Bibliographic Notes

There are a good number of introductory-level textbooks on data warehousing and OLAP technology—for example, Kimball, Ross, Thornthwaite, et al. [KRTM08]; Imhoff, Galemmo, and Geiger [IGG03]; and Inmon [Inm96]. Chaudhuri and Dayal [CD97] provide an early overview of data warehousing and OLAP technology. A set of research papers on materialized views and data warehouse implementations were collected in Materialized Views: Techniques, Implementations, and Applications by Gupta and Mumick [GM99].

The history of decision support systems can be traced back to the 1960s. However, the proposal to construct large data warehouses for multidimensional data analysis is credited to Codd [CCS93] who coined the term OLAP for online analytical processing. The OLAP Council was established in 1995. Widom [Wid95] identified several research problems in data warehousing. Kimball and Ross [KR02] provide an overview of the deficiencies of SQL regarding the ability to support comparisons that are common in the business world, and present a good set of application cases that require data warehousing and OLAP technology. For an overview of OLAP systems versus statistical databases, see Shoshani [Sho97].

Gray et al. [GCB⁺97] proposed the data cube as a relational aggregation operator generalizing group-by, crosstabs, and subtotals. Harinarayan, Rajaraman, and Ullman [HRU96] proposed a greedy algorithm for the partial materialization of cuboids in the computation of a data cube. Data cube computation methods have been investigated by numerous studies such as Sarawagi and Stonebraker [SS94]; Agarwal et al. [AAD⁺96]; Zhao, Deshpande, and Naughton [ZDN97]; Ross and Srivastava [RS97]; Beyer and Ramakrishnan [BR99]; Han, Pei, Dong, and Wang [HPDW01]; and Xin, Han, Li, and Wah [XHLW03]. These methods are discussed in depth in Chapter 5.

The concept of iceberg queries was first introduced in Fang, Shivakumar, Garcia-Molina et al. [FSGM⁺98]. The use of join indices to speed up relational query processing was proposed by Valduriez [Val87]. O’Neil and Graefe [OG95] proposed a bitmapped join index method to speed up OLAP-based query processing. A discussion of the performance of bitmapping and other nontraditional index techniques is given in O’Neil and Quass [OQ97].

For work regarding the selection of materialized cuboids for efficient OLAP query processing, see, for example, Chaudhuri and Dayal [CD97]; Harinarayan, Rajaraman, and Ullman [HRU96]; and Sristava et al. [SDJL96]. Methods for cube size estimation can be found in Deshpande et al. [DNR⁺97], Ross and Srivastava [RS97], and Beyer and Ramakrishnan [BR99]. Agrawal, Gupta, and Sarawagi [AGS97] proposed operations for modeling multidimensional databases. Methods for answering queries quickly by online aggregation are described in Hellerstein, Haas, and Wang [HHW97] and Hellerstein et al. [HAC⁺99]. Techniques for estimating the top N queries are proposed in Carey and Kossman [CK98] and Donjerkovic and Ramakrishnan [DR99]. Further studies on intelligent OLAP and discovery-driven exploration of data cubes are presented in the bibliographic notes in Chapter 5.