The implementation of customized system libraries in an exokernel environment is considered as a promising approach in optimizing data mining processes. Customized libraries in exokernel environments have been successfully used in optimizing other applications, and is potentially suitable to demanding applications such as data mining. A prototype, to test our hypothesis, is under construction. This work introduces data mining, the exokernel environment and describes our prototype's building strategy

Camargo, Renata da Silva

Reis, Verônica Lagrange Moutinho dos

Thomé, Antonio Carlos Gay

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Proposing a CustomizedExokernel Library toData MiningRenata da Silva CamargoAntônio Carlos Gay ThoméVerônica Lagrange Moutinho dos ReisProposing a Customized Exokernel Library to Data MiningRenata da Silva Camargo Antônio Carlos Gay ThoméVerônica Lagrange Moutinho dos ReisNúcleo de Computação EletrônicaFederal University of Rio de Janeiro (UFRJ)Caixa Postal 2324, Rio de Janeiro,RJ 20001-970, BRAZILPhone: (55)(21)598-3123FAX: (55)(21)270-8554{ rcamargo, thome, veronica } @nce. ufrj .brAbstractThe implementation of customized system libraries in an exokernel environment is considered asa promising approach in optimizing data mining processes. Customized libraries in exokernel envi-ronments have been successfully used in optimizing other applications, and is potentially suitable todemanding applications such as data mining. A prototype, to test our hypothesis, is under construction.This work introduces data mining, the exokernel environment and describes our prototype's buildingstrategy.Keywords:Knowledge discovery database, data mining, exokernel.1 Introd uctionNow that big companies have enormous quantities of data, stored in electronic format, a fundamentalquestion arise : what to do with it? This is a very common question, especially because being able tobetter maJlag( and use information leads to greater competitiveness and bigger earnings.In order t(1 better use data, it is important to separate the meaningful, relevant one, from the rest.The existence of such large databases, combined with the need to efficiently extract useful information,paved the way to new techniques devoted to search and extract knowledge. These techniques are col-lectively known as Knowledge Discovery Databases (KDD). They provide efficient tools to process anddiscover impoI"tant data, not always obvious, from very large collections of data and their goal is decisionsupport.The benefits irlcurred from this new paradigm are, among others: more accurate predictions; savings madepossible from the usage of better quality data; better marketing strategies; better knowledge betweenproducts alld services [3].The KDD literature [1, 7, 10, 12, 14] is devoted to optimizing the software solution at the applicationleveI, payiJlg no attention to the operating system or to the hardware that supports the application.KDD algoI"ithms incur complex and expensive search techniques. So, some optimizations may not producesignificant improvements. Sometimes the limitations are strongly related to the usage ofresources providedby the operating system.On the ot}ler hand, it has been shown that traditional operating systems (OS) are inflexible and unableto provide applications with the required performance and functionality [2, 6] .One approach in tryingto solve th is problem is to develop customized operating systems. These OS should give flexible accessto system resources to standard applications. The exokernel [6] is an example of an OS environmentdesigned tt) give applications efficient control of alI system's hardware and software resources, while stillguaranteei:lg these resources' protection and security.The main Joal of this research is to determine the feasibility of developing customized libraries for KDD applicatiorLS in an exokernel environment.This paper is further divided into five sections. Section 2 describes the KDD process; section 3 describesthe stages of data mining; section 4 introduces the main concepts of the exokernel paradigm; section 5discusses the suitability of an exokernel environment in supporting data mining applications and, finally,the conclusion1 -.",-Figure 1. Areas invo1ved in the KDD process.2 Knowledge Discovery DatabaseAs the information technology rapidly grows, a general tendency to produce and store increasing quantitiesof data is observed , which leads to larger data bases. The new issue is how to classify increasing quantitiesof data in less time, using less resources in order to increase the value associated with its decision making.KDD comes to fill that need, as it aims at improving organizations' access to always increasin  quantitiesof data, providing techniques to analyze and manipulate these data.KDD is a set of tools and techniques capable of extracting and discovering knowledge that is not obvious,from the manipulation of many, very large databases, targeting decision making [5].The KDD process uses many areas such as: statistics, database technology, machine learning, expertsystems , artificial intelligence, visualization techniques and mathematics (see figure 1).The KDD process is interactive and iterative, and follows five distinct steps [5, 7]:1. Selection.to understand the application domain;.to search prior important knowledge and the application 's goals;.to create a target dataset on which the search will be made.2. Preprocessing.to clean the data, eliminating noise, inconsistencies, redundancies, missing data, errors, etc.23. Transformation.to find useful features to represent the data, based on the desired goals;.to reduce the effective number of variables considered;.to search invariant representations for the data.4. Data mining.to choose the data mining tasks;.to choose the data mining algorithms, the techniques to be used find data patterns;.to use the chosen algorithm to look for interesting patterns.5. Interpretation/Evaluation.to interpret the results and, if necessary, to return to previous steps;.to consolidate knowledge;.to discover and eliminate conflicts;.to incorporate the new knowledge.Figure 2 depicts the five KDD steps.KDD is the overall process of discovering useful knowledge from data. Data mining is a particular step inthis process [5, 7,8,9, 17]: the one that actually extracts the knowledge from the transformed data.3 Data miningData mining is the process of discovering new important correlations, patterns and tendencies, throughthe careful exploration and analysis of very large data sets, using pattern recognition, mathematics andstatistics [7]. Its main motivation is the very large commercial, governmental and scientific databases thathave already exceeded the human capacity to efficiently interpret them.From a global point-of-view, it will become increasingly important to discover new business opportunitiesfrom the careful analysis of the bulky data, stored in corporate systems and that sometimes are considereduseless, after a lifetime of service generating reports and routine maps. The faster that happens, the betterthe competitive conditions to the organization against other organizations and the market. Organizationsthat first use their stored data will have a head start.Some important goals, to data mining, are:3SelectionPre-prTransDataInterpEvFigure 2. Steps of the KDD process..Data \'.isutllization: done before any analysis. Used to qualify and organize the data to be worked on,and to dis(;over new ways of exposing this data such that it becomes more natural and transparentto the users..KnowledgE Discovery: to expose hidden relations, patterns and correlations between different typesof datc, pr( sent in the organization's databases..Data jlccuracy: to improve data consistency to future processing and analysis.The most used techniques in data mining projects are [4, 5, 7, 8, 9]:Visualizatio11;To analyze and observe data in graphical form, could be useful to determine the characteristics applicableto small subsets that could not be noticed by statistics tools.StatisticsUsed to exe(.ute tasks such as grouping, regression analysis and correlation analysis.Induction R ulesTo incur a hypothesis from facts already known, where these facts are database registers and the hypothesis4is a decisio]l tree, which should group data in a meaningful way. Its main advantage is to be easy tounderst;Lnd.Learnin, Bllsed InstancesTo use (lata to generate new instances, based on the approximation of classes according to some metric.Deducti'!}e LearningTo generate knowledge as a byproduct of previous knowledge.Neural " Vet'IJJorksUsed to ider,tify and recognize patterns, to approximate functions and in predictions. These models "learn"to solve a Ploblem based on previous examples. Their "training" may be supervised or unsupervised.Genetic AlgorithmsUsed to find optimal sets of parameters that describe a predictive ful1ction. These are heuristic searchtechniqlles ("ommonly used in optimization problems.These t( chniques support a set of operations and differ in the type of problems they are able to solve. Forexampl(. [5, 7, 9]:.A ;socation: returns similarities among clements of a set;.Sequel1tial Analysis : models the states of the process gel1eratil1g the sequel1ce or extractil1g al1dre Jorting deviations al1d trel1ds over time;.Classificatiol1: maps a data item into ol1e of the severa] predefil1ed classes;.CJust(.rizatiol1: creates the several classes;.R( gression: maps a data item il1to a real-value prediction variable.The SU( cesf of the data mining process is strongly depel1del1t of the suitability of the type of task tothe problem as well as the quality al1d qual1tity of data available. Al1other importal1t factor is thecomputdtiol1al cost il1curred by the complex programs used.Currel1tly, there are mal1y researchers workil1g on optimizing the data mil1il1g processes and algorithms[1, 7, 1(', 12, 14]. However, we propose a new approach to the optimization of data mil1il1g applicatiol1s,though the usage of a more flexible operating systems architecture. The idea is to optimize the operatil1gsystem ,;ervices provided to the applications by, for example, an exokerl1el el1virol1ment usil1g customizedlibrarie ;.54 ExokernelThe operating system controls all the resources, providing the base over which all programs execute. Theoperating system defines the interface between the application and the physical resources. This interfacein implemented by a set of routines that offer services to users an  their code, as well as to other operatingsystem's routines. The basic set of these routines is called nucleus, or kernel. It 's main functions are [13,16]:.to handle interruptions;.to create and eliminate processes;.to synchronize and communicate processes;.scheduling;.to manage the memory;.to manage the file system;.to do input/output;.to do accounting and system safety.One important issue, when implementing operating systems is to provide protection to the kernel. It isalso important to manage resources in the best possible way to the majority of the applications. That is,the algorithms used in managing system resources must optimize their utilization as a whole. However ,with these benefits, comes the lack of flexibility and, sometimes, performance, because applications areforced to use abstractions and specific policies to access hardware resources. For example, one applicationmay need to wait more or be forced to use unnecessary resources if that 's the standard procedure for thisOS.It has been shown that traditional operating systems are unable to give applications the flexibility, func-tionality and performance needed [2, 6]. One solution is to use an operating system that allows userapplications to directly access functions originally performed by the kernel.This is the goal of exokernel, an operating system design developed at the computing laboratory of MIT .Exokernel "exports" all management tasks to the application level and is responsible, at kernel level, toguarantee the protection of the resources by multiplexing the hardware, through the basic kernel routines6Operating SystemHardwareHardwareFigure 3. A traditional operating system and an exokernel.and by implementing capabilities. It gives applications total control over resources, through user-levelcustomized libraries.Figure 3 presents the basic structure of a traditional OS and that of an exokernel. Observe that underexokernel, the applications use customized routines and that these routines will use exokernel to allocateand deallocate hardware resources such as processor, memory and I/O drivers.When the application needs to request some service, it will call the customized library, through systemcalls. Different libraries may implement the same service in different ways, considering different needs ofdifferent applications. A previous experiment [11] showed that some applications' performance is muchbetter when using customized libraries.Some systems were already built, based on the exokernel design. The first one was Aegis and the XOK isthe newer one executing on PCs [11]. Many Unix applications such as gcc, perl, apache, tcsh and telnetrun on it.5 Using Exokernel to Data MiningHaving in mind the urging need organizations have to efliciently obtain new and useful information fromhuge databases, we propose the utilization of libraries customized to data mining applications, in anexokernel environment as, for example, the XOK. Given the complexity of the search engines and theirintense usage of computational power, we believe that a customized library will add flexibility, functionalityand, mainly, performance to these applications.Because most of the data mining applications limitations are strongly related to the facilities provided by7the operating system, a customized library may help performance better than simple code optimizations.The exokernel, as stated previously, was designed to provide efficient protection and control of the availableresources to the user applications. This is achieved by a smal1 kernel that exports al1 the hardware resourcesthrough a low leveI interface and using user-levellibraries that provide the abstractions needed by theapplication [6, 11].Observing the needs and benefits of the involved fields, came the idea of combining both, by building acustomized library to serve data mining applications. The main idea is to define the appropriate policiesto manage the resources used by data mining processes. To evaluate this approach, we are currentlyworking on a project composed of three parts:1. to use a commercial data mining software, on a predefined database. This stage's goal is to analyzeand define the resource utilization, and overheads of as many data mining application as possible;2. to develop a prototype of a library where pertaining resource management policies can be tested.For example, we wil1 experiment with different virtual memory page replacement policies. Thisstage's goal is to determine which are the most adequate management strategies to data mining.This prototype will be implemented in an ExOS environment, using XOK, which is a public domainsoftware and executed under Linux. Its current version is 1.0;3. to evaluate and validate the prototype and libraries.6 ConclusionsWe presented the essence of the KDD process, in particular, the data mining step as wel1 as the exokernelparadigm.We discussed the need big organizations have to improve the access to important data and the techniquesthey can use. These techniques use algorithms that execute over very large databases and require veryexpensive computational power .The idea of building a customized library for data mining applications was inspired by the exokernelparadigm and by the characteristics of KDD applications, that require huge computational resources.As preliminary evaluations demonstrated the feasibility of this approach, we are currently building aprototype, at the Computing Laboratory of the Federal University of Rio de Janeiro.8References[1] Agrawal, R., Stolors, P. The Fourth International Conference on Knowledge Discovery and DataMining. AAAI PRESS, 1998.[2] Anderson T. The Case for Application-Specific Operating Systems. In Third Workshop on WorkstationOperating Systems, 1992, p. 92-94.[3] Berry, M. J. A., Linoff, G. Data Mining Techniques for Marketing, Sales and Customer Support. WileyComputer Publishing, 1997.[4] Bigus, J. P. Data Mining with Neural Networks. McGraw-Hill, 1996.[5] Communications Of The ACM. Data Mining. November: v .39, n. 11, 1996.[6] Engler, D. R., Kaashoek, F., O'Toole, J. Jr. Exokernel: An Operating System Architecture forApplication-Level Resource Management. In Proceedings of the 15th Symposium on Operating SystemsPrincipIes, December: 1995, p. 251-266.[7] Fayyad, U. A., Piatestsky-Shapiro, G., Smyth, P. Advances in Knowledge Discovery and Data Mining.From Data Mining to Knowledge Discovery: An Overview. AAAI Press/The MIT Press 1996, p. 1-34.[8] Freitas, A. A., Lavington, S. H. Mining Very Large Databases With Parallel Processing. KluwerAcademic Publishers, 1998.[9] Han J. pata Mining: Concepts and Techniques. Simon Fraser University, 1996.[10] Heckerman, D., Mannila, H., Pregibon, D., Uthurusamy, R. Proceedings of the Third InternationalConference on Knowledge Discovery and Data Mining. AAAI PRESS, 1998.[11] Kaashoek, F., Engler, D. R., Ganger, G. R., Briceo, H. M., Hunt, R., Mazires, D., Pinckney, T.,Grimm, R., Jannotti, J., Mackenzie, K. Application Performance and Flexibility on Exokernel Systems.In Proceedings of the 16th Symposium on Operating Systems PrincipIes (SOSP), October, 1997.[12] King, R. D., Feng, C., Sutherland, A. A.I. StatLog: Comparison of Classification Algorithms on LargeReal- World Problems. May, june, 1995, p. 289-333.[13] Machado, F. B., Maia, L. P. Arquitetura de Sistemas Operacionais. Ed. LTC, 1996.[14] Proceedings, PADD98, The Second International Conference on The Practical Application of Knowl-edge Discovery and Data Mining. The Westminster Central Hall, London, UK, March, 25-27th 1998.[15] Tanenbaum, A. S. Distributed Operating Systems. Prentice Hall, 1995.9[16] Tanenbaum, A. S. Sistemas Operacionais Modernos. Prentice Hall do Brasil, 1995.[17] Weiss, S. M., lndurkhya, N. Predictive Data Mining a Practical Guide. Morgan Kaufmann Publishers,1998.10

Proposing a customized exokernel library to data mining

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Proposing a customized exokernel library to data mining

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