A Two-Tiered Segmentation Approach for Transaction Data Warehousing

Xiufeng Liu, Huan Huo, Nadeem Iftikhar, Per Sieverts Nielsen

Research output: Chapter in Book/Report/Conference proceedingContribution to book/anthologyResearchpeer-review


Data warehousing populates data from source systems into a central data warehouse (DW) through extraction, transformation, and loading (ETL). Massive transaction data are routinely recorded in a variety of applications such as retail commerce, bank systems, and Website management. Transaction data records a time and relevant reference data needed for a particular transaction record. It is non-trivial for a standard ETL to process transaction data with dependencies and velocity. This paper presents a two-tiered segmentation approach for transaction data warehousing. The approach uses the so-called two-staging ETL to process the detailed records from operational source systems, followed by dimensional data process to populate a dimension data store with star or snowflake schema. The proposed approach is an all-in-one solution capable of processing fast/slowly changing data, and early/late-arriving data. The paper empirically evaluates the proposed method, and the results have shown its effectiveness for transaction data warehousing.
Original languageEnglish
Title of host publicationEmerging Perspectives in Big Data Warehousing
EditorsDavid Taniar, Wenny Rahayu
Number of pages27
PublisherIGI global
Publication dateJun 2019
ISBN (Print)9781522555162
ISBN (Electronic)9781522555179
Publication statusPublished - Jun 2019
SeriesAdvances in Data Mining and Database Management (ADMDM) Book Series


  • technology, engineering and IT

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