InterWeaving
MAT 259, 2016
Nefeli Manoudaki
Concept
After the sudden expansion of AI in the creative field, some artist expressed their concern about the traditional art forms. This exploration aims to understand how traditional art forms like textile art are related or influenced by modern technological fields like AI, as reflected in the Seattle library checkout patterns.
Query
SELECT
o.title,
o.itemtype,
o.deweyClass,
GROUP_CONCAT(DISTINCT s.subject
SEPARATOR '; ') AS subjects,
COUNT(*) AS checkout_count
FROM
spl_2016.outraw o
INNER JOIN
spl_2016.subject s ON o.bibNumber = s.bibNumber
WHERE
(LOWER(o.title) LIKE '%artificial intelligence%'
OR LOWER(o.title) LIKE '%creative ai%')
AND o.cout >= '2021-01-01'
AND o.cout < '2024-01-01'
GROUP BY o.title , o.itemtype , o.deweyClass
ORDER BY checkout_count DESC;
SELECT
YEAR(cout) AS year,
MONTH(cout) AS month,
COUNT(*) AS total_checkout_count
FROM
spl_2016.outraw
WHERE
(LOWER(title) LIKE '%artificial intelligence%'
OR LOWER(title) LIKE '%creative ai%')
AND cout >= '2021-01-01'
AND cout < '2024-01-01'
GROUP BY year , month
ORDER BY year , month;
SELECT
o.title,
o.itemtype,
o.deweyClass,
GROUP_CONCAT(DISTINCT s.subject
SEPARATOR '; ') AS subjects,
COUNT(*) AS checkout_count
FROM
spl_2016.outraw o
INNER JOIN
spl_2016.subject s ON o.bibNumber = s.bibNumber
WHERE
(LOWER(title) LIKE '%textile%'
OR deweyClass IN ('745' , '729', '730', '746'))
AND o.cout >= '2021-01-01'
AND o.cout < '2024-01-01'
GROUP BY o.title , o.itemtype , o.deweyClass
ORDER BY checkout_count DESC;
SELECT
YEAR(cout) AS year,
MONTH(cout) AS month,
COUNT(*) AS total_checkout_count
FROM
spl_2016.outraw
WHERE
(LOWER(title) LIKE '%textile%'
OR deweyClass IN ('745' , '729', '730', '746'))
AND cout >= '2021-01-01'
AND cout < '2024-01-01'
GROUP BY year , month
ORDER BY year , month;
Final result
Data were extracted from the SPL database using the Dewey Decimal Classification system and using the specific keyword searches that define each of my categories. “Artificial Intelligence” and “Textile Art”.
The focus was on checkout history over the last three years, analyzing the number of checkouts on each topic per month.
The script code for each topic is divided into two searches. The first part focuses on consolidating the checkout data for the selected keyword or certain Dewey Decimal classifications, then categorizing and combining the Subject information, and later quantifying and organizing by the checkout value. The second part is aggregating the checkout counts across all relevant titles, grouping the results by year and month, and counting the total checkout in each grouped period.
Code