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Mapping spatio-temporal disaster data into MongoDB
Widyani Y.a, Laksmiwati H.a, Bangun E.D.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, 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.This paper describes an approach to map the spatio-temporal disaster data into MongoDB. This process is part of our research with title Prototype Development Disaster Management Information System (DIMaS) which manages disaster data in Indonesia where data is represented by the aspects of space and time. It would be better if spatio-temporal data in DIMaS is stored in spatio-temporal DBMS. But, there is limited number of such DBMS. Furthermore, existing spatio-temporal DBMS such as Secondo, is hard to be integrated with and manipulated through our web application. Therefore, MongoDB is selected as the target DBMS. As a NoSQL database, MongoDB can support large stream data processing and provide sufficient basic features to manage the flow of data space on a specific location along time, to meet the needs of data expression in spatio-temporal environment. Spatio-temporal data has spatio-temporal attributes which is modeled in spatio-temporal research [6] as Object Relational Model. When we use MongoDB, we have to convert these attributes into a collection of JSON documents. The attributes of the spatio-temporal as mpoint (moving point), mline (moving line), or mregion (moving region) must be mapped into the form of JSON document collection. Mapping process has been designed on how JSON documents can be formed in accordance with disaster data that has been stored in Object Relational Model [6]. Some examples on how the object data mapping process carried out in the form of JSON documents will be described in this paper. As a result, it is shown that spatio-temporal data, which is modeled as object-relational, can be converted into JSON documents. As a future work, we have to build a query engine to process a request to these spatio-temporal 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=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Disaster management informations,Document collection,MongoDB,Prototype development,Spatio temporal,Spatio-temporal data,Spatio-temporal environment,Stream data processing[/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]Disaster data,Mapping,MongoDB,Oodbms,Spatio-temporal[/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/ICODSE.2016.7936157[/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]