Preview

iPolytech Journal

Advanced search

CLASSIFICATION SCHEME-BASED GENERATION OF MULTIDIMENSIONAL INFORMATION SYSTEM METADATA

https://doi.org/10.21285/1814-3520-2018-12-145-156

Abstract

The purpose of the paper is development of a method for generating metadata of a multidimensional information system by classification scheme conjugation. Each classification scheme is a hierarchy of dimension values (members) related to a separate structural component of the observed phenomenon. The method is based on the identification of groups of dimension values that are associated with the groups of values of other dimensions. The groups of members for different dimensions are used to generate clusters of member combinations. Cluster combinations are generated by the Cartesian product of groups of members. The metadata of the information system are presented as a set of possible member combinations, which is formed as a set of clusters. To solve this complex problem the observed phenomenon is considered as a set of structural components. Separate sets of dimensions, which are semantically related to the structural components of the observed phenomenon, are distinguished from the complete set of dimensions of the information system. The semantic relationships identified in the analysis of the structural component allow to generate a hierarchy of groups of dimension values and represent all of them in the form of a graph - a classification scheme associated with the structural component. In information systems with a multidimensional representation of a subject domain, data cubes are characterized by high sparseness, which complicates metadata generation. The classification schemes describe specific aspects of the metadata associated with the individual structural components of the observed phenomenon. Conjugation of the classification schemes allows to obtain a complete description of the metadata. The use of classification schemes provides the opportunity to divide the task of describing the structure of the multidimensional information system analytical space into simpler tasks of analysis of its individual structural components. The conjugation of classification schemes related to different structural components enables the generation of information system metadata. The central place in the metadata belongs to the set of possible member combinations.

About the Authors

M. B. Fomin
Peoples’ Friendship University of Russia (RUDN University)
Russian Federation


E. A. Kuznetsov
Laboratory of New Information Technologies (LANIT)
Russian Federation


S. G. Shorokhov
Peoples’ Friendship University of Russia (RUDN University)
Russian Federation


References

1. Thomsen E. OLAP Solution: Building Multidimensional Information System. NY, Willey Computer Publishing. 2002. 688 p.

2. Висков А.В., Фомин М.Б. Методы описания допустимых сочетаний реквизитов-признаков при использовании многомерных моделей в инфокоммуникационных системах // T-Comm. Телекоммуникации и Транспорт. 2012. № 7. С. 45-47.

3. Hirata, C.M., Lima, J.C. Multidimensional cyclic graph approach: representing a data cube without common sub-graphs. Information Sciences. 2011. Vol. 181. P. 2626-2655. DOI: 10.1016/j.ins.2010.05.012

4. Luo Z.W., Ling T.W., Ang C.H., Lee S.Y., Cui B. Range top/bottom k queries in OLAP sparse data cubes. In: Mayr H.C., Lazansky J., Quirchmayr G., Vogel P. Database and Expert Systems Applications - DEXA 2001. Vol. 2113. P. 678-687. Heidelberg, Springer, 2001. P. 678-687. DOI: 10.1007/3-540-44759-8_66

5. Vitter J.S., Wang M. Approximate computation of multidimensional aggregates of sparse data using wavelets. In: Proceedings of the 1999 International Conference on Management of Data - SIGMOD 1999. New York, ACM. 1999. P. 193-204. DOI:10.1145/304182.304199

6. Messaoud R.B., Boussaid O., Rabaseda S.L. A multiple correspondence analysis to organize data cube. In: Databases and Information Systems IV - DB&IS 2006. Vilnius, IOS Press. 2007. P. 133-146.

7. Karayannidis N., Sellis T., Kouvara Y. CUBE file: a file structure for hierarchically clustered OLAP cube. In: Bertino E., Christodoulakis S., Plexousakis D., Christophides V., Koubarakis M., BЕohm K., Ferrari E. Advances in Database Technology - EDBT 2004, vol. 2992. Heidelberg, Springer. 2004. P. 621-638. DOI: 10.1007/978-3-540-24741-8_36

8. Chen C., Feng J., Xing L. Computation of sparse data cubes with constraints. In: Kambayashi Y., Mohania M., Wob W. Data Warehousing and Knowledge Discovery - DaWaK 2003. Vol. 2737. Heidelberg, Springer. 2003. P. 14-23. DOI: 10.1007/978-3-540-45228-7_3

9. Wang W., Lu H., Feng J., Yu J.X. Condensed cube: an effective approach to reducing data cube size. In: Proceedings of the 18th International Conference on Data Engineering - ICDE 2002. IEEE Computer Society, Washington. 2002. P. 155-165.

10. Gomez L.I., Gomez S.A., Vaisman A.A. generic data model and query language for spatiotemporal OLAP cube analysis. In: Rundensteiner, E., Markl, V., Manolescu, I., Amer-Yahia S., Naumann F., Ari I. Proceedings of the 15-th International Conference on Extending Database Technology - EDBT 2012. New York, ACM. 2012. P. 300-311.

11. Фомин М.Б. Описание метаданных многомерных информационных систем с использованием кластерного метода // Вестник Иркутского государственного технического университета. 2017. Т 21. № 7. С. 78-86. https: doi.org/10.21285/1814-3520-2017-7-78-86.

12. Salmam F.Z., Fakir M., Errattahi R. Prediction in OLAP data cubes. Journal of Information & Knowledge Management. 2016. Vol. 15. No. 2. P. 449-458. DOI: 10.1142/S0219649216500222

13. Fu L.: Efficient evaluation of sparse data cubes. In: Li Q., Wang G., Feng L. Advances in Web-Age Information Management, vol. 3129 - WAIM 2004. Heidelberg, Springer, 2004. P. 336-345. DOI: 10.1007/978-3-540-27772-9_34

14. Romero O., Pedersen T.B., Berlanga R., Nebot V., Aramburu M.J., Simitsis A.: Using semantic web technologies for exploratory OLAP: A survey. IEEE Transactions on Knowledge and Data Engineering. 2015. Vol. 27. No. 2. P. 571-588. DOI: 10.1109/TKDE.2014.2330822

15. Salmam F.Z., Fakir M., Errattahi R. Explanation in OLAP data cubes. Journal of Information Technology Research. 2014. Vol. 7. No. 4. P. 36-78. DOI: 10.4018/jitr.2014100105

16. Orlov Y., Gaidamaka Y., Zaripova E. Approach to estimation of performance measures for SIP server model with batch arrivals. In: Vishnevsky V., Kozyrev D. Distributed Computer and Communication Networks. DCCN 2015, vol 601. Cham, Springer, pp. 141-150. DOI: 10.1007/978-3-319-30843-2_15

17. Висков А.В., Фомин М.Б. Моделирование аналитических измерений в многомерных базах данных // Вестник Иркутского государственного технического университета. 2012. Т. 63. № 4. С. 15-19.


Review

For citations:


Fomin M.B., Kuznetsov E.A., Shorokhov S.G. CLASSIFICATION SCHEME-BASED GENERATION OF MULTIDIMENSIONAL INFORMATION SYSTEM METADATA. Proceedings of Irkutsk State Technical University. 2018;22(12):145-156. (In Russ.) https://doi.org/10.21285/1814-3520-2018-12-145-156

Views: 195


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2782-4004 (Print)
ISSN 2782-6341 (Online)