Analysis of Performance Metrics from a Database Management System Using Kohonen s Self Organizing Maps
|
|
- Mercy Chase
- 7 years ago
- Views:
Transcription
1 WSEAS Transactions on Systems Issue 3, Volume 2, July 2003, ISSN Analysis of Performance Metrics from a Database Management System Using Kohonen s Self Organizing Maps Claudia L. Fernandez, Jose Torrres-Jimenez Computer Science Department ITESM Campus Cuernavaca Av. Paseo de la Reforma # 182-A Temixco, Mor MEXICO Claudia_l_fernandez@yahoo.com, jtj@itesm.mx Miguel A.Reyes Martinez Cesar A. Coutino-Gomez Computer Science Department, Tecnologico de Monterrey Campus Ciudad de México Calle del Puente Km. 222, México D.F., Mexico migreyes@itesm.mx, ccoutino@itesm.mx Abstract: Data clustering is one of the most interesting data mining problems. Data clustering is the process of discovering groups of data items based on similarities without specifying any additional information. Each cluster contains data items that are similar to some respect and are unlike to the data items in a different cluster. The solution to the clustering problem is more complex when the data items to be classified belong to a large and highdimensional data set. Kohonen s self-organizing maps (SOM) is a neural network that uses an unsupervised learning algorithm, and through a process called self-organization, configures the output units into a topological representation of the input data. SOM provides a solution to the data clustering problem by finding relationships between inputs and outputs and organizing data based on similarities. SOM allows the visualization of highdimensional data with a topology preserving map that reduces multi-dimensional data to a lower-dimensional map or grid of neurons. In this paper, the SOM algorithm is used in conjunction with the hierarchical clustering algorithm Ward to improve the visualization of data clusters. With this process, SQL statements with similar performance metrics are grouped in one cluster and their performance metrics are more alike than the metrics from the SQL transactions in different clusters. The analysis of SQL performance metrics is a current problem in the RDBMS industry that can be solve by applying the SOM algorithm. Keywords: Data mining, Self-organizing maps, DBMS, SQL, Performance analysis. 1 Introduction Data mining is the process of inspecting a large data set with the goal of discovering knowledge previously unknown. In data mining large amounts of data are analyzed and data clustering techniques allow classifying, synthesizing and visualizing large data sets. Data clustering is the process of discovering groups of data items based on similarities without specifying any additional information [4]. Each cluster contains data items that are similar to some respect and are unlike to the data items in a different cluster. The right number of groups where the data items can be classified is unknown. When the data set to be classified is very large and the data items are highly dimensional, i.e. have many components, the solution to the clustering problem is more complex. The Self-Organizing Maps (SOM) is a neural network that with unsupervised learning organizes data by similarities in different clusters.
2 WSEAS Transactions on Systems Issue 3, Volume 2, July 2003, ISSN SOM has been successfully used in data mining to classify and visualize large amounts of data sets that are highly dimensional [8]. Today s business environments rely on Database Management Systems (DBMS) for the management of information. The need for fast response time database systems is ever increasing. Database application users expect to get their reports in few seconds and long running database processes are not acceptable. Companies cannot afford to lose customers because slow response database systems. Every second that a process runs in a DBMS can be translated into money since there are several resources such as hardware and software that are utilized. Having well-tuned database systems is essential for today s enterprises [7]. But maintaining high performance DBMS is not an easy task to achieve. Database administrators are primarily responsible of managing and tuning database performance. One of the biggest challenges they face is that in order to maintain well-tuned database systems, first they need to analyze and understand large amounts of complex performance metrics that Relational Database Management Systems (RDBMS) provide. The analysis of those performance metrics is crucial to identify performance inefficiencies. Oracle is one of the most popular commercial RDBMS used. In RDBMS, data is accessed and modified through Structured Query Language (SQL) statements [5]. Oracle, as well as other RDBMS, generates SQL performance metrics that need to be analyzed in order to understand the database s performance state. The data set of performance metrics typically contains thousands of data items, where each data item contains more than a dozen different metrics or variables. This paper shows how SOM can be used to analyze the performance metrics of a DBMS using as a case study the commercial RDBMS Oracle. The application of the SOM algorithm on performance metrics allows the discovery of patterns. This can assist a database administrator to better understand how SQL statements use different database resources and to identify SQL performance inefficiencies. If performance inefficiencies are identified, then the database administrator can plan on performance tune the database. In order to performance tune a database, it is necessary to know first if there are performance inefficiencies. This paper does not present the techniques for tuning a database system for enhanced performance. It focuses on the analysis of performance metrics using SOM in order to identify performance inefficiencies. The paper is organized as follows: in section 2 we discuss the analysis of performance metrics in Oracle; in section 3 we present self-organization for performance metrics; section 4 provides the results obtained from experiments and section 5 exposes the conclusions. 2 Analyzing Performance Metrics in Oracle On-going performance monitoring and analysis of Oracle allows database administrators to maintain well-tuned systems. Oracle provides several performance metrics that can be analyzed to understand how the system is performing [5]. If performance problems are identified, Oracle s performance can be improvement by tuning different aspects in the database system. Oracle is a highly tunable RDBMS that permits to make adjustments in order to change performance. The first step in the performance enhancement process is to understand and analyze the different performance metrics that Oracle provides to determine the need for tuning. Sixty percent or more of the performance problems are attributed to SQL statements [7]. SQL is used to retrieve and modify data in a RDBMS. Since sixty percent or more of the performance problems in a database are caused by poor performing SQL statements, then it is crucial to monitor and analyze SQL performance. When a SQL statement is executed, Oracle stores the SQL statement s code and several performance related metrics in one of the buffers in its shared memory, the Oracle SQL Area (OSA) [5]. Only unique SQL statements are stored and some of the performance metrics contain accumulative data that gets accrued in each execution of the SQL statement. The OSA is a set of tables and views in the database system catalog that store the SQL statements and its performance statistics until the database server is shut down or the shared memory is reset [6]. When the OSA fills-up, some elements are released to free up space to store the new ones. The performance metrics that Oracle stores in the OSA for a SQL statement indicate resources usage such as I/O metrics, number of
3 WSEAS Transactions on Systems Issue 3, Volume 2, July 2003, ISSN executions, number of rows processed and others. For every SQL statement, Oracle provides more than a dozen of different performance metrics. The number of performance metrics provided by Oracle is being increased with every Oracle version release. In most production databases, the OSA can contain several thousands of SQL statements. This number can be higher especially when running OLTP (On- Line Transaction Processing Systems) on Oracle. OLTP systems typically execute in a day thousands of SQL statements where each SQL statement may be executed hundreds of times. Therefore, the data set of SQL performance metrics from OSA is highdimensional since every data item or SQL statement has many metrics associated. This data set also contains a large amount of data items. It is essential to use techniques that can assist to analyze the large and highly dimensional data set of SQL performance metrics. Currently in the database industry only simple statistical methods and simple graphical representations are used. The statistical methods used are the minimum, maximum, average and median. The graphical representations used are 2-D bar or XY charts. These methods do not allow the visualization of a large data set and do not properly allow the discovery of relationships between the metrics that can reveal performance patterns and assist to the identification of performance inefficiencies. The Self-Organization Maps developed by Teuvo Kohonen in 1982 [8] have the ability of revealing relationships between data through self-organization. SOM allows the visualization of high-dimensional data with a topology preserving map that reduces multi-dimensional data to a lower-dimensional map. SOM can be used to analyze the SQL performance metrics from Oracle. The justification of the use of SOM for the analysis of performance metrics is that SOM has the ability of self-organizing data items in clusters based on similarities and the discovery of structures between data, as well as the capability of reducing the dimensionality of a data set. 3 Self-Organization of Performance Metrics This section presents the steps and parameters used during experiments for the analysis of performance metrics with SOM. The process of utilizing SOM for the analysis of SQL performance metrics is as follows: 1. Selection of the input data 2. Pre-processing 1. Execution of the SOM with the parameters described in section Visual analysis of the clusters 4. Definition of the clusters using Ward s clustering method The maps presented in this paper were generated with the software Viscovery SOMine [2]. These maps seek to identify the SQL statements that use the most database resources. 3.1 Input Data A data set with SQL performance metrics was generated from simulating the execution of an OLTP application. Oracle on Windows NT was used for the experiments. The data set contains 10,000 data items where each data items corresponds to a SQL statement and its performance metrics 3.2 Pre-processing From all the metrics that OSA provides for a SQL statement, we selected those relevant for performance analysis. Twenty one metrics or variables were selected. Three of those metrics are ratios we calculated with other metrics that are commonly used by database administrators for performance analysis. These metrics provide information on the resources usage and are the following [6]: BUFFER_GETS, BUFFER_GETS/EXECUTIONS, DISK_READS/EXECUTIONS, DISK_READS/BUFFER_GETS, DISK_READS, EXECUTIONS, SHARABLE_MEM, PERSISTENT_MEM, RUNTIME_MEM, SORTS, VERSION_COUNT, LOADED_VERSIONS, OPEN_VERSIONS, USERS_OPENING, USERS_EXECUTING, LOADS, INVALIDATIONS, PARSE_CALLS, ROWS_PROCESSED, COMMAND_TYPE and OPTIMIZER_MODE. Appendix 1 contains the explanation of each metric. All these metrics are numeric except the OPTIMIZER_MODE, which indicates the Oracle s optimizer mode used to execute the SQL statement.
4 WSEAS Transactions on Systems Issue 3, Volume 2, July 2003, ISSN This metric can have any of the following values: CHOOSE, ALL_ROWS, RULE and MULTIPLE_CHILDREN _PRESENT. The values in this variable were pre-processed and replaced with the values CHOOSE=1, ALL_ROWS=2, RULE=3 and MULTIPLE_ CHILDREN_PRESENT=0. All the attributes were escalated to the variance in order to maintain consistent magnitudes between data values. 3.3 SOM parameters The Euclidean distance was used to define the distance between the nodes in the neural network. The neighborhood function used is the Gaussian. Using these parameters the SOM was trained with the Viscovery SOMine software that uses the batch- SOM algorithm. 3.4 Visualization of clusters After the SOM is complete, the map with the selforganization is presented in a graphical manner using different color shades. Blue is used for small values, green for middle values and red is used for the large values. Fig. 1 shows the map after the selforganization. variables to consider and the number of clusters to create. From the visual inspection of Fig. 1 it is possible to see that the color shades highlight 11 different clusters. Only with the visual analysis of the color shades it not possible to exactly determine the borders of each cluster. This is the justification of the use of Ward s algorithm in Viscovery SOMine to facilitate the identification of the clusters from Fig. 1. Fig. 2 shows the 11 clusters after applying Ward s algorithm on all the components. 4 Results For each cluster, the relationship between each of the components can be identified by visually inspecting the feature maps of components (Fig. 3-4). From the maps we identify that the components BUFFER_GETS, BUFFER_GETS/EXECUTIONS, EXECUTIONS, PARSE_CALLS and ROWS_PROCESSED are correlated. All the metrics above have their highest values in Cluster 7. Cluster 7 contains those SQL statements that generated high CPU cycles because of a high number of executions and a high number of accesses to the server s memory. The SQL statements is this cluster are candidates for performance tuning with the goal of decreasing memory usage. Fig. 1 SOM 3.5 Definition of clusters using Ward s methods. Ward is a hierarchical clustering algorithm [3] that in order to form clusters, it requires knowing the Fig. 2 Self-organization of SQL performance metrics in 11 clusters
5 WSEAS Transactions on Systems Issue 3, Volume 2, July 2003, ISSN one of the approaches that Oracle can take for SQL execution [5]. These SQL statements are also candidates for tuning with the goal of decreasing the use of Oracle s shared memory. All the other clusters do not show significant use of database resources and do not indicate performance inefficiencies. Fig. 3 Maps of the components Fig. 4 Maps of the components Another interesting cluster is Cluster 2. This cluster contains the SQL statements with the highest values of DISK_READ/EXECUTIONS. This indicates high I/O usage. It also contains SQL statements with the highest value of PERSISTENT_MEM and middle range values for the metrics: RUNTIME_MEM, SORTS, LOADED_VERSIONS and OPEN_VERSIONS. The SQL statements in this cluster are also candidates for performance tuning with the goal of decreasing I/O usage. Cluster 1 contains the SQL statements with the highest ratio of DISK_READS/BUFFERS_GETS indicating that the I/O usage is higher than the CPU processing. These SQL statements also have the highest values of SHARABLE_MEM (Oracle s shared memory) and RUNTIME_MEM. An interesting aspect is that the SQL statements ran under the OPTIMIZER_MODE=3 (RULE) that is At this point the SOM has been analyzed and different types of SQL statements performance have been identified. Therefore, it is possible to analyze data items not included during the training of the SOM and determine to which cluster they belong by performing a distance analysis. This recall technique makes possible the analysis of new SQL performance metrics based on previous identified groups without the need of re-computing the SOM. 5 Conclusions The analysis of SQL performance metrics is a current problem in the RDBMS industry. This paper demonstrated how Kohonen s Self-Organizing Maps can be used to analyze performance metrics of a RDBMS. With the use of the SOM and Ward s algorithms, clusters of SQL statements are identified. The SQL statements in one cluster have performance metrics more similar than the metrics of the SQL statements in other clusters. We have shown how the visual analysis of the feature maps of components permits the discovery of patterns and relationships between the performance metrics. It also allows the identification of SQL statements with a high usage of database resources to assist database administrators to determine the need for performance tuning different aspects of Oracle. References [1] A. Ultsch. Data Mining and Knowledge Discovery with Emergent Self-Organizing Feature Maps for Multivariate Time Series [2] Eudaptics software gmbh. Viscovery SOMine. Austria (1999) URL [3] Brian Everitt. Cluster Analysis. Halsted Press, New York (1981) [4] Guido Deboeck, Teuvo Kohonen. Visual Explorations in Finance with Self-Organizing Maps. Springer, Berlin (1998)
6 WSEAS Transactions on Systems Issue 3, Volume 2, July 2003, ISSN [5] Oracle9i Database Concepts 01/server.920/a96524/c01_02intro.htm#10525 [6] Oracle Corporation. Oracle8i Reference. Dynamic Performance Views (2000) URL 01/doc/server.817/a76961/ch3156.htm#15879 [7] Richard Niemiec. Oracle Performance Tuning. Oracle Press (1999) [8] Teuvo Kohonen. Self-organizing Maps. Springer, Berlin (1997) Appendix 1 SQL Performance Metrics BUFFER_GETS = Total number of memory blocks read. BUFFER_GETS/EXECUTIONS = Number of memory blocks read for each execution of the SQL statement. The higher this ratio, the more CPU memory consumption the SQL statement requires for the execution. DISK_READS/EXECUTIONS = Number of blocks read from the hard disk for each execution of the SQL statement. If this ration is high, it could indicate that the SQL statement could be I/O resource intensive. DISK_READS/BUFFER_GETS = Ratio between the blocks read from the hard disk and the memory blocks accessed. A high number could indicate that I/O usage is higher that CPU processing. DISK_READS = Number of blocks read from hard disk. EXECUTIONS = Total number of executions. SHARABLE_MEM = Sum of amount (bytes) of sharable memory used. PERSISTENT_MEM = Sum of amount (bytes) of persistent memory used. RUNTIME_MEM = Fixed amount of memory required to execute the process. SORTS = Sum of the number of sorts performed. Sorts perform temporary processing in some of the Oracle s memory areas. VERSION_COUNT = Number of children processes present in the cache. LOADED_VERSIONS = Number of children processes that are present in the cache and have their context heap. OPEN_VERSIONS = Number of child processes open under the parent process. USERS_OPENING = Number of child cursors that are currently open under this current parent. USERS_EXECUTING = Number of users that have any of the child cursors opened. LOADS = Number of times the object was loaded or reloaded. INVALIDATIONS = Total number of invalidations over all the child processes. PARSE_CALLS = Sum of all parse calls to all the child processes under the parent. ROWS_PROCESSED = Total number of rows processed. COMMAND_TYPE = Oracle s command type definition. OPTIMIZER_MODE = Mode under which the SQL statement is executed.
Visualization of Breast Cancer Data by SOM Component Planes
International Journal of Science and Technology Volume 3 No. 2, February, 2014 Visualization of Breast Cancer Data by SOM Component Planes P.Venkatesan. 1, M.Mullai 2 1 Department of Statistics,NIRT(Indian
More informationToad for Oracle 8.6 SQL Tuning
Quick User Guide for Toad for Oracle 8.6 SQL Tuning SQL Tuning Version 6.1.1 SQL Tuning definitively solves SQL bottlenecks through a unique methodology that scans code, without executing programs, to
More informationA Study of Web Log Analysis Using Clustering Techniques
A Study of Web Log Analysis Using Clustering Techniques Hemanshu Rana 1, Mayank Patel 2 Assistant Professor, Dept of CSE, M.G Institute of Technical Education, Gujarat India 1 Assistant Professor, Dept
More informationAn Analysis on Density Based Clustering of Multi Dimensional Spatial Data
An Analysis on Density Based Clustering of Multi Dimensional Spatial Data K. Mumtaz 1 Assistant Professor, Department of MCA Vivekanandha Institute of Information and Management Studies, Tiruchengode,
More informationMonitoring of Complex Industrial Processes based on Self-Organizing Maps and Watershed Transformations
Monitoring of Complex Industrial Processes based on Self-Organizing Maps and Watershed Transformations Christian W. Frey 2012 Monitoring of Complex Industrial Processes based on Self-Organizing Maps and
More informationUSING SELF-ORGANISING MAPS FOR ANOMALOUS BEHAVIOUR DETECTION IN A COMPUTER FORENSIC INVESTIGATION
USING SELF-ORGANISING MAPS FOR ANOMALOUS BEHAVIOUR DETECTION IN A COMPUTER FORENSIC INVESTIGATION B.K.L. Fei, J.H.P. Eloff, M.S. Olivier, H.M. Tillwick and H.S. Venter Information and Computer Security
More informationResponse Time Analysis
Response Time Analysis A Pragmatic Approach for Tuning and Optimizing SQL Server Performance By Dean Richards Confio Software 4772 Walnut Street, Suite 100 Boulder, CO 80301 866.CONFIO.1 www.confio.com
More informationMyOra 3.0. User Guide. SQL Tool for Oracle. Jayam Systems, LLC
MyOra 3.0 SQL Tool for Oracle User Guide Jayam Systems, LLC Contents Features... 4 Connecting to the Database... 5 Login... 5 Login History... 6 Connection Indicator... 6 Closing the Connection... 7 SQL
More informationultra fast SOM using CUDA
ultra fast SOM using CUDA SOM (Self-Organizing Map) is one of the most popular artificial neural network algorithms in the unsupervised learning category. Sijo Mathew Preetha Joy Sibi Rajendra Manoj A
More informationWeb Usage Mining: Identification of Trends Followed by the user through Neural Network
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 617-624 International Research Publications House http://www. irphouse.com /ijict.htm Web
More informationVisualization of large data sets using MDS combined with LVQ.
Visualization of large data sets using MDS combined with LVQ. Antoine Naud and Włodzisław Duch Department of Informatics, Nicholas Copernicus University, Grudziądzka 5, 87-100 Toruń, Poland. www.phys.uni.torun.pl/kmk
More informationSpecific Usage of Visual Data Analysis Techniques
Specific Usage of Visual Data Analysis Techniques Snezana Savoska 1 and Suzana Loskovska 2 1 Faculty of Administration and Management of Information systems, Partizanska bb, 7000, Bitola, Republic of Macedonia
More informationA Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization
A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization Ángela Blanco Universidad Pontificia de Salamanca ablancogo@upsa.es Spain Manuel Martín-Merino Universidad
More informationUsing Data Mining for Mobile Communication Clustering and Characterization
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
More informationPerformance Implications of Various Cursor Types in Microsoft SQL Server. By: Edward Whalen Performance Tuning Corporation
Performance Implications of Various Cursor Types in Microsoft SQL Server By: Edward Whalen Performance Tuning Corporation INTRODUCTION There are a number of different types of cursors that can be created
More informationResponse Time Analysis
Response Time Analysis A Pragmatic Approach for Tuning and Optimizing Database Performance By Dean Richards Confio Software 4772 Walnut Street, Suite 100 Boulder, CO 80301 866.CONFIO.1 www.confio.com Introduction
More informationUSING SELF-ORGANIZING MAPS FOR INFORMATION VISUALIZATION AND KNOWLEDGE DISCOVERY IN COMPLEX GEOSPATIAL DATASETS
USING SELF-ORGANIZING MAPS FOR INFORMATION VISUALIZATION AND KNOWLEDGE DISCOVERY IN COMPLEX GEOSPATIAL DATASETS Koua, E.L. International Institute for Geo-Information Science and Earth Observation (ITC).
More informationDB2 for i. Analysis and Tuning. Mike Cain IBM DB2 for i Center of Excellence. mcain@us.ibm.com
DB2 for i Monitoring, Analysis and Tuning Mike Cain IBM DB2 for i Center of Excellence Rochester, MN USA mcain@us.ibm.com 8 Copyright IBM Corporation, 2008. All Rights Reserved. This publication may refer
More informationWhite Paper April 2006
White Paper April 2006 Table of Contents 1. Executive Summary...4 1.1 Scorecards...4 1.2 Alerts...4 1.3 Data Collection Agents...4 1.4 Self Tuning Caching System...4 2. Business Intelligence Model...5
More informationGeoManitoba Spatial Data Infrastructure Update. Presented by: Jim Aberdeen Shawn Cruise
GeoManitoba Spatial Data Infrastructure Update Presented by: Jim Aberdeen Shawn Cruise Organization Overview Manitoba Innovation Energy and Mines Business Transformation and Technology (BTT) Application
More informationPRACTICAL DATA MINING IN A LARGE UTILITY COMPANY
QÜESTIIÓ, vol. 25, 3, p. 509-520, 2001 PRACTICAL DATA MINING IN A LARGE UTILITY COMPANY GEORGES HÉBRAIL We present in this paper the main applications of data mining techniques at Electricité de France,
More informationEZManage V4.0 Release Notes. Document revision 1.08 (15.12.2013)
EZManage V4.0 Release Notes Document revision 1.08 (15.12.2013) Release Features Feature #1- New UI New User Interface for every form including the ribbon controls that are similar to the Microsoft office
More informationResponse Time Analysis
Response Time Analysis A Pragmatic Approach for Tuning and Optimizing Oracle Database Performance By Dean Richards Confio Software, a member of the SolarWinds family 4772 Walnut Street, Suite 100 Boulder,
More informationCITY UNIVERSITY OF HONG KONG 香 港 城 市 大 學. Self-Organizing Map: Visualization and Data Handling 自 組 織 神 經 網 絡 : 可 視 化 和 數 據 處 理
CITY UNIVERSITY OF HONG KONG 香 港 城 市 大 學 Self-Organizing Map: Visualization and Data Handling 自 組 織 神 經 網 絡 : 可 視 化 和 數 據 處 理 Submitted to Department of Electronic Engineering 電 子 工 程 學 系 in Partial Fulfillment
More informationComparison of Supervised and Unsupervised Learning Classifiers for Travel Recommendations
Volume 3, No. 8, August 2012 Journal of Global Research in Computer Science REVIEW ARTICLE Available Online at www.jgrcs.info Comparison of Supervised and Unsupervised Learning Classifiers for Travel Recommendations
More informationVisual decisions in the analysis of customers online shopping behavior
Nonlinear Analysis: Modelling and Control, 2012, Vol. 17, No. 3, 355 368 355 Visual decisions in the analysis of customers online shopping behavior Julija Pragarauskaitė, Gintautas Dzemyda Institute of
More informationINTERACTIVE DATA EXPLORATION USING MDS MAPPING
INTERACTIVE DATA EXPLORATION USING MDS MAPPING Antoine Naud and Włodzisław Duch 1 Department of Computer Methods Nicolaus Copernicus University ul. Grudziadzka 5, 87-100 Toruń, Poland Abstract: Interactive
More informationIBM DB2: LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs
coursemonster.com/au IBM DB2: LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs View training dates» Overview Learn how to tune for optimum performance the IBM DB2 9 for Linux,
More informationEVALUATION OF NEURAL NETWORK BASED CLASSIFICATION SYSTEMS FOR CLINICAL CANCER DATA CLASSIFICATION
EVALUATION OF NEURAL NETWORK BASED CLASSIFICATION SYSTEMS FOR CLINICAL CANCER DATA CLASSIFICATION K. Mumtaz Vivekanandha Institute of Information and Management Studies, Tiruchengode, India S.A.Sheriff
More informationDBQT - Database Query Tool Manual 1/11. Manual DBQT. Database Query Tool. Document Version: 08-03-17. 2008 unu.ch
1/11 DBQT Database Query Tool Document Version: 08-03-17 2/11 Table of Contents 1. INTRODUCTION... 3 2. SYSTEM REQUIREMENTS... 3 3. GRAPHICAL USER INTERFACE... 4 3.1 MENU BAR... 4 3.2 DATABASE OBJECTS
More informationMAGENTO HOSTING Progressive Server Performance Improvements
MAGENTO HOSTING Progressive Server Performance Improvements Simple Helix, LLC 4092 Memorial Parkway Ste 202 Huntsville, AL 35802 sales@simplehelix.com 1.866.963.0424 www.simplehelix.com 2 Table of Contents
More informationOn the use of Three-dimensional Self-Organizing Maps for Visualizing Clusters in Geo-referenced Data
On the use of Three-dimensional Self-Organizing Maps for Visualizing Clusters in Geo-referenced Data Jorge M. L. Gorricha and Victor J. A. S. Lobo CINAV-Naval Research Center, Portuguese Naval Academy,
More informationReconstructing Self Organizing Maps as Spider Graphs for better visual interpretation of large unstructured datasets
Reconstructing Self Organizing Maps as Spider Graphs for better visual interpretation of large unstructured datasets Aaditya Prakash, Infosys Limited aaadityaprakash@gmail.com Abstract--Self-Organizing
More informationPerformance And Scalability In Oracle9i And SQL Server 2000
Performance And Scalability In Oracle9i And SQL Server 2000 Presented By : Phathisile Sibanda Supervisor : John Ebden 1 Presentation Overview Project Objectives Motivation -Why performance & Scalability
More informationMyOra 4.5. User Guide. SQL Tool for Oracle. Kris Murthy
MyOra 4.5 SQL Tool for Oracle User Guide Kris Murthy Contents Features... 4 Connecting to the Database... 5 Login... 5 Login History... 6 Connection Indicator... 6 Closing the Connection... 7 SQL Editor...
More informationModule 15: Monitoring
Module 15: Monitoring Overview Formulate requirements and identify resources to monitor in a database environment Types of monitoring that can be carried out to ensure: Maximum availability Optimal performance
More informationDB2 for Linux, UNIX, and Windows Performance Tuning and Monitoring Workshop
DB2 for Linux, UNIX, and Windows Performance Tuning and Monitoring Workshop Duration: 4 Days What you will learn Learn how to tune for optimum performance the IBM DB2 9 for Linux, UNIX, and Windows relational
More informationVisual analysis of self-organizing maps
488 Nonlinear Analysis: Modelling and Control, 2011, Vol. 16, No. 4, 488 504 Visual analysis of self-organizing maps Pavel Stefanovič, Olga Kurasova Institute of Mathematics and Informatics, Vilnius University
More informationOptimizing Performance. Training Division New Delhi
Optimizing Performance Training Division New Delhi Performance tuning : Goals Minimize the response time for each query Maximize the throughput of the entire database server by minimizing network traffic,
More informationMyOra 3.5. User Guide. SQL Tool for Oracle. Kris Murthy
MyOra 3.5 SQL Tool for Oracle User Guide Kris Murthy Contents Features... 4 Connecting to the Database... 5 Login... 5 Login History... 6 Connection Indicator... 6 Closing the Connection... 7 SQL Editor...
More informationData topology visualization for the Self-Organizing Map
Data topology visualization for the Self-Organizing Map Kadim Taşdemir and Erzsébet Merényi Rice University - Electrical & Computer Engineering 6100 Main Street, Houston, TX, 77005 - USA Abstract. The
More informationAdvanced Web Usage Mining Algorithm using Neural Network and Principal Component Analysis
Advanced Web Usage Mining Algorithm using Neural Network and Principal Component Analysis Arumugam, P. and Christy, V Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu,
More informationIntroduction. Part I: Finding Bottlenecks when Something s Wrong. Chapter 1: Performance Tuning 3
Wort ftoc.tex V3-12/17/2007 2:00pm Page ix Introduction xix Part I: Finding Bottlenecks when Something s Wrong Chapter 1: Performance Tuning 3 Art or Science? 3 The Science of Performance Tuning 4 The
More informationPerformance rule violations usually result in increased CPU or I/O, time to fix the mistake, and ultimately, a cost to the business unit.
Is your database application experiencing poor response time, scalability problems, and too many deadlocks or poor application performance? One or a combination of zparms, database design and application
More informationSegmentation of stock trading customers according to potential value
Expert Systems with Applications 27 (2004) 27 33 www.elsevier.com/locate/eswa Segmentation of stock trading customers according to potential value H.W. Shin a, *, S.Y. Sohn b a Samsung Economy Research
More informationSelf Organizing Maps: Fundamentals
Self Organizing Maps: Fundamentals Introduction to Neural Networks : Lecture 16 John A. Bullinaria, 2004 1. What is a Self Organizing Map? 2. Topographic Maps 3. Setting up a Self Organizing Map 4. Kohonen
More informationKnowledge Discovery in Stock Market Data
Knowledge Discovery in Stock Market Data Alfred Ultsch and Hermann Locarek-Junge Abstract This work presents the results of a Data Mining and Knowledge Discovery approach on data from the stock markets
More informationRegression Performance Testing with mbrace. Version 1.0 08 September 2013 Author: Michael Kok
Regression Performance Testing with mbrace Version 1.0 08 September 2013 Author: Michael Kok Contents 1 Performance Regression Testing... 3 2 Comparing one transaction type in two releases... 3 3 Comparing
More informationSelf-Organizing g Maps (SOM) COMP61021 Modelling and Visualization of High Dimensional Data
Self-Organizing g Maps (SOM) Ke Chen Outline Introduction ti Biological Motivation Kohonen SOM Learning Algorithm Visualization Method Examples Relevant Issues Conclusions 2 Introduction Self-organizing
More informationThe Complete Performance Solution for Microsoft SQL Server
The Complete Performance Solution for Microsoft SQL Server Powerful SSAS Performance Dashboard Innovative Workload and Bottleneck Profiling Capture of all Heavy MDX, XMLA and DMX Aggregation, Partition,
More informationOracle Database 11g: Performance Tuning DBA Release 2
Oracle University Contact Us: 1.800.529.0165 Oracle Database 11g: Performance Tuning DBA Release 2 Duration: 5 Days What you will learn This Oracle Database 11g Performance Tuning training starts with
More informationOracle Database 12c: Performance Management and Tuning NEW
Oracle University Contact Us: 1.800.529.0165 Oracle Database 12c: Performance Management and Tuning NEW Duration: 5 Days What you will learn In the Oracle Database 12c: Performance Management and Tuning
More informationGraph Database Proof of Concept Report
Objectivity, Inc. Graph Database Proof of Concept Report Managing The Internet of Things Table of Contents Executive Summary 3 Background 3 Proof of Concept 4 Dataset 4 Process 4 Query Catalog 4 Environment
More informationvrealize Operations Manager User Guide
vrealize Operations Manager User Guide vrealize Operations Manager 6.0.1 This document supports the version of each product listed and supports all subsequent versions until the document is replaced by
More informationOracle Enterprise Manager 12c New Capabilities for the DBA. Charlie Garry, Director, Product Management Oracle Server Technologies
Oracle Enterprise Manager 12c New Capabilities for the DBA Charlie Garry, Director, Product Management Oracle Server Technologies of DBAs admit doing nothing to address performance issues CHANGE AVOID
More informationClassification of Engineering Consultancy Firms Using Self-Organizing Maps: A Scientific Approach
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol:13 No:03 46 Classification of Engineering Consultancy Firms Using Self-Organizing Maps: A Scientific Approach Mansour N. Jadid
More informationSAS Application Performance Monitoring for UNIX
Abstract SAS Application Performance Monitoring for UNIX John Hall, Hewlett Packard In many SAS application environments, a strategy for measuring and monitoring system performance is key to maintaining
More informationA Computational Framework for Exploratory Data Analysis
A Computational Framework for Exploratory Data Analysis Axel Wismüller Depts. of Radiology and Biomedical Engineering, University of Rochester, New York 601 Elmwood Avenue, Rochester, NY 14642-8648, U.S.A.
More informationEnergy Efficient MapReduce
Energy Efficient MapReduce Motivation: Energy consumption is an important aspect of datacenters efficiency, the total power consumption in the united states has doubled from 2000 to 2005, representing
More information3D Interactive Information Visualization: Guidelines from experience and analysis of applications
3D Interactive Information Visualization: Guidelines from experience and analysis of applications Richard Brath Visible Decisions Inc., 200 Front St. W. #2203, Toronto, Canada, rbrath@vdi.com 1. EXPERT
More informationData Mining and Neural Networks in Stata
Data Mining and Neural Networks in Stata 2 nd Italian Stata Users Group Meeting Milano, 10 October 2005 Mario Lucchini e Maurizo Pisati Università di Milano-Bicocca mario.lucchini@unimib.it maurizio.pisati@unimib.it
More informationClustering & Visualization
Chapter 5 Clustering & Visualization Clustering in high-dimensional databases is an important problem and there are a number of different clustering paradigms which are applicable to high-dimensional data.
More informationDB2 LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs
Kod szkolenia: Tytuł szkolenia: CL442PL DB2 LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs Dni: 5 Opis: Learn how to tune for optimum the IBM DB2 9 for Linux, UNIX, and Windows
More informationVisualizing an Auto-Generated Topic Map
Visualizing an Auto-Generated Topic Map Nadine Amende 1, Stefan Groschupf 2 1 University Halle-Wittenberg, information manegement technology na@media-style.com 2 media style labs Halle Germany sg@media-style.com
More informationMOC 20467B: Designing Business Intelligence Solutions with Microsoft SQL Server 2012
MOC 20467B: Designing Business Intelligence Solutions with Microsoft SQL Server 2012 Course Overview This course provides students with the knowledge and skills to design business intelligence solutions
More informationThe Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
More informationANALYSIS OF MOBILE RADIO ACCESS NETWORK USING THE SELF-ORGANIZING MAP
ANALYSIS OF MOBILE RADIO ACCESS NETWORK USING THE SELF-ORGANIZING MAP Kimmo Raivio, Olli Simula, Jaana Laiho and Pasi Lehtimäki Helsinki University of Technology Laboratory of Computer and Information
More informationDB Audit Expert 3.1. Performance Auditing Add-on Version 1.1 for Microsoft SQL Server 2000 & 2005
DB Audit Expert 3.1 Performance Auditing Add-on Version 1.1 for Microsoft SQL Server 2000 & 2005 Supported database systems: Microsoft SQL Server 2000 Microsoft SQL Server 2005 Copyright SoftTree Technologies,
More informationPEPPERDATA IN MULTI-TENANT ENVIRONMENTS
..................................... PEPPERDATA IN MULTI-TENANT ENVIRONMENTS technical whitepaper June 2015 SUMMARY OF WHAT S WRITTEN IN THIS DOCUMENT If you are short on time and don t want to read the
More informationLVQ Plug-In Algorithm for SQL Server
LVQ Plug-In Algorithm for SQL Server Licínia Pedro Monteiro Instituto Superior Técnico licinia.monteiro@tagus.ist.utl.pt I. Executive Summary In this Resume we describe a new functionality implemented
More informationCA NSM System Monitoring. Option for OpenVMS r3.2. Benefits. The CA Advantage. Overview
PRODUCT BRIEF: CA NSM SYSTEM MONITORING OPTION FOR OPENVMS Option for OpenVMS r3.2 CA NSM SYSTEM MONITORING OPTION FOR OPENVMS HELPS YOU TO PROACTIVELY DISCOVER, MONITOR AND DISPLAY THE HEALTH AND AVAILABILITY
More informationPerformance Characteristics of VMFS and RDM VMware ESX Server 3.0.1
Performance Study Performance Characteristics of and RDM VMware ESX Server 3.0.1 VMware ESX Server offers three choices for managing disk access in a virtual machine VMware Virtual Machine File System
More informationMONITORING A WEBCENTER CONTENT DEPLOYMENT WITH ENTERPRISE MANAGER
MONITORING A WEBCENTER CONTENT DEPLOYMENT WITH ENTERPRISE MANAGER Andrew Bennett, TEAM Informatics, Inc. Why We Monitor During any software implementation there comes a time where a question is raised
More informationA Review of Data Mining Techniques
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,
More information2002 IEEE. Reprinted with permission.
Laiho J., Kylväjä M. and Höglund A., 2002, Utilization of Advanced Analysis Methods in UMTS Networks, Proceedings of the 55th IEEE Vehicular Technology Conference ( Spring), vol. 2, pp. 726-730. 2002 IEEE.
More informationOracle Database 11g: SQL Tuning Workshop
Oracle University Contact Us: + 38516306373 Oracle Database 11g: SQL Tuning Workshop Duration: 3 Days What you will learn This Oracle Database 11g: SQL Tuning Workshop Release 2 training assists database
More informationMultiscale Object-Based Classification of Satellite Images Merging Multispectral Information with Panchromatic Textural Features
Remote Sensing and Geoinformation Lena Halounová, Editor not only for Scientific Cooperation EARSeL, 2011 Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with
More informationAbout Me: Brent Ozar. Perfmon and Profiler 101
Perfmon and Profiler 101 2008 Quest Software, Inc. ALL RIGHTS RESERVED. About Me: Brent Ozar SQL Server Expert for Quest Software Former SQL DBA Managed >80tb SAN, VMware Dot-com-crash experience Specializes
More informationA Comparison of Oracle Performance on Physical and VMware Servers
A Comparison of Oracle Performance on Physical and VMware Servers By Confio Software Confio Software 4772 Walnut Street, Suite 100 Boulder, CO 80301 303-938-8282 www.confio.com Comparison of Physical and
More informationExpert Oracle Exadata
Expert Oracle Exadata Kerry Osborne Randy Johnson Tanel Poder Apress Contents J m About the Authors About the Technical Reviewer a Acknowledgments Introduction xvi xvii xviii xix Chapter 1: What Is Exadata?
More informationQuick Start Guide. Ignite for SQL Server. www.confio.com. Confio Software 4772 Walnut Street, Suite 100 Boulder, CO 80301 866.CONFIO.
Quick Start Guide Ignite for SQL Server 4772 Walnut Street, Suite 100 Boulder, CO 80301 866.CONFIO.1 www.confio.com Introduction Confio Ignite gives DBAs the ability to quickly answer critical performance
More informationNetBeans Profiler is an
NetBeans Profiler Exploring the NetBeans Profiler From Installation to a Practical Profiling Example* Gregg Sporar* NetBeans Profiler is an optional feature of the NetBeans IDE. It is a powerful tool that
More informationOracle Database 11g: SQL Tuning Workshop Release 2
Oracle University Contact Us: 1 800 005 453 Oracle Database 11g: SQL Tuning Workshop Release 2 Duration: 3 Days What you will learn This course assists database developers, DBAs, and SQL developers to
More information4D WebSTAR 5.1: Performance Advantages
4D WebSTAR 5.1: Performance Advantages CJ Holmes, Director of Engineering, 4D WebSTAR OVERVIEW This white paper will discuss a variety of performance benefits of 4D WebSTAR 5.1 when compared to other Web
More informationSQL Server Administrator Introduction - 3 Days Objectives
SQL Server Administrator Introduction - 3 Days INTRODUCTION TO MICROSOFT SQL SERVER Exploring the components of SQL Server Identifying SQL Server administration tasks INSTALLING SQL SERVER Identifying
More informationGateway Portal Load Balancing
Gateway Portal Load Balancing Pre-requisites We advise you to start by reading our Load Balancing overview. Generated Clients and Web Access There are two ways to connect to a Load Balanced cluster: Using
More informationHadoop Technology for Flow Analysis of the Internet Traffic
Hadoop Technology for Flow Analysis of the Internet Traffic Rakshitha Kiran P PG Scholar, Dept. of C.S, Shree Devi Institute of Technology, Mangalore, Karnataka, India ABSTRACT: Flow analysis of the internet
More informationMANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL
MANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL G. Maria Priscilla 1 and C. P. Sumathi 2 1 S.N.R. Sons College (Autonomous), Coimbatore, India 2 SDNB Vaishnav College
More informationOracle Database In-Memory The Next Big Thing
Oracle Database In-Memory The Next Big Thing Maria Colgan Master Product Manager #DBIM12c Why is Oracle do this Oracle Database In-Memory Goals Real Time Analytics Accelerate Mixed Workload OLTP No Changes
More informationMS SQL Performance (Tuning) Best Practices:
MS SQL Performance (Tuning) Best Practices: 1. Don t share the SQL server hardware with other services If other workloads are running on the same server where SQL Server is running, memory and other hardware
More informationMIDAS. Event Log Viewer User s Guide. Part Number MN/MID-EVLOG.IOM Revision 0
MIDAS Event Log Viewer User s Guide Part Number MN/MID-EVLOG.IOM Revision 0 Table Of Contents: OVERVIEW... 3 STARTING THE EVENT LOG VIEWER... 4 HOW THE VIEWER IS ORGANIZED... 7 DATA VIEW SELECTOR... 7
More informationGraphical Web based Tool for Generating Query from Star Schema
Graphical Web based Tool for Generating Query from Star Schema Mohammed Anbar a, Ku Ruhana Ku-Mahamud b a College of Arts and Sciences Universiti Utara Malaysia, 0600 Sintok, Kedah, Malaysia Tel: 604-2449604
More informationConverged, Real-time Analytics Enabling Faster Decision Making and New Business Opportunities
Technology Insight Paper Converged, Real-time Analytics Enabling Faster Decision Making and New Business Opportunities By John Webster February 2015 Enabling you to make the best technology decisions Enabling
More informationICOM 6005 Database Management Systems Design. Dr. Manuel Rodríguez Martínez Electrical and Computer Engineering Department Lecture 2 August 23, 2001
ICOM 6005 Database Management Systems Design Dr. Manuel Rodríguez Martínez Electrical and Computer Engineering Department Lecture 2 August 23, 2001 Readings Read Chapter 1 of text book ICOM 6005 Dr. Manuel
More informationUsing Database Performance Warehouse to Monitor Microsoft SQL Server Report Content
Using Database Performance Warehouse to Monitor Microsoft SQL Server Report Content Applies to: Enhancement Package 1 for SAP Solution Manager 7.0 (SP18) and Microsoft SQL Server databases. SAP Solution
More informationProactive database performance management
Proactive database performance management white paper 1. The Significance of IT in current business market 3 2. What is Proactive Database Performance Management? 3 Performance analysis through the Identification
More informationOptimizing Your Database Performance the Easy Way
Optimizing Your Database Performance the Easy Way by Diane Beeler, Consulting Product Marketing Manager, BMC Software and Igy Rodriguez, Technical Product Manager, BMC Software Customers and managers of
More informationUser Guide. version 1.0
User Guide version 1.0 December 16, 2010 About Kaseya Kaseya is a global provider of IT automation software for IT Solution Providers and Public and Private Sector IT organizations. Kaseya's IT Automation
More informationPrograma de Actualización Profesional ACTI Oracle Database 11g: SQL Tuning Workshop
Programa de Actualización Profesional ACTI Oracle Database 11g: SQL Tuning Workshop What you will learn This Oracle Database 11g SQL Tuning Workshop training is a DBA-centric course that teaches you how
More informationImproved metrics collection and correlation for the CERN cloud storage test framework
Improved metrics collection and correlation for the CERN cloud storage test framework September 2013 Author: Carolina Lindqvist Supervisors: Maitane Zotes Seppo Heikkila CERN openlab Summer Student Report
More information