84 lines
3 KiB
Markdown
84 lines
3 KiB
Markdown
---
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author: Claudio Maggioni
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title: Visual Analytics -- Assignment 2 -- Part 2
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geometry: margin=2cm,bottom=3cm
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---
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changequote(`{{', `}}')
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# Indexing
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Similarly to part 1 of the assignment, the first step of indexing is to convert
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the newly given CSV dataset (stored in `data/restaurants_extended.csv`) into a
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JSON-lines file which can be directly used as the HTTP request body of
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Elasticsearch document insertion requests.
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The conversion is performed by the script `./convert.sh`. The converted file
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is stored in the JSON-lines file `data/restaurants_extended.jsonl`.
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The sources of `./convert.sh` are listed below:
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```shell
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include({{../convert.sh}})
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```
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The only change in the script is the way the field containing the restaurant
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location is parsed. In the extended dataset, city, country and continent are in
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this field and separated by `/`. The script maps the three values in separate
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fields and additionally maps the entire string to an additional `cityRaw` field
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which is used in the generation of the runtime field for part 2.
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The sourced of the updated upload script, loading the new index are listed
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below:
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```shell
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include({{../upload.sh}})
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```
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Mappings are stored in `mappings.json` and are identical to the ones in Part 1
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other than for the new location fields and their `.keyword` counterparts
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similarly generated as the old `city` field.
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9499 documents are imported.
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# Data Visualization
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The Dashboard, Canvas, and requested dependencies (like scripted fields and
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stored searched) are stored in the JSON object export file `export.ndjson`.
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Screenshot of the Dashboard and Canvas can be found below.
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The scripted field `continent_scripted` has been generated with the following
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Painless expression:
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```java
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doc['cityRaw.keyword'].value.substring(doc['cityRaw.keyword'].value.lastIndexOf("/") + 1)
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```
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The expression extracts the last portion of the `cityRaw` field, i.e. the
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portion of text between the last `/` and the end of the field, which contains
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the continent.
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# Ingestion Plugin
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Sources for the ingestion plugin can be found in the Gitlab repository:
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[_usi-si-teaching/msde/2022-2023/visual-analytics-atelier/elasticsearch-plugin/ingest-lookup-maggicl_](https://gitlab.com/usi-si-teaching/msde/2022-2023/visual-analytics-atelier/elasticsearch-plugin/ingest-lookup-maggicl).
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The plugin can be built and installed on Elasticsearch with the script
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`./install-on-ec.sh` included in the repository by changing the variable
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`ES_LOCATION` to the path to the local installation of Elasticsearch.
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The plugin works as illustrated in the `README.md` file in the repository, and
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it has been tested with a unit test suite included in its sources.
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The plugin lookup procedure works by splitting the indicated field in words
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(non-empty sequences of non-space characters -- according to the PCRE regular
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expression specification) and matching each word with the given
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substitution map, performing substitutions when needed.
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