Advanced access to multidimensional data

• Autorzy: Białecki A. , Wdowiak S.
• Języki publikacji: EN

Abstrakty

EN

One of the biggest problems in Data Mining is scalability of developed solutions. It is implied by the following factors: amount of data, splitting data on partitions, curse of dimensionality, many different types of data in the same repository, data neighbourhood and many others. In this paper we propose review of use clustering and space filling in for different areas of data mining. Access to multidimensional data and "what if' queries, typical for OLAP, data warehouses etc. have been discussed. A dedicated GIS Warehouse system as au example of spatial data system is another area of Multidimensional Hierarchical Clustering/ Hierarchy Interleaving. It requires only minor modifications of the typical star schema. Multidimensional data have been presented as a new area for `Z-curves' access methods. Also the idea of `cubegrades' has been reviewed as useful for high level data analysis. Cubegrades are generalised version of association rules.

Słowa kluczowe

EN

multidimensional data; association rules; MHC/HI technique; Z-curves; cubegrades

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Informatyka Teoretyczna i Stosowana
• Rocznik: 2003
• Tom: R. 3, nr 5
• Strony: 7--18
• Opis fizyczny: Bibliogr. 11 poz., 7 rys.

Twórcy

autor

Białecki A.

• Department of Electronics and Computer Science, Warsaw University of Technology Nowowiejska 15/19, 00-665 Warszawa

autor

Wdowiak S.

• Department of Electronics and Computer Science, Warsaw University of Technology Nowowiejska 15/19, 00-665 Warszawa

Bibliografia

• [1] Dobosz P., Wdowiak S., Rybiński H., Technika MHC/HI jako nowa metoda organizacji i dostępu do danych wielowymiarowych w hurtowniach danych i systemach GIS, 2002.
• [2] Gaede V., Günther O., Multidimensional Data Access.
• [3] Gancarz L., Wdowiak S., GIS Warehouse - federated DBMS environment for spatial data ware-housing, Proc. of Databases, Data Warehousing and Knowledge Discovery, Baden Baden 2003.
• [4] Imielinski T., Khachiyan L., Abdulghani A., Cubegrades: Generalizing Association Rules, Data Mining and Knowledge Discovery 2002, 6, 219-257.
• [5] Imielinski T., Virmani A., M-SQL: A query language for database mining, Data Mining and Knowledge Discovery 1999, 3, 373-408.
• [6] Imielinski T., Virmani A., Abdulghani A., DMajor - Application Programming Interface for Database Mining, Data Mining and Knowledge Discovery 1999, 3, 347-372.
• [7] Markl V., Ramsak F., Bayer R., Improving OLAP Performance by Multidimensional Hierarchical Clustering, IEEE Computer Society, Montreal 1999.
• [8] Markl V., MISTRAL, Processing Relational Queries using a Multidimensional Access Technique, Ph.D. Thesis, Munich 1999.
• [9] Markl V., Bauer M., Bayer R., Variable UB-Trees: An efficient way to accelerate OLAP queries.
• [10] Markl V., Bayer R., The Tetris-Algorithm for Sorted Reading from UB-Trees.
• [11] Procopius O., Data Structures for Spatial Database Systems 1997.