Histograms for OLAP and Data-Stream Queries

Histograms for OLAP and Data-Stream Queries

Francesco Buccafurri (DIMET, Università di Reggio Calabria, Italy)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch151
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Histograms are an important tool for data reduction both in the field of data-stream querying and in OLAP, since they allow us to represent large amount of data in a very compact structure, on which both efficient mining techniques and OLAP queries can be executed. Significant time- and memory-cost advantages may derive from data reduction, but the trade-off with the accuracy has to be managed in order to obtain considerable improvements of the overall capabilities of mining and OLAP tools. In this chapter we focus on histograms, that are shown in the recent literature to be one of the possible concrete answers to the above requirements.
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Histograms are a lossy compression technique widely used in various application contexts, like query optimization, statistical and temporal databases and OLAP applications. In OLAP, compression allows us to obtain fast approximate answers by evaluating queries on reduced data in place of the original ones. Histograms are well-suited to this purpose, especially in case of range queries (Muthukrishnan & Strauss, 2003).

A histogram is a compact representation of a relation R. It is obtained by partitioning an attribute X of the relation R into k sub-ranges, called buckets, and by maintaining for each of them a few information, typically corresponding to the bucket boundaries, the number of tuples with value of X belonging to the sub-range associated to the bucket (often called sum of the bucket), and the number of distinct values of X of such a sub-range occurring in some tuple of R (i.e., the number of non-null frequencies of the sub-range).

Figure 1 reports an example of 3-bucket histogram, built on a domain of size 12 with 3 null elements. For each bucket (represented as an oval), we have reported the boundaries (on the left and the right side, respectively) and the value of the sum of the elements belonging to the bucket (inside the oval). Observe that, the null values (i.e. the values at 6, 7 and 9) do not occur in any bucket.

Figure 1.

An example of a 3-bucket histogram built on a domain of size 12.


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