ORCID Profile
0000-0001-5133-6688
Current Organisation
Technical University of Denmark
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Publisher: ACM
Date: 13-04-2015
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 09-2016
DOI: 10.1109/NBIS.2016.15
Publisher: Springer International Publishing
Date: 2017
Publisher: IGI Global
Date: 07-2016
Abstract: In data warehousing, the data from source systems are populated into a central data warehouse (DW) through extraction, transformation and loading (ETL). The standard ETL approach usually uses sequential jobs to process the data with dependencies, such as dimension and fact data. It is a non-trivial task to process the so-called early-/late-arriving data, which arrive out of order. This paper proposes a two-level data staging area method to optimize ETL. The proposed method is an all-in-one solution that supports processing different types of data from operational systems, including early-/late-arriving data, and fast-/slowly-changing data. The introduced additional staging area decouples loading process from data extraction and transformation, which improves ETL flexibility and minimizes intervention to the data warehouse. This paper evaluates the proposed method empirically, which shows that it is more efficient and less intrusive than the standard ETL method.
Publisher: IGI Global
Date: 2019
DOI: 10.4018/978-1-5225-5516-2.CH001
Abstract: Data warehousing populates data from different 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 record the timest and relevant reference data needed for a particular transaction record. It is a non-trivial task for a standard ETL to process transaction data with dependencies and high velocity. This chapter presents a two-tiered segmentation approach for transaction data warehousing. The approach uses a so-called two-staging ETL method to process detailed records from operational systems, followed by a dimensional data process to populate the data store with a 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. This chapter evaluates the proposed method, and the results have validated the effectiveness of the proposed approach for processing transaction data.
Publisher: Elsevier BV
Date: 06-2019
No related grants have been discovered for Xiufeng Liu.