Orizonturi ale cunoaºterii Volumul 2, nr. 1/2010 SISTEME INFORMATICE CU TEHNICI DATA MINING DESTINATE MEDIULUI DE AFACERI Drd. Mihai Andronie 1 1 Acad

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1 Orizonturi ale cunoaºterii Volumul 2, nr. 1/2010 SISTEME INFORMATICE CU TEHNICI DATA MINING DESTINATE MEDIULUI DE AFACERI Drd. Mihai Andronie 1 1 Academia de Studii Economice, Bucureºti Rezumat: Tehnicile data mining pot fi integrate cu succes în sistemele informatice din multiple domenii pentru a gãsi soluþii la o serie de probleme. Pentru ca aceste tehnici sã poatã fi folosite este nevoie sã fie integrate în sistemele informatice ºi de date care sã fie exploatate. Dintre multiplele domenii de aplicabilitate ale sistemelor data mining unul din cele mai importante este economia. Aplicarea tehnicilor data mining în domeniul economic poate aduce o multitudine de avantaje companiilor care le utilizeazã, atât în aria lor de activitate cât ºi la nivel de management. De aceea pentru viitor se estimeazã o creºtere semnificativã a pieþei sistemelor data mining odatã ce aceastã tehnologie atinge maturitatea. Cuvinte cheie: sistem informatic, data mining, mediu de afaceri, analiza de piaþã. Abstract: Data mining techniques can be successfully integrated into informatics systems from multiple fields to find solutions to problems. For these techniques to be used they need to be integrated into informatics systems with data to be analyzed. Among the many areas of application of data mining systems one of the most important is the economy. Applying data mining techniques in the economy can bring many benefits to companies that use them, both in their business and at management level. Therefore in the future the data mining systems market is expected to significantly, once this technology has reached maturity. Key words: informatics systems, data mining, business environment, market analysis. JEL Classification: C80; C89 Introducere Odatã cu noile realizãri apãrute în domenii cum ar fi tehnologia informaþiei, a bazelor de date, a depozitelor de date sau a altor domenii conexe, acestea au inceput sa îºi facã din ce în ce mai mult simþitã prezenþa în majoritatea zonelor de activitate umanã. În acest fel bazele de date ºi sistemele informatice cu baze de date care incorporeazã ºi tehnici data mining ºi-au fãcut apariþia ºi în mediul de afaceri. Dacã la început bazele de date ºi depozitele de date erau folosite exclusiv pentru gestiunea curenta a afacerilor, indiferent de domeniul de activitate al acestora, în ultimii ani informaþiile acumulate în acestea de-a lungul timpului au început sa fie exploatate ºi în vederea obþinerii de informaþii suplimentare cum ar fi descoperirea de tendinþe sau de modele care ar putea prezenta interes deosebit 116 Orizonturi ale cunoaºterii Volumul 2, nr. 1/2010

2 INFORMATICS SYSTEMS WITH INTEGRATED DATA MINING TECHNIQUES DESIGNED FOR THE ECONOMIC ENVIRONMENT Post-Graduate Ph.D. Mihai Andronie 1 1 Academy of Economic Studies, Bucharest Abstract: Data mining techniques can be successfully integrated into informatics systems from multiple fields to find solutions to problems. For these techniques to be used they need to be integrated into informatics systems with data to be analyzed. Among the many areas of application of data mining systems one of the most important is the economy. Applying data mining techniques in the economy can bring many benefits to companies that use them, both in their business and at management level. Therefore in the future the data mining systems market is expected to significantly, once this technology has reached maturity. Key words: informatics systems, data mining, business environment, market analysis. Résumé: Les techniques d exploration de données peuvent être intégrées avec succès dans les systèmes informatiques provenant de plusieurs champs à trouver des solutions à une série de problèmes. Pour que ces techniques puissent être utilisées, elles doivent être intégrées dans les systèmes informatiques et des basses de données à exploiter. Parmi les nombreux domaines d application des systèmes d exploration de données, l une des plus importantes est l économie. L application de techniques d exploration de données dans l économie peut apporter de nombreux avantages aux entreprises qui les utilisent, tant dans leur domaine d activité qu au niveau de la gestion d entreprise. Par conséquent, à l avenir on estime augmenter considérablement les systèmes d exploration de données du marché dès que cette technologie arrive à la maturité. Mots-clés: système informatique, exploration des données, milieu d affaires, analyse de marché. Zusammenfassung: Die Data Mining Techniken können erfolgreich in die informatischen Knowledge horizons Volume 2, no. 1/2010 Systeme aus zahlreichen Bereichen mit einbezogen werden, damit man Lösungen zu einer Reihe von Problemen findet. Damit man diese Techniken verwendet werden können, ist es notwendig, dass sie in informatische Systeme und Dateisysteme integriert werden, die dann bearbeitet werden. Unter den vielen Anwendungsbereichen der Data Mining Systeme ist die Wirtschaft einer der wichtigsten. Die Anwendung der Data Mining Systeme in dem Wirtschaftsbereich kann eine Vielzahl von Vorteilen den Firmen bringen, die sie sowohl in ihrem Bereich als auch auf Management Niveau verwenden. Deshalb schätzt man für die Zukuft eine bedeutende Zunahme der Data Mining Techniken auf dem Markt, sobald diese Technologie reif genug wird. Schlusselwort: informatischen Systeme, data mining, Geschaftsbereich, Markt analyse JEL Classification: C80; C89 Introduction With new developments occurring in areas such as information technology, databases, data warehousing and other related areas, they began to make their appearance more and more in most areas of human activity. In this way databases and informatics systems with databases that incorporate data mining techniques have emerged in the business environment. At their beginning, databases and data warehouses were used exclusively for the actual management of affairs, whatever their field of activity but in recent years the information that was stored by companies over time began to be exploited in order to obtain new information such as the discovery of additional trends or patterns 117

3 pentru proprietarul datelor [3]. Astfel firmele au început sã îºi dea seama de beneficiile aduse de exploatarea avansatã a datelor ºi sã investeascã în sisteme de analizã avansatã a datelor denumite ºi sisteme data mining. Aceste sisteme, deºi la început scumpe, au reuºit sã se impunã pe piaþã datoritã multiplelor avantaje pe care le aduc, atât în mediul de afaceri cât ºi în multe alte domenii. Pentru viitor se estimeazã o creºtere semnificativã a pieþei sistemelor data mining odatã ce aceastã tehnologie se apropie de maturitate [4]. Acest lucru nu înseamnã totuºi cã nu vor mai apãrea noi tehnici ºi metode de analizã avansatã a datelor sau cã cele deja existente nu vor fi îmbunãtãþite odatã ce tehnologia avanseazã. Pentru o firmã care doreºte sã foloseascã un sistem de data mining pentru optimizarea propriei activitãþi existã disponibile pe piaþã o serie de sisteme de data mining care pot fi integrate in sistemul informatic al firmei respective în caz cã acesta existã ºi este compatibil. Un sistem informatic care integreazã tehnici data mining poate fi adaptat pentru cazul particular al unei firme, în funcþie de datele de care aceasta dispune ºi de cerinþele acesteia. 1. Caracteristici reprezentative Pentru ca un sistem informatic cu tehnici data mining incorporate sã fie profitabil pentru o firmã din mediul de afaceri este necesar ca acesta sã îndeplineascã anumite cerinþe. Caracteristicile pe care un sistemul informatic trebuie sã le aibã sunt în legãturã ºi cu necesitãþile firmei unde acesta va fi utilizat. În primul rând, în cazul unei firme care activeazã într-un domeniu economic, indiferent care este acesta, este necesar sã existe în prealabil un sistem informatic propriu sau cel puþin date pentru a fi exploatate înainte de a putea folosi un sistem data mining. Fig. 1. Integrarea unui sistem data mining în sistemul informatic al unei firme. În mare parte din situaþii sistemul informatic al unei firme constã dintr-o colecþie de aplicaþii care sunt interconectate ºi au acces la date, de cele mai multe ori stocate în baze de date relaþionale. Utilizatorii sistemului informatic, anagajaþi ai firmei, au acces la aceste date folosind aceste aplicaþii (Fig. 1.). Pentru integrarea unui sistem data mining în sistemul informatic al unei firme acesta trebuie sa poatã prelua date din sistemul informatic utilizând tehnici pentru analiza avansatã a acestora, cum ar fi: gruparea, asocierea, previziunea, regresia, etc. În urma analizei se pot extrage din datele existente informaþii ºi cunoºtinþe utile la diferite nivele în cadrul firmei, de la activitatea de bazã pânã la managementul de vârf. Un alt aspect care trebuie luat în considerare înainte de a lua decizia de a integra un sistem data mining în sistemul informatic al unei firme este importanþa datelor de care firma dispune. Datele pe care firma le deþine sau la care are acces 118 Orizonturi ale cunoaºterii Volumul 2, nr. 1/2010

4 that could be of particular interest to the data owner [3]. In this way companies began to realize the benefits of advanced data exploitation and they began to invest in advanced data analysis systems known as data mining systems. These systems, although initially expensive, managed to impose on the market because of the variety of benefits they bring, both in business and in many other areas. For the future is expected a significant increase in the data mining systems market once this technology is approaching maturity [4]. This does not mean that there will not be discovered new techniques and advanced data analysis methods or that the already existing ones will not be improved once the technology progresses. For a company that wants to use a data mining system to optimize its activities there are available on the market a multitude of data mining products that can be integrated into the informatics system of the company in case it exists and it is compatible. An informatics system that integrates data mining techniques can be adapted to the particular case of a company, according to data available and its requirements. 1. Representative features For an informatics system with incorporated data mining techniques to be profitable for a company it is necessary that it meets certain requirements. The features that an informatics system should have are in connection with the specific of the company where it needs to be used. First, if a company operating in an economic field, whatever its domain of activity, it must previously own an informatics system or at least data to be exploited before it can use a data mining system. Fig. 1. Integration of a data mining system in the informatics system of a company. In most of the cases a company s informatics system consists of a collection of applications that are interconnected and have access to data, often stored in relational databases. Information system users, employees of the company, have access to the data using these applications (Fig. 1.). To integrate a data mining system in a company s informatics system it must be able to retrieve data from the informatics system and use its advanced analysis techniques, such as clustering, association, Knowledge horizons Volume 2, no. 1/2010 prediction, regression, etc. Following the analysis of existing data there can be extracted useful information and knowledge at different levels within the firm, from the basic work to top management. Another aspect to be considered before deciding to buy and integrate a data mining system in a company s informatics system is the importance of the data that company has. The data which the firm owns or can access must be relevant and 119

5 trebuiesc sã fie relevante ºi sã conþinã potenþiale cunoºtinþe care sa fie cãutate folosind tehnicile data mining. Pentru aceasta se poate face o evaluare a datelor la care firma are acces. În cele mai multe din cazuri utilizarea tehnicilor data mining aduce avantaje pentru firme, indiferent de domeniul de activitate al acestora. Nu în ultimul rând trebuiesc luate în considerare ºi costurile implementãrii unui sistem data mining. Pentru firmele mici acestea pot fi importante lucru care poate încetini procesul pãtrundere a produselor data mining pe aceastã piaþã. Pe de altã parte pentru firmele mari folosirea acestor produse ºi-a arãtat deja avantajele. 2. Domenii de aplicabilitate Sistemele informatice au un domeniu de aplicabilitate foarte larg. Acestea pot fi întâlnite în aproape orice domeniu de activitate umanã care poate fi informatizat. Acolo unde existã astfel de sisteme informatice ºi unde se adunã cantitãþi mari de date, fie în baze de date fie în depozite de date, se pot folosi ºi sisteme data mining incorporate. Sistemele data mining, folosind tehnici specializate, aduc informaþii ºi cunoºtinþe utile domeniului în care sunt aplicate pe baza analizei avansate a datelor deja existente. Se poate spune cã existã anumite domenii în care se pot utiliza cu precãdere tehnici data mining, unde avantajele aduse de folosirea acestora sunt foarte mari. Dintre aceste domenii se pot enumera: Cercetãri ºtiinþifice. În domeniul ºtiinþelor existã cantitãþi foarte mari de date care pot fi exploatate pentru a descoperi tendinþe sau alte modele ascunse care pot fi importante pentru domeniul studiat [2]; Economie. În economie utilizarea sistemelor data mining este de primã importanþã datoritã faptului cã exploatarea datelor deþinute de diferiþi agenþi economici le pot aduce acestora avantaje competitive, atât la nivelul producþiei cât ºi la nivelul managementului; în acest articol se pune accentul în special pe utilizarea tehnicilor data mining în domeniul economic; Finanþe bãnci. În domeniul financiar bancar se folosesc tehnici data mining pentru diferite probleme, cum ar fi de exemplu managementul riscului, previziuni, analize financiare, etc. Telecomunicaþii. În domeniul telecomunicaþiilor tehnicile de data mining au multiple aplicaþii dintre care putem sã amintim analiza multidimensionalã a datelor pentru a rezolva probleme cum ar fi aflarea orelor cu trafic maxim sau diferite alte modele de solicitare a reþelelor de comunicaþie [2]. Un exemplu de astfel de aplicatie ar fi în domeniul telefoniei mobile unde este necesar sã se cunoascã modele de variaþie a încãrcãrii reþelei; Securitatea reþelelor. Detecþia intruziei este o altã aplicaþie unde tehnicile data mining sunt folosite cu succes. Odatã cu extinderea fãrã precedent a internetului a devenit din ce in ce mai important ca reþelele de calculatoare private sã fie protejate de accesul nepermis a unor persoane din exterior [2]. Diverse tehnici data mining fac posibilã evitarea acestui lucru. 3. Aplicatii informatice cu tehnici data mining incorporate in sistemele informatice economice În domeniul economic existã o multitudine de beneficii aduse de integrarea tehnicilor data mining în sistemele informatice. În acest paragraf vor fi amintite câteva dintre tehnicile cele mai utilizate împreunã cu aria lor de aplicabilitate în domeniul economic. Printre cele mai utile aplicaþii ale data mining în domeniul economic se numarã acelea din aria distribuþiei de mãrfuri cu amãnuntul. Existã magazine clasice sau on-line care înregistreazã în propriile baze de date informaþii referitoare la vânzãri, clienþi, etc. Avem în acest caz diferite aplicaþii ale tehnicilor data mining cum ar fi: Bine cunoscuta problemã de marketing [3] a analizei coºului de cumpãrãturi în urma cãreia se pot obþine diferite relaþii între produsele care se vând împreunã; Folosirea de tehnici data mining care sã ajute la proiectarea de depozite de date [2] pentru o aplicaþie specificã. In acest fel se poate mai uºor determina ce dimensiuni sã fie incluse într-un astfel de model; Determinarea eficienþei campaniilor de vânzãri [2] este o altã problemã din domeniul economic la care se poate gãsi o soluþie aplicând diferite tehnici data mining; Analiza comportamentului clienþilor; aceºtia pot fi sau nu loiali faþã de companie, pot sã rãspundã mai mult sau mai puþin la anumiþi factori; cunoscând comportamentul clienþilor se poate adopta o strategie care sã maximizeze profiturile. 120 Orizonturi ale cunoaºterii Volumul 2, nr. 1/2010

6 contain potential knowledge to be searched using data mining techniques. For this it should be made an assessment of the data which the company has access. In most of the cases using data mining techniques provide benefits for companies, whatever their field of activity. Last but not least there should be considered and the costs of the implementation of a data mining system. For small businesses the costs can be important which can slow down the penetration of data mining products in this market. On the other hand for large companies the using of these products has already shown benefits. 2. Fields of application Informatics systems have a very wide field of application. They can be found in almost every domain of human activity that can be computerized. Where such systems exist and where large amounts of data are collected, whether in databases or data warehouses, incorporated data mining systems may be used. Data mining systems, using specialized techniques, bring information and knowledge useful to the field where they are applied based on advanced analysis of existing data. One can say that there are areas where there can be used data mining techniques, especially where the benefits of their use are very high. Among these areas we can enumerate the following: Scientific research. In science there are large amounts of data that can be exploited to uncover hidden trends or patterns that may be important for the studied domain [2]; Economy. In economics the use of data mining systems is of prime importance because the exploitation of data held by different companies can bring them competitive advantages, both in the production and management levels; this article focuses in particular on the use of data mining techniques in the economic environment; Finance and banks. In the area of banking financial data mining techniques are used for finding solutions to different problems, such as risk management, forecasting, financial analysis, etc. Telecommunications. Data mining techniques used for telecommunications have many applications; among them we might mention Knowledge horizons Volume 2, no. 1/2010 multidimensional data analysis to solve problems such as finding the maximum traffic hours or different other load patterns for the communication networks [2]. An example of such application would be in mobile telephony where it is necessary to know the patterns of variation in network load; Network security. Intrusion detection is another application where data mining techniques are used successfully. With the unprecedented expansion of the Internet, it has become increasingly important for private computer networks to be protected from unauthorized access of people from outside [2]. Various data mining techniques make it possible to avoid this. 3. Informatics applications with data mining techniques incorporated in economic informatics systems In the economic field there are many benefits brought by the integration of data mining techniques into the informatics systems. In this paragraph there will be remembered some of the techniques most frequently used together with their range of applicability in the economy. Among the most useful applications of data mining in the economy is retail distribution of goods. There are traditional and online stores that record in their databases information about sales, customers, products, etc. We have in these speciffic case different applications of data mining techniques such as: The well known marketing problem [3] of market basket analysis to from which there can be obtained different relations between the products that are sold together; Using data mining techniques to assist in the design of data warehouses [2] for a specific application. In this way there can be more easily determined what dimensions to be included in such a model; Determining the effectiveness of sales campaigns [2] is another economic problem for witch it can be found a solution by applying various data mining techniques Customer behavior analysis; they may or may not be loyal to the company, may respond more or less to certain factors; knowledge of customer behavior can help a company adopt a strategy to maximize its profits. 121

7 O altã aplicaþie foarte importantã în economie este utilizarea tehnicilor data mining pentru managementul strategic al firmelor. Pentru o companie este de primã importanþã ca deciziile luate la vârf sã fie luate în cunoºtinþã de cauzã ºi nu bazate numai pe talentul ºi experienþa managerului. Acest lucru a devenit posibil odatã cu posibilitatea efectuãrii de previziuni bazate pe datele la care compania are acces. Acesta este poate domeniul în care tehnicile data mining sunt indispensabile odatã ce datele care vor trebui analizate au ajuns la o cantitate care nu mai poate fi gestionatã în mod tradiþional. Concluzii Tehnicile data mining au domenii de aplicabilitate foarte variate, acestea fiind folosite acolo unde exista volume mari de date pentru a fi exploatate. Acestea pot fi integrate în sistemele informatice utilizate în mediul economic. Aplicarea tehnicilor data mining în domeniul economic poate aduce o multitudine de avantaje companiilor care le utilizeazã, în aria lor de activitate cât ºi la nivel de management. Din aceastã cauzã ne putem aºtepta ca piaþa de produse pentru data mining sã creasca semnificativ în urmãtorii ani. BIBLIOGRAFIE [1] Mihai ANDRONIE - Modern data mining techniques, The Ninth International Conference on Informatics in Economy IE, Bucureºti 2009, p , ISBN ; [2] Jiawei Han, Micheline Kamber: Data Mining. Concepts and Techniques, Second Edition, Elsevier, 2006; [3] David Hand, Heikki Mannila, Padhraic Smith: Principles of Data Mining, Prentice Hall India, 2006; [4] Daniel Andersson, Hannes Fries: Data Mining Maturity. A Quantitative Study of Large Companies in Sweden, Jonkoping University, Master s Thesis in Informatics, 2008; [5] Maria Andronie, Mihai Andronie: Data Mining Techniques Used in Metallurgic Industry, Metalurgia International vol. XIV (2009) special issue no.12, ISSN ; [6] Andrei Mihalache, Adrian GHENCEA, Ion LUNGU: Adaptive Information Systems the premise for Self Learning Systems. Proceedings of the International Business Information Management Conference (13th IBIMA) on 9 10 November 2009, Marrakech, Morocco, ISBN: ; [7] Andrei MIHALACHE, Cornel VINTILA, Adela CORNESCU A New Model Approach for Business Applications in Enterprise 2.0 Environment. Proceedings of the World Multiconference on APPLIED ECONOMICS, BUSINESS AND DEVELOPMENT (AEBD 09), Tenerife, Canary Islands, Spain, pp , WSEAS Press 2009, ISBN: , ISSN: ; [8] Zenovic. Gherasim, Doina Fusaru, Maria Andronie: Sisteme informatice pentru asistarea deciziei economice, Editura Fundatiei România de Mâine Bucureºti, Orizonturi ale cunoaºterii Volumul 2, nr. 1/2010

8 Another very important application in economics is the use of data mining techniques for strategic management of businesses. For a company it is of prime importance that the decisions at the top to be taken on an informed basis and not based solely on the talent and experience of the manager. This becomes reasonable with the possibility of making predictions based on the data that the company owns or has access to. This is perhaps the area where data mining techniques are necessary once the data to be analyzed reaches a quantity that can not be managed in a traditional way. Conclusions Data mining techniques have a very wide domain of applicability, being used where there are very large amounts of data to be exploited. In order to do this they can be integrated into informatics systems used in the economic environment. Applying data mining techniques in the economy can bring many benefits to companies that use them, in both their business and management level. Therefore we can expect the market for data mining products will grow significantly in the following years. REFERENCES [1] Mihai ANDRONIE - Modern data mining techniques, The Ninth International Conference on Informatics in Economy IE, Bucureºti 2009, p , ISBN ; [2] Jiawei Han, Micheline Kamber: Data Mining. Concepts and Techniques, Second Edition, Elsevier, 2006; [3] David Hand, Heikki Mannila, Padhraic Smith: Principles of Data Mining, Prentice Hall India, 2006; [4] Daniel Andersson, Hannes Fries: Data Mining Maturity. A Quantitative Study of Large Companies in Sweden, Jonkoping University, Master s Thesis in Informatics, 2008; [5] Maria Andronie, Mihai Andronie: Data Mining Techniques Used in Metallurgic Industry, Metalurgia International vol. XIV (2009) special issue no.12, ISSN ; [6] Andrei Mihalache, Adrian GHENCEA, Ion LUNGU: Adaptive Information Systems the premise for Self Learning Systems. Proceedings of the International Business Information Management Conference (13th IBIMA) on 9 10 November 2009, Marrakech, Morocco, ISBN: ; [7] Andrei MIHALACHE, Cornel VINTILA, Adela CORNESCU A New Model Approach for Business Applications in Enterprise 2.0 Environment. Proceedings of the World Multiconference on APPLIED ECONOMICS, BUSINESS AND DEVELOPMENT (AEBD 09), Tenerife, Canary Islands, Spain, pp , WSEAS Press 2009, ISBN: , ISSN: ; [8] Zenovic. Gherasim, Doina Fusaru, Maria Andronie: Sisteme informatice pentru asistarea deciziei economice, Editura Fundatiei România de Mâine Bucureºti, Knowledge horizons Volume 2, no. 1/