Star queries are the most prevalent kind of que-ries in data warehousing, OLAP and business in-telligence applications. Thus, there is an impera-tive need for efficiently processing star queries. To this end, a new class of fact table organiza-tions has emerged that exploits path-based surro-gate keys in order to hierarchically cluster the fact table data of a star schema [DRSN98, MRB99, KS01]. In the context of these new or-ganizations, star query processing changes radi-cally. In this paper, we present a complete ab-stract processing plan that captures all the neces-sary steps in evaluating such queries over hierar-chically clustered fact tables. Furthermore, we present optimizations for surrogate key process-ing and a novel early grouping transformation for grouping on the dimension hierarchies. Our algo-rithms have been already implemented in a commercial relational database management sys-tem (RDBMS) and the experimental evaluation, as well as customer feedback, indicates speed-ups of orders of magnitude for typical star que-ries in real world applications.

Hierarchies are an important means to categorize data stored in OLAP systems. OLAP queries follow the drill/slice/dice-paradigm and therefore exhibit navigation patterns that follow the hierarchy of a dimension. In real-world applications, hierarchies are often unbalanced and share levels, resulting in complex hierarchy structures. So far, encoding methods for simple structured hierarchies have been introduced to handle hierarchies efficiently for query processing. In this paper we propose the HINTA algorithm to compute the clustering order for complex hierarchies by linearization. The physical clustering of OLAP data computed by HINTA significantly improves the performance of OLAP queries. HINTA enables clustering of complex hierarchies that can share hierarchy levels in several classifications over one dimension. 1

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Introduction On-line reorganization is and will be a major problem for transaction systems of the 1990s and the 2000s. Mainframes are rapidly being replaced by workstation farms. Most large software systems must eventually be ported to these new cheaper hardware architectures. The new hardware architectures lend themselves to a client-server software architecture. Presentation services are off-loaded to client workstations. Even servers are no longer on mainframes, but instead on collections of workstations, each perhaps responsible for only a part of the database. Application software will have to be rewritten to conform to the new hardware and software architectures. In addition, some companies may want to take advantage of newer object-oriented software and object-relational systems and new indexing and presentation options. They must then reorganize their data to make the best use of these new software systems. But at the same time, large companies such as airlines and ba

: A multidimensional access method offering significant performance increases by intelligently partitioning the query space is applied to relational database management systems (RDBMS). We introduce a formal model for multidimensional partitioned relations and discuss several typical query patterns. The model identifies the significance of multidimensional range queries and sort operations. The discussion of current access methods gives rise to the need for a multidimensional partitioning of relations. A detailed analysis of space partitioning focussing especially on Z-ordering illustrates the principle benefits of multidimensional indexes. After describing the UB-Tree and its standard algorithms for insertion, deletion, point queries, and range queries, we introduce the spiral algorithm for nearest neighbor queries with UB-Trees and the Tetris algorithm for efficient access to a table in arbitrary sort order. We then describe the complexity of the involved algorithms and give solutio...

Analysis-oriented database applications, such as data warehousing or customer relationship management, play a crucial role in the database area. In general, the multidimensional data model is used in these applications, realized as star or snow-flake schemata in the relational world. The so-called star queries are the prevalent type of queries on such schemata. All database vendors have extended their products to support star queries efficiently.

Abstract: Analysis-oriented database applications, such as data warehousing or customer relationship management, play a crucial role in the database area. In general, the multidimensional data model is used in these applications, realized as star or snow-flake schemata in the relational world. The so-called star queries are the prevalent type of queries on such schemata. All database vendors have extended their products to support star queries efficiently. However, mostly reporting queries benefit from the optimizations, like pre-aggregation, while adhoc queries usually lack efficient support. We present the DBMS Transbase in this paper, which provides a new physical organization of the data based on hierarchical clustering and multidimensional clustering combined with multidimensional indexing. In combination with new query optimizations (e.g., hierarchical pre-grouping) significant performance improvements are achieved. The paper describes how the new technology is implemented in the Transbase product and how it is made available to the user as transparently as possible. The benefits are illustrated with a real-world data warehousing scenario. 1