Abstract
Multidimensional databases are large collections of data, often historical, used for sophisticated analysis oriented to decision making. This activity is supported by an emerging category of software technology, called On-Line Analytical Processing (OLAP). In spite of a lot of commercial tools already available, a fundamental study for OLAP systems is still lacking. In this paper we introduce a model and a query language to establish a theoretical basis for multi-dimensional data. The model is based on the notions of dimension and f-table. Dimensions are linguistic categories corresponding to different ways of looking at the information. F-tables are the constructs used to represent factual data, and are the logical counterpart of multi-dimensional arrays, the way in which current analytical tools store data. The query language is a calculus for f-tables, and as such it offers a high-level support to multi-dimensional data analysis. Scalar and aggregate functions can be embedded in calculus expressions in a natural way. We discuss on conceptual problems related with the design of multidimensional query languages, and compare our model and language with other approaches.
This work was partially supported by Consiglio Nazionale delle Ricerche and by MURST.
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References
S. Abiteboul and C. Beeri. On the power of languages for the manipulation of complex objects. Technical Report 846, INRIA, 1988.
S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison-Wesley, 1995.
S. Agarwal et al. On the computation of multidimensional aggregates. In Twenty-second Int. Conf. on Very Large Data Bases, Bombay, pages 506–521, 1996.
R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. In Thirteenth IEEE International Conference on Data Engineering, pages 232–243, 1997.
D. Chatziantoniou and K. Ross. Querying multiple features of groups in relational databases. In Twenty-second Int. Conf. on Very Large Data Bases, Bombay, pages 295–306, 1996.
S. Chaudhuri and U. Dayal. Decision support, Data Warehousing, and OLAP. In Tutorials of the Twenty-second Int. Conf. on Very Large Data Bases, 1996.
S. Chaudhuri and K. Shim. Optimization of queries with user-defined predicates. In Twenty-second Int. Conf. on Very Large Data Bases, Bombay, pages 87–98, 1996.
E.F. Codd, S.B. Codd, and C.T. Salley. Providing OLAP (On Line Analytical Processing) to user-analysts: An IT mandate. Arbor Software White Paper, http://www.arborsoft.com.
G. Colliat. OLAP, relational, and multidimensional database systems. ACM SIGMOD Record, 25(3):64–69, September 1996.
M. Escobar-Molano, R. Hull, and D. Jacobs. Safety and translation of calculus queries with scalar functions. In Twelfth ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems, pages 253–264, 1993.
J. Gray, A. Bosworth, A. Layman, and H. Pirahesh. Data Cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. In Twelfth IEEE International Conference on Data Engineering, pages 152–159, 1996.
M. Gyssens, L.V.S. Lakshmanan, and I.N. Subramanian. Tables as a paradigm for querying and restructuring. In Fifteenth ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems, pages 93–103, 1996.
V. Harinarayan, A. Rajaraman, and J. Ullman. Implementing data cubes efficiently. In ACM SIGMOD International Conf. on Management of Data, pages 205–216, 1996.
W.H. Inmon. Building the Data Warehouse. John Wiley, second edition, 1996.
T. Isakowitz, S. Schocken, and H.C. Lucas. Toward a logical/physical theory of spreadsheet modeling. ACM Trans. on Inf. Syst., 13(1):1–37, January 1995.
A. Klug. Equivalence of relational algebra and relational calculus query languages having aggregate functions. Journal of the ACM, 29(3):699–717, 1982.
L. Libkin, R. Machlin, and L. Wong. A query language for multidimensional arrays: Design, implementation, and optimization techniques. In ACM SIGMOD International Conf. on Management of Data, pages 228–239, 1996.
L. Libkin and L. Wong. Aggregate functions, conservative extension, and linear orders. In Workshop on Database Programming Languages, pages 282–294, 1993.
S. Rao, A. Badia, and D. Van Gucht. Providing better support for a class of decision support queries. In ACM SIGMOD International Conf. on Management of Data, pages 217–227, 1996.
A. Shoshani. OLAP and statistical databases: Similarities and differences. In Sixteenth ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems, pages 185–196, 1997.
D. Srivastava, S. Dar, H.V. Jagadish, and A. Levy. Answering queries with aggregation using views. In Twenty-second Int. Conf. on Very Large Data Bases, Bombay, pages 318–329, 1996.
Stanford Technology Group, Inc. Designing the data warehouse on relational databases, 1995. Unpublished manuscript.
Red Brick Systems. Decision-makers, business data, and RISQL, 1995. White Paper, http://www.redbrick.com.
J.L. Weldon. Managing multidimensional data: Harnessing the power. Database Programming & Design, 8(8):24–33, August 1995.
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Cabibbo, L., Torlone, R. (1998). Querying multidimensional databases. In: Cluet, S., Hull, R. (eds) Database Programming Languages. DBPL 1997. Lecture Notes in Computer Science, vol 1369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64823-2_18
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DOI: https://doi.org/10.1007/3-540-64823-2_18
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