Index Structures for Data Warehouses by Marcus Jürgens

By Marcus Jürgens

Information warehouses vary considerably from conventional transaction-oriented operational database purposes. Indexing thoughts and index buildings utilized within the transaction-oriented context usually are not possible for facts warehouses.
This paintings develops particular heuristic indexing strategies which approach variety queries on aggregated facts extra successfully than these ordinarily utilized in transaction-oriented platforms. The e-book provides chapters on:
- the cutting-edge in information warehouse research
- info garage and index structures
- discovering optimum tree-based index structures
- aggregated info in tree-based index structures
- functionality types for tree-based index structures
- and strategies for evaluating index constructions.

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Extra resources for Index Structures for Data Warehouses

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Here, the leafs are created first. Then the upper levels of the tree are built successively from the bottom to the top. The bottom-up approach works best, if all data is known in advance and no changes are made after the create phase. Hybrid techniques can be applied. In this case the index is created efficiently with a bottom-up technique and changes are propagated with the top-down method. Inserting data in a multidimensional index structure incrementally is expensive. , 1997]. If the node exceeds its capacity tuples are loaded into the buffer.

Experiments show that the solutions found by the MIP are only slightly better than solutions found by the heuristic R∗ -tree. The time complexity of the MIP grows exponentially in the size of the input. Because of this time complexity this approach cannot be applied to real world databases. However, for small data sets this technique evaluates how closely the heuristic approaches attain its optimum. In the next chapters we will apply heuristic techniques to organize multidimensional data. Heuristic approach scale much better with the problem size than the MIP approach.

The index structure consists of the two different kinds of nodes: leaf nodes and non-leaf nodes. 2a. Each leaf node contains data entries dataentry = (region, ptr). 2b. Each non-leaf node p contains directory entries direntry = (region, ptr). Each region covers at least the union of all regions of the entries ptr points to. region) direntry ∈ptr 3. Each node, except for the root node, has exactly one predecessor. By region there can be any sub-region of the d-dimensional data space defined. A point can also be modeled as a rectangle with zero area.

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