Changes between Version 30 and Version 31 of VLO/CMDI data workflow framework


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Timestamp:
11/13/15 12:03:57 (9 years ago)
Author:
go.sugimoto@oeaw.ac.at
Comment:

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  • VLO/CMDI data workflow framework

    v30 v31  
    128128[[Image( Dashboard workflow.png)]]
    129129
    130 One of the important aspects toward the Dashboard is that it has two primary functionalities. The first functionality is to control all the ingestion modules (harvester to indexer), so that it can, for example, manually stop the harvesting, or changes the mapping definition, and re-index the published data sets. It will serve as an additional service to the current almost full-automatic ingestion. The second functionality is to monitor the ingestion process. That means each module will communicate with the reports database to provide statistics about a particular data transaction. For instance, harvester will supply the statistics about the outcome of the harvesting, while mapper/normaliser will tell the coverage of facets and controlled vocabularies. Indexer will tell the total number of indexed records and broken links. Based on this database, the Dashboard will be able to produce data quality reports which can not only be viewed in the Dashboard itself, but also in a PDF file which each data provider can access. The reports database could provide API for internal and external services to create a viewer (eg harvesting viewer), but it is optional, because the Dashboard is the main interface to include all in one.
     130One of the important aspects toward the Dashboard is that it has two primary functionalities. The first functionality is to control all the ingestion modules (harvester to indexer), so that it can, for example, manually stop the harvesting, or changes the mapping definition, and re-index the published data sets. It will serve as an additional service to the current almost full-automatic ingestion. The second functionality is to monitor the ingestion process. That means each module will communicate with the reports database to provide statistics about a particular data transaction. For instance, harvester will supply the statistics about the outcome of the harvesting, while mapper/normaliser will tell the coverage of facets and controlled vocabularies. Validator may check the broken links, whereas indexer will tell the total number of indexed records. Based on this database, the Dashboard will be able to produce data quality reports which can not only be viewed in the Dashboard itself, but also in a PDF file which each data provider can access. The reports database could provide API for internal and external services to create a viewer (eg harvesting viewer), but it is optional, because the Dashboard is the main interface to include all in one.
    131131
    132132== Reference ==