By Marcus Jürgens
Info warehouses range considerably from conventional transaction-oriented operational database functions. Indexing suggestions and index constructions utilized within the transaction-oriented context are usually not possible for information warehouses.
This paintings develops particular heuristic indexing innovations which approach variety queries on aggregated information extra successfully than these ordinarily utilized in transaction-oriented structures. The e-book provides chapters on:
- the cutting-edge in facts warehouse research
- facts garage and index structures
- discovering optimum tree-based index structures
- aggregated info in tree-based index structures
- functionality versions for tree-based index structures
- and methods for evaluating index constructions.
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Extra resources for Index Structures for Data Warehouses
Sample text
If less than 32 are 0 it is more space efficient to store a list of Tuple IDentifiers instead of a complete bitmap vector [O’Neil and Quass, 1997]. To organize the bitmap vectors and/or the tid-lists, a B-tree is used on top of the bitmap vectors/tidlists. This technique can adapt to the actual data. If there are only a few different values, there are a few bitmap vectors. If the number of different values increases, the bitmap vectors are more sparse. Once less than a certain 1 ) of all values are 1 it might be worth to change the index fraction (e.
This section describes an approach that supports range queries more time efficient than the previous described techniques. There are different definitions of range-based encoding [Chan and Ioannidis, 1998], [Wu and Buchmann, 1998]. In this thesis we use the definition by Chan and Ioannidis. The main idea is to set the bit of the kth bitmap vector to 1 if the value is smaller or equal the kth value. 10) where B indicates the use of the range encoded bitmap index. In this approach range queries of arbitrary size are processed by just reading two bitmap vectors.
Average=sum/count. 3. An aggregation function is called holistic , if there is no constant bound on the size of the storage needed to describe a sub-aggregate. Examples are mostFrequent, ranks, and median. The last kind of functions we generally do not support in the multidimensional case. , 1989] if an order relation on the tree is given. We now provide an example to show how distributive aggregates are used to compute holistic functions if such an ordering can be assumed. 4 is sorted according root R 10 X Y 9 Z 13 count X a 4 d Y 3 b 3 e c 3 f Z g 3 3 h 3 3 i 3 j 4 4 7 3 1 9 4 3 3 3 5 4 8 5 3 3 3 4 3 4 2 3 4 1 4 2 6 4 4 5 5 5 a b c d e f g h i 8 j inner nodes leaf nodes Fig.