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Query Handling of Temporal Data with Different Calendar Systems and Time Granularities
Dwisaputra R.a, Widagdo T.E.a
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, 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]© 2019 IEEE.Temporal data is a type of data that changes over time, this kind of data have been used in information systems that involves recording the history of the entity. Those things make temporal data processing becomes important. Temporal data can have different time granularities or different calendar systems. Meanwhile, different time granularities and different calendar systems cannot be handled yet with a well-known DBMS. Hence, in this thesis, a tool will be built to help a DBMS to support temporal data with different calendar systems and different time granularities. This tool consists of two parts, i.e. DBMS extension and application modules. DBMS extension can be used to handle the processing of temporal data with different calendar systems and time granularities with the implementation of some data types to store time, metadata to store time information, and functions to validate and convert time in the DBMS. The application module can be used to translate the query given by the user, so the user doesn’t have to enter complex queries. The application module also simplifies the process done by the database administrator when they enter the definition of new time granularity. The evaluation is done by adding two different calendar systems and two new time granularities. Next, simple queries are executed that operate data using the calendar systems and time granularities. Simple queries that are tested cover data temporal operation SELECT, INSERT, DELETE, and JOIN. This testing is done to make sure that the tool can handle different calendar systems and time granularities. The test result shows that the tool can run the queries and give expected query results.[/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]Application module,Complex queries,Database administrators,Query results,Temporal Data,Temporal operation,Time granularities,Time information[/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]calendar system,ontology,temporal data,time granularity[/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/ICoDSE48700.2019.9092750[/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]