Data warehousing applications cope with enormous data sets in the range of Gigabytes and Terabytes. Queries usually either select a very small set of this data or perform aggregations on a fairly large data set. Materialized views storing pre-computed aggregates are used to efficiently process queries with aggregations. This approach increases resource requirements in disk space and slows down updates because of the view maintenance problem. Multidimensional hierarchical clustering (MHC) of OLAP data overcomes these problems while offering more flexibility for aggregation paths. Clustering is introduced as a way to speed up aggregation queries without additional storage cost for materialization. Performance and storage cost of our access method are investigated and compared to current query processing scenarios. In addition performance measurements on real world data for a typical star schema are presented.

/pdf/improving-olap-performance-by-multidimensional-hierarchical-35zaqkfjyf.pdf

Improving OLAP performance by multidimensional hierarchical clustering

A method of analysing join algorithms based upon the time required to access, transfer and perform the relevant CPU-based operations on a disk page is proposed. The costs of variations of several of the standard join algorithms, including nested block, sort-merge, GRACE hash and hybrid hash, are presented. For a given total buffer size, the cost of these join algorithms depends on the parts of the buffer allocated for each purpose. For example, when joining two relations using the nested block join algorithm, the amount of buffer space allocated for the outer and inner relations can significantly affect the cost of the join. Analysis of expected and experimental results of various join algorithms show that a combination of the optimal nested block and optimal GRACE hash join algorithms usually provide the greatest cost benefit, unless the relation size is a small multiple of the memory size. Algorithms to quickly determine a buffer allocation producing the minimal cost for each of these algorithms are presented. When the relation size is a small multiple of the amount of main memory available (typically up to three to six times), the hybrid hash join algorithm is preferable.

/pdf/join-algorithm-costs-revisited-48fim0ydpx.pdf

Join algorithm costs revisited

Proceedings of the 18th International Conference on Very Large Data Bases

The HIBROWSE (High Resolution Interface for Database Specific Browsing and Searching) design offers improved searching functionality for users of bibliographic databases. The interface provides a multi-windowed view of data stored on a relational database management system, using layered attribute value aggregation and classification. The user interrogates the database by invoking automatically formulated SQL (structured query language) queries that fil ter the contents of each window according to user-selected attribute values. The interface is described using 'hand crafted' screens to create realistic examples of HIBROWSE systems for EPOQUE, the European Parliament Online Query System, INSPEC, MEDLINE and EMBASE.

HIBROWSE for bibliographic database

We propose a new class of algorithms that can be used to speed up the execution of multi-way join queries or of queries that involve one or more joins and a group-by. These new evaluation techniques allow to perform several hash-based operations (join and grouping) in one pass without repartitioning intermediate results. These techniques work particularly well for joining hierarchical structures, e.g., for evaluating functional join chains along key/foreign-key relationships. The idea is to generalize the concept of hash teams as proposed by Graefe et.al [GBC98] by indirectly partitioning the input data. Indirect partitioning means to partition the input data on an attribute that is not directly needed for the next hash-based operation, and it involves the construction of bitmaps to approximate the partitioning for the attribute that is needed in the next hash-based operation. Our performance experiments show that such generalized hash teams perform significantly better than conventional strategies for many common classes of decision support queries.

/pdf/generalised-hash-teams-for-join-and-group-by-29m0jhf1o7.pdf

Generalised Hash Teams for Join and Group-by

This paper examines a partial match retrieval scheme which supports range queries for highly dynamic databases The scheme relies on order preserving multi-attribute hashing In general, designing optimal indexes is NP-hard Greedy algorithms used to determine the optimal indexes for simple partial match queries are not directly applicable because there are a larger number of queries to consider in determining the optimal indexes In this paper we present heuristic algorithms which provide near-optimal solutions The optimisation scheme we propose can be used to design other dynamic file structures such as the grid file, BANG file and multilevel grid file to further enhance their retrieval performance taking into consideration the query distribution

Optimal dynamic multi-attribute hashing for range queries

The join operation is one of the most frequently used and expensive query processing operations in relational database systems. One method of joining two relations is to use a hash-based join algorithm. Hash-based join algorithms typically have two phases, a partitioning phase and a partition joining phase. We describe how an optimal multi-attribute hash (MAH) indexing scheme can be used to reduce the average cost of the partitioning phase of any hash-based join algorithm, by eliminating the partitioning phase entirely for many of the most common join queries. We demonstrate that the technique can be extended to include multiple copies of the data le, each with a diierent organization of the MAH indexing scheme, and that this further reduces the average cost of performing the partitioning phase of the hash join algorithm. We describe a relatively inexpensive method for determining a good MAH indexing scheme. Our experiments show that the schemes found using this method are usually optimal and performs the partitioning phase of the hash join algorithm at least three times faster than using the standard approach. We show that a signiicant change in the query pattern is required for a reorganization of the data le to be necessary, and show that reorganizing the data le is an inexpensive operation.

Using Optimized Multi-Attribute Hash Indexes for Hash Joins.

The sorting or partitioning of relations is very common in relational database systems. Implementations of the join operation include the sort-merge join algorithm, which sorts both relations, and the hash join algorithm, which usually partitions both relations. We describe how clustering records using an optimal multi-attribute hash (MAH) file, taking the query pattern and distribution into account, reduces the average cost of sorting or partitioning. We demonstrate that maintaining multiple copies of a data file, each with a different clustering organization, further reduces the average cost of sorting or partitioning. We describe an inexpensive method for determining a good partitioning index (MAH file organization). Our analysis and experiments show that the partitioning indexes we find are usually optimal and can often partition a relation more than ten times faster than by not using any clustering. We also show that a significant change in the query pattern or distribution is required before a reorganization of a data file is necessary, and that such a reorganization is, in general, an inexpensive operation.

Optimal Clustering of Relations to Improve Sorting and Partitioning for Joins

Generalising Minimal Marginal Increase to Cluster Records in Multi-dimensional Data Files.

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