[Top-K] answering topk queries with multidimen1_selections: the ranking cube Approach

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Answering top-K queries with multidimen1_selections: the ranking cube Approach

In a top-K query, two measurements reflect the performance: a selection condition & a ranking function.

The selection condition dimension may be very high, and the ranking function is not necessarily linear.

This document proposes a model called ranking cube and defines a rank-ware measure, which does not solve the curse of dimension and proposes ranking flagments.

Ii. Cube Structure

Build a ranking cube based on selection dimension. The measure of each cell should be rank-ware.

The most naive method is to put all the related tuple into the cell, so there are two shortcomings: waste of space, not rank-ware.

To save no space, you can only store tuple IDs.

To solve the rank-ware problem, two criteria are defined: geometry-based partition & Block-level data access.

 

So boring. Don't write it first