computing systems management of Oracle Grid Control. As preliminaries, the authors describe an evolutionary way the Oracle management systems were developed. Next, on basis of Grid Control application, the preset approach for building management systems is presented. Finally the authors show the plugin mechanism that allows extending Grid Control functionality to administrating of custom systems. On basis of a Logback logging utility a custom plugin development process was shown.