Dimensional Data KNN-Based Imputation

TL;DR

This paper introduces a novel imputation method for data warehouse dimensions that combines hierarchical and KNN-based approaches, effectively considering warehouse structure and dependencies to improve data completeness.

Contribution

It presents a new dimension-specific imputation technique integrating hierarchical and KNN methods, addressing limitations of existing fact-focused approaches.

Findings

Effective in handling missing dimension data

Respects data warehouse structure and constraints

Demonstrates efficiency in experimental tests

Abstract

Data Warehouses (DWs) are core components of Business Intelligence (BI). Missing data in DWs have a great impact on data analyses. Therefore, missing data need to be completed. Unlike other existing data imputation methods mainly adapted for facts, we propose a new imputation method for dimensions. This method contains two steps: 1) a hierarchical imputation and 2) a k-nearest neighbors (KNN) based imputation. Our solution has the advantage of taking into account the DW structure and dependency constraints. Experimental assessments validate our method in terms of effectiveness and efficiency.