Abstract
| Original language | English |
|---|---|
| Title of host publication | Emerging Perspectives in Big Data Warehousing |
| Number of pages | 27 |
| Publication date | 2019 |
| Chapter | 1 |
| DOIs | |
| Publication status | Published - 2019 |
Cite this
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A Two-Tiered Segmentation Approach for Transaction Data Warehousing. / Liu, Xiufeng; Huo, Huan; Iftikhar, Nadeem ; Nielsen, Per Sieverts.
Emerging Perspectives in Big Data Warehousing. 2019.Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
TY - CHAP
T1 - A Two-Tiered Segmentation Approach for Transaction Data Warehousing
AU - Liu, Xiufeng
AU - Huo, Huan
AU - Iftikhar, Nadeem
AU - Nielsen, Per Sieverts
PY - 2019
Y1 - 2019
N2 - 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 timestamp 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.
AB - 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 timestamp 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.
U2 - 10.4018/978-1-5225-5516-2.ch001
DO - 10.4018/978-1-5225-5516-2.ch001
M3 - Book chapter
BT - Emerging Perspectives in Big Data Warehousing
ER -