The Role of Multidimensional Databases in Modern Organizations

Transcription

1 Ecnmic Insights Trends and Challenges Vl.IV (LXVII) N. 2/ The Rle f Multidimensinal Databases in Mdern Organizatins Ana Tănăsescu Faculty f Ecnmic Sciences, Petrleum-Gas University f Plieşti, Bd. Bucureşti 39, , Plieşti, Rmania atanasescur@yah.cm Abstract The relatinal databases represent an ineffective data strage methd when rganizatins manipulate a large amunt f hetergeneus data and must realize cmplex analyses n these data. Thus, a new cncept is required, the multidimensinal database cncept. In this paper the multidimensinal database cncept will be presented, its characteristics will be described, a cmparative analysis between multidimensinal databases and relatinal databases will be realized and a multidimensinal database will be presented. This database was designed using Pal fr Excel tl and can be used in rder t analyse the academic staff research activity frm a faculty. Keywrds: multidimensinal databases; multidimensinal mdelling; relatinal databases JEL Classificatin: C63; C81 Intrductin The large amunt f hetergeneus data accumulated by rganizatins represents ne f the reasns why rganizatins need a technlgy that allws them t quickly achieve cmplex analyses n these data. Because the relatinal databases are ineffective frm this pint f view, a new cncept is required, the multidimensinal database cncept. Annually, a large vlume f infrmatin regarding the teaching and research activity f the academic staff frm faculty departments is accumulated. The departments managers as well as the teaching prcess and research activity respnsible persns must, peridically, elabrate reprts, synthesizing the infrmatin accumulated in different time perids. A multidimensinal database where this infrmatin will be stred is required t be designed in rder t streamline their activity. In the specialty literature there are als presented ther methds f rganizing and analysing infrmatin frm a mdern rganizatin 1,2. 1 Bucur, C., Opinin Mining Platfrm fr Intelligence in Business, Ecnmic Insights Trends and Challenges, Vl. III (LXVI), N. 3, 2014, pp C a m b r i a, E. e t a l, New Avenues in Opinin Mining and Sentiment Analysis, IEEE Intelligent Systems, Vl. 28, N. 2, pp , March-April 2013.

2 96 Ana Tănăsescu Further, the multidimensinal database cncept and its characteristics will be presented, a cmparative analysis between multidimensinal databases and relatinal databases (RDB) will be achieved and a multidimensinal database designed using Pal fr Excel tl will be described. This database will be used t manage the infrmatin regarding the articles publishing activity f the academic staff frm a faculty. Cncept and Characteristics A multidimensinal database (MDB) represents a database type where the data are stred in cells and each cell psitin is defined by a number f variables named dimensins. Each cell signifies a business event and the dimensins values indicate the mment and the lcatin where the event happens 3. In the specialty literature the fllwing synnyms fr the MDB cncept are used: cube, hypercube, OLAP cube, OLAP database, multidimensinal data stre. Althugh, lgically a MDB resembles a cube, physically it is stred as a cmpressed multidimensinal array, with ffset psitining (Fig. 1 and Fig. 2). Authr Time A1 T1 Quantity Citatins Publicatin P1 Fig. 1. The data cube fr articles publishing activity management. Authr Time A1 T1 Quantity Citatins Publisher P1 Fig. 2. The data cube fr bks publishing activity management. 3 Rainardi, V., Building a Data Warehuse With Examples in SQL Server, Apress, 2007.

3 The Rle f Multidimensinal Databases in Mdern Organizatins 97 The data cube frm Figure 1 has three dimensins: Authr, Publicatin and Time and the cell cntains tw measures: the quantity f articles frm a publicatin and the citatins number. This data cube manages the infrmatin regarding the articles published by the academic staff. As in Figure 1, the data cube frm Figure 2 is intended fr the management f infrmatin abut the bks publishing activity. The data mdel crrespnding t a multidimensinal database design is the multidimensinal data mdel that cntains fact tables with measure type attributes and dimensin tables with descriptive attributes. The members f dimensin tables can be rganized in hierarchies 4,5,6,7,8,9,10,11. In multidimensinal mdelling can be used mre multidimensinal mdelling schemas: star schema, snw flake schema etc. The star schema crrespnding t the data cube frm Figure 1 is an imprved versin f a previusly created schema 12 and it is presented in Figure 3. The bjects f this schema represented in a star frm are: the fact table (Articles_Analysis) that is placed in its centre and the dimensin tables (Authr, Date, Publicatin) that are cnnected t the fact table. Fig. 3. Star schema fr the multidimensinal database. The snw flake schema (Fig. 4) that cntains three mre additinal tables (Functin, Publicatin_Type and Department) is btained by nrmalizing the Authr and Publicatin tables. 4 Airinei, D., Depzite de date, Plirm Publishing Huse, Iaşi, Filip, F. Gh., Decizie asistată de calculatr: decizii, decidenţi metde de bază şi instrumente infrmatice asciate, Tehnical Publishing Huse, Bucharest,, Inmn, W. H., Building the Data Warehuse, 4th Editin, Wiley Publishing, Kimball, R., Rss, M., The Data Warehuse Tlkit: The Cmplete Guide t Dimensinal Mdeling (Secnd Editin), Wiley, Liu, L., Özsu, M. T., Encyclpedia f Database Systems, Springer, Lungu, I., Bâra, A., Sisteme infrmatice executive, ASE Publishing Huse, Bucharest, Vaisman, A., Zimányi, E., Data Warehuse Systems Design and Implementatin, Data- Centric Systems and Applicatins, Springer, Nguyen, T. M., Cmplex Data Warehusing and Knwledge Discvery fr Advanced Retrieval Develpment: Innvative Methds and Applicatins, Infrmatin Science Reference, Pă traş cu, A., Tă n ă sescu A., A Dcument Warehuse Architecture Mdel, The Tenth Internatinal Cnference On Infrmatics In Ecnmy, Educatin, Research & Business Technlgy, Bucharest, May 5-6, 2011.

4 98 Ana Tănăsescu Fig. 4. Snw flake schema fr the multidimensinal database. Cmparative Analysis between Multidimensinal Databases and Relatinal Databases Analysing the specialty literature 13,14,15,16,17,18,19, it cannt be stated that a certain database type is much better that the ther r that the relatinal databases (RDB) will be entirely eliminated frm the market. Bth database types present advantages and disadvantages and it is imprtant t identify the situatins where a certain database type is adequate t be used. The main differences between thse tw database types are emphasized in table 1. As it can be seen in table 1, the MDB stre bth detailed data and aggregated data, in a multidimensinal array frmat, being used, especially, fr data analysis applicatins. Unlike them, in the RDB the data are stred in tables, the RDB representing the key element in the traditinal transactin prcessing systems. In the early 90s 20, there was a data query language nly fr the RDB, this language is the Structured Query Language (SQL), an internatinal standardized language. In 1997 Micrsft has elabrated the Multidimensinal Expressins language (MDX) as a part f the OLE DB fr Olap specificatin. Since 2001 XML fr Analysis (XMLA) has becme the standard fr accessing Olap data, thus, standardizing the MDX language t. 13 D d d s, D., H a s a n, H., G u l d, E., Relatinal versus multidimensinal databases as a fundatin fr nline analytical prcessing, IRIS 22: Prceedings f the 22nd Infrmatin Systems Research Seminar in Scandinavia, 1999, pp Inmn, W. H., p. cit. 15 K i m b a l l, R., R s s, M., p cit. 16 R a d e n, N., Data, Data Everywhere, Infrmatin Week, Octber 30, R a i n a r d i, V., p. cit. 18 Vaisman, A., Zimányi, E., p cit. 19 T h m s e n, E., Slutins: Building Multidimensinal Infrmatin Systems, Wiley, R a d e n, N., p. cit.

5 The Rle f Multidimensinal Databases in Mdern Organizatins 99 Table 1. MDB versus RDB Characteristic MDB RDB Data Organizatin multidimensinal array, cell, dimensins, fact table table Data Level detailed data and aggregated data detailed data Typical Operatin analysis update Query Language MDX SQL Disk Space Use lw large Perfrmance gd weak Flexibility lw high Data Prcessing Time high lw Scalability lw high Design Scpe limited very large Data Access supprt direct access t data supprt indirect access t data The MDB present advantages such as: use a less disk space because they are cmpressed and d nt use indexing in rder t search data; have better perfrmances in servicing the user needs because the aggregated data are precalculated and the strage frmat reduces the input-utput peratins number; ensure a higher perfrmance because they are mdelled taking int accunt nly the requirements f a single users grup. supprt a direct, efficient and fast access t data. Hwever, the MDB have disadvantages t: the data prcessing time is high as the aggregated data need t be recalculated when the peratinal data surces are updated; the scalability decreases as the number f dimensins increases r when the database is very large; the MDB flexibility is lw; the design scpe is limited, thus, as the number f users grups whse requirements must be taken int accunt increases, the mdel risks t fail. The Multidimensinal Database Design The multidimensinal database that can be used t manage infrmatin regarding the articles publishing activity f the academic staff frm a faculty was built using Pal fr Excel tl, created by the Jedx AG cmpany 21,22. The prduct premium versins were named Jedx at the end f the 2011, but, currently, bth pen-surce versins and premium versins are called Jedx. 21 Jedx AG, First Steps with Pal fr Excell 3.2, Pal plan analyse reprt, [accessed n 10 th f June 2015].

6 100 Ana Tănăsescu Pal fr Excel is a MOLAP database, cmpsed f the Pal Olap server and an AddIn fr Excel r OpenOffice Calc cmpnent. Pal is a cell-related, in memry, multidimensinal and hierarchical database 23. I have used the Mdeller tl (Fig. 5) in rder t create the data cube, the cube dimensins, the dimensins base and cnslidated elements. Fig. 5. Olap data cube design with Mdeller tl in Pal fr Excel. Thus, the Statistics data cube 8 dimensins are displayed in Figure 5: Authr, Functin, Department, Publicatin, Publicatin_Type that crrespnd t the five hmnyms dimensin tables frm the snw flake schema; Year and Mnth that detail the Date dimensin table; Measure that cntains the tw measure type attributes (Quanty and Citatins) frm the fact table f the snw flake schema. The base elements and the cnslidated elements f the Publicatin_Type dimensin are shwn in Figure 6. Fig. 6. Adding elements t dimensins. 23 Jedx, A.G., p. cit.

7 The Rle f Multidimensinal Databases in Mdern Organizatins 101 The data frm the Olap data cube that will be used t manage the infrmatin regarding the articles publishing activity can be manually added r can be imprted frm an external data surce (flat text file, ODBC query, ther data cube), using the Data Imprt Wizard tl. The Olap data cube described in this paper was ppulated by imprting the data frm the EvidentaArticle relatinal database, with the SQL query presented in Figure 7. Fig. 7. Imprting data in Pal frm a relatinal database. Cmplex multidimensinal analyses n the data frm the data cube can be realized based n the seven dimensins that cntain nly descriptive elements. A multidimensinal analysis example f the data frm the multidimensinal database is presented in Figure 8 where the articles publishing activity f a prfessr is analyzed n each year and n each publicatin type. Fig. 8. Retrieving data frm the multidimensinal database.

8 102 Ana Tănăsescu Cnclusins The MDB described in this paper can be easily updated and adapted in rder t be used by the research activity respnsible persns frm any faculty, regardless f prfile. It presents the fllwing advantages: prvides a high perfrmance; supprts cmplex analyses; uses a less disk space; supprts direct access t data; is adequate t a limited design scpe (department, faculty). References 1. Airinei, D., Depzite de date, Plirm Publishing Huse, Iaşi, B u c u r, C., Opinin Mining Platfrm fr Intelligence in Business, Ecnmic Insights Trends and Challenges, Vl. III (LXVI), N. 3, 2014, pp C a m b r i a, E. e t a l, New Avenues in Opinin Mining and Sentiment Analysis, IEEE Intelligent Systems, Vl. 28, N. 2, pp , March-April D d d s, D., H a s a n, H., G u l d, E., Relatinal versus multidimensinal databases as a fundatin fr nline analytical prcessing, IRIS 22: Prceedings f the 22nd Infrmatin Systems Research Seminar in Scandinavia, 1999, pp Filip, F. Gh., Decizie asistată de calculatr: decizii, decidenţi metde de bază şi instrumente infrmatice asciate, Tehnical Publishing Huse, Bucharest, Inmn, W. H., Building the Data Warehuse, 4th Editin, Wiley Publishing, Jedx, A.G., First Steps with Pal fr Excell 3.2, Pal plan analyse reprt, Kimball, R., Rss, M., The Data Warehuse Tlkit: The Cmplete Guide t Dimensinal Mdeling (Secnd Editin), Wiley, Liu, L., Özsu, M. T., Encyclpedia f Database Systems, Springer, Lungu, I., Bâra, A., Sisteme infrmatice executive, ASE Publishing Huse, Bucharest, Nguyen, T. M., Cmplex Data Warehusing and Knwledge Discvery fr Advanced Retrieval Develpment: Innvative Methds and Applicatins, Infrmatin Science Reference, P ă traş cu, A., Tă n ă sescu A., A Dcument Warehuse Architecture Mdel, The Tenth Internatinal Cnference On Infrmatics In Ecnmy, Educatin, Research & Business Technlgy, Bucharest, May 5-6, Raden, N., Data, Data Everywhere, Infrmatin Week, Octber 30, Rainardi, V., Building a Data Warehuse With Examples in SQL Server, Apress, Thmsen, E., OLAP Slutins: Building Multidimensinal Infrmatin Systems, Wiley, Vaisman, A., Zimányi, E., Data Warehuse Systems Design and Implementatin, Data- Centric Systems and Applicatins, Springer, [accessed n 10 th f June 2015].