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Handling of internal inconsistency OLAP – Based lock table using Message Oriented Middleware in near real time data warehousing
Wibowo A.a, Akbar S.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.Overlap between data warehouse update and selection for OLAP operation in near real time data warehousing causing OLAP internal inconsistency. OLAP internal inconsistency is an inconsistent result condition from two OLAP queries that should give the same result. Some solutions for this problem have been developed, but overlap is still occurred. Space allocation within data warehouse is also required to make these solutions work. However, saving space has been an issue on data warehouse management. Thus, using space within data warehouse to handle OLAP internal inconsistency should be prevented. This paper propose a mechanism to handle OLAP internal inconsistency that prevent overlap condition between updating and selecting process without needed space within data warehouse. This mechanism is developed by combining lock table and Message Oriented Middleware (MOM). Lock table prevent update process in the data warehouse table that is still used by OLAP operation. It avoid overlap, thus OLAP can be done anytime with consistency result guaranteed. During data warehouse table is being locked, message queue on MOM will be used to save operational data that came from data source. It made operational process ongoing and OLAP internal inconsistency can be handled without use space within data warehouse.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Between data warehouse,Consistency result,ETL,Message oriented middleware,Near real-time datum,OLAP Internal Inconsistency,Operational process,Warehouse management[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ETL,Lock Table,Message Oriented Middleware,Near real time data warehousing,OLAP Internal Inconsistency[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ISITIA.2015.7220001[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]