{"id":8336,"date":"2021-09-23T09:15:28","date_gmt":"2021-09-23T09:15:28","guid":{"rendered":"http:\/\/testing-domain.tech\/?p=8336"},"modified":"2025-01-24T09:05:22","modified_gmt":"2025-01-24T09:05:22","slug":"decoding-iudx-data-descriptors","status":"publish","type":"post","link":"https:\/\/dataforpublicgood.org.in\/cdpg\/blog\/decoding-iudx-data-descriptors\/","title":{"rendered":"Decoding IUDX: Data Descriptors"},"content":{"rendered":"\t\t
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Data silos present formidable roadblocks in the journey of our cities towards becoming data smart. Data silos are isolated islands of data which make it prohibitively difficult to exchange data for its use in other application areas. This prevents active participation of application developers and solution providers which is crucial to tackle the complex urban challenges faced by the cities today. IUDX<\/a> promises to break these innovation stifling data constructs by enabling seamless exchange of data across silos. IUDX neutralizes the data silo threat using the twin firepower of: (a) Standardized and open data access APIs and (b) Linked Data (LD) based data contextualization. In this article we are going to look at the data contextualization part. In particular, we are going to see the Data Descriptor object in action, which is a JSON-LD<\/a> object, and the role played by it in improving the usability and interoperability of data provided by IUDX.<\/span><\/p>

Data descriptors provide consistent and uniform descriptions of attributes appearing in data objects. Data descriptor, which is a linked data object represented in JSON-LD, is associated with a given resource and is present in the IUDX catalogue item corresponding to a given resource. Linked data allows one to share semantics to the data attributes, by linking attributes to data vocabularies, in a machine interpretable fashion and thus provides a promising way out of isolation for the data in the silos.<\/span><\/p>

\u00a0<\/span>Let us understand this with the help of an example. Let there be a data resource with \u2018id\u2019 \u2018cityx-itms\/bus-live-loc\u2019 and let the data packet associated with such a data resource be:<\/span><\/p><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>

<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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{
\u00a0\u00a0\u00a0\u00a0 “route_id”: “1345AD”,
\u00a0\u00a0\u00a0\u00a0 “location”: {
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 “type”: “Point”,
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 “coordinates”: [77.5707078, 13.013814]
\u00a0\u00a0\u00a0\u00a0 },
\u00a0\u00a0\u00a0\u00a0 “speed”: 28.00,
\u00a0\u00a0\u00a0\u00a0 “observationDateTime”: “2020-09-16T13:30:00+05:30”
\u00a0}<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Associated with each resource a meta-information object is present in the IUDX catalogue<\/a> which contains a data descriptor object that helps in understanding the various attributes associated with the above data. In short, this object helps in providing context in which the data can be understood and interpreted. For the above example, the resource item present in the catalogue for the resource with id \u2018cityx-itms\/bus-live-loc\u2019 contains the following data descriptor which a JSON-LD object:<\/span><\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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{
\u00a0\u00a0 “@context”: [
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 “https:\/\/voc.iudx.org.in\/”,
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 {
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 “qudt-unit”: “http:\/\/qudt.org\/vocab\/unit\/”,
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 “dataSchema”: { “@type”: “@id” }
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }
\u00a0\u00a0 ],
\u00a0\u00a0 “type”: [“iudx:DataDescriptor”, “iudx:TransitManagement”],
\u00a0\u00a0 “description”: “Data descriptor for live vehicle position of the public buses in city X.”,
\u00a0\u00a0 “route_id”: {
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “type”: [“ValueDescriptor”],
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “description”: “Route ID assigned to the route for the bus.”,
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “dataSchema”: “iudx:Text”
\u00a0\u00a0 },
\u00a0\u00a0 “last_stop_id”: {
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “type”: [“ValueDescriptor”],
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “description”: “Stop ID\/Stop name of the previous bus stop corresponding to the bus in this observation.”,
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “dataSchema”: “iudx:Text”
\u00a0\u00a0 },
\u00a0\u00a0 “location”: {
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “type”: [“ValueDescriptor”],
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “description”: “The coordinates for the current position of the bus corresponding to this observation.”,
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “dataSchema”: “iudx:Point”
\u00a0\u00a0 },
\u00a0\u00a0 “speed”: {
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “type”: [“ValueDescriptor”],
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “description”: “The Speed of the bus observaed at the last tracked coordinates.”,
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “dataSchema”: “iudx:Number”,
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “unitCode”: “KMH”,
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “unitText”: “kilometre per hour”
\u00a0\u00a0 },
\u00a0\u00a0 “observationDateTime”: {
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “type”: [“ValueDescriptor”],
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “description”: “The time at which the vehicle was last tracked.”,
\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 “dataSchema”: “iudx:DateTime”
\u00a0\u00a0 }
}<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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We note that the above data descriptor object provides important meta-information about all the data attributes present in the data for the corresponding resource. In particular, it provides:<\/p>