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Framework of Semantic Data Warehouse for heterogeneous and incomplete data
Nugraheni E.a,b, Akbar S.a,b, Saptawati G.A.P.a,b
a Research Center for Informatics, Indonesian Institute of Sciences, Bandung, Indonesia
b School of Electronic Egineering and Informatics, 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]© 2016 IEEE.The demand to analyse and extract knowledge derived from the collaboration of various sources has motivated the construction of Semantic Data Warehouse (SDW). The data derived from various external source introduces challenges, because it has heterogeneous structural characteristics, syntax and semantics and gives the possibility of incomplete data sources to provide the data required for analysis. It is possible that the required data is not provided by the original data source, but there are other data that can be used to generate this data. This research proposes a framework for development semantic data warehouse that can handle incomplete data and the heterogeneity data problems (format, syntax, and structure) through the use of ontologies. The framework also provides tools to handle incomplete data source. The available tools function is to transform the relevant objects instances to an external ontology classes to generate the required data. Identification the multidimensional elements and the conceptual design of multidimensional schema uses a hybrid approach that combines requirement-driven and supply-driven. The construction of cube schema using QB4OLAP vocabularies generate multidimensional schema and RDF data cubes that can perform OLAP operations.[/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]External sources,Incomplete data,instances transformation,Multidimensional schemata,RDF data,Requirement-driven,Semantic data warehouse,Structural characteristics[/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]data warehouse,incomplete data source,instances transformation,RDF data,semantics data[/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/TENCONSpring.2016.7519397[/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]