Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
IGeLU 2014
1. Analytics @ Lancaster University Library
IGeLU 2014
John Krug, Systems and Analytics Manager, Lancaster University Library
http://www.slideshare.net/jhkrug/igelu-analytics-2014
2. Lancaster University, the Library
and Alma
• We are in Lancaster in the UK North West.
• ~ 12,000 FTE students, ~ 2300 FTE Staff
• Library has 55 FTE staff, building refurbishment in progress
• University aims to be 10, 100 – Research, Teaching, Engagement
• Global outlook with partnerships in Malaysia, India, Pakistan and
a new Ghana campus
• Alma implemented January 2013 as an early adopter.
• I am Systems and Analytics Manager, at LUL since 2002 to
implement Aleph – systems background, not library
• How can library analytics help?
3. Alma Analytics reporting and
dashboards
• Following implementation of Alma, analytics dashboards
rapidly developed for common reporting tasks
• Ongoing work in this area, refining existing and developing
new reports
6. Projects & Challenges
• LDIV – Library Data, Information & Visualisation
• ETL experiments done using PostgresQL and Python
• Data from Aleph, Alma, Ezproxy, etc.
• Smaller projects:
• e.g. Re-shelving performance – required to use Alma Analytics
returns data along with the number of trolleys re-shelved daily.
• Challenges – Infrastructure, Skills, time
• Lots of new skills/knowledge needed for Analytics. For us :
Alma analytics (OBIEE), python, Django, postgres, Tableau, nginx,
openresty, lua, json, xml, xsl, statistics, data preparation, ETL, etc, etc,
etc
7. Alma analytics data extraction
• Requires using a SOAP API (thankfully a RESTful API is now
available for Analytics)
• SOAP support for python not very good, much better with
REST. Currently using the suds python library with a few bug
fixes for compression, ‘&’ encoding, etc.
• A script get_analytics invokes the required report,
manages collection of multiple ‘gets’ if the data is large and
produces a single XML file result.
• Needs porting from SOAP to REST.
• Data extraction from Alma Analytics is straight forward,
especially with REST
8. Data from other places
• Ezproxy logs
• Enquiry/exit desk query statistics
• Re-shelving performance data
• Shibboleth logs, hopefully soon. We are dependent on central IT
services
• Library building usage counts
• Library PC usage statistics
• JUSP & USTAT aggregate usage data
• University faculty and department data
• Social networking
• New Alma Analytics subject areas, especially uResolver data
9. Gaps in the electronic resource
picture
• Currently we have aggregate data from JUSP, USTAT
• Partial off campus picture from ezproxy, but web orientated
rather than resource
• Really want the data from Shibboleth and uResolver
• Why the demand for such low level data about individuals?
10. The library and learner analytics
• Learner analytics a growth field
• Driven by a mass of data from VLEs and MOOCs …. and
libraries
• Student satisfaction & retention
• Intervention(?)
• if
low(library borrowing) & low(eresource access) &
high(rate of near late or late submissions) &
low_to_middling(grades)
then
do_something()
• The library can’t do all that, but the university could/can
• Library can provide data
11. The library as data provider
• LAMP – Library Analytics & Metrics
Project from JISC
• http://jisclamp.mimas.ac.uk
• We will be exporting loan and anonymised
student data for use by LAMP.
• They are experimenting with dashboards
and applications
• Prototype application later this year.
• Overlap with our own project LDIV
• The Library API
• For use by analytics projects within the university
• Planning office, Student Services and others
12. The Library API
• Built using openresty, nginx, lua
• Restful like API interface
• e.g. Retrieve physical loans for a patron
• GET http://lib-ldiv.lancs.ac.uk:8080/ploans/0010215?start=45&number=1&format=xml (or json)
<?xml version="1.0" encoding="UTF-8"?>
<response>
<record>
<call_no>AZKF.S75 (H)</call_no>
<loan_date>2014-07-10 15:44:00</loan_date>
<num_renewals>0</num_renewals>
<bor_status>03</bor_status>
<rowid>3212</rowid>
<returned_date>2014-08-15 10:16:00</returned_date>
<collection>MAIN</collection>
<rownum>1</rownum>
<material>BOOK</material>
<patron>b3ea5253dd4877c94fa9fac9</patron>
<item_status>01</item_status>
<call_no_2>B Floor Red Zone</call_no_2>
<bor_type>34</bor_type>
<key>000473908000010-200208151016173</key>
<due_date>2015-06-19 19:00:00</due_date>
</record>
</response>
[{
"rownum": 1,
"key": "000473908000010-200208151016173",
"patron": "b3ea5253dd4877c94fa9fac9",
"loan_date": "2014-07-10 15:44:00",
"due_date": "2015-06-19 19:00:00",
"returned_date": "2014-08-15 10:16:00",
"item_status": "01",
"num_renewals": 0,
"material": "BOOK",
"bor_status": "03",
"bor_type": "34",
"call_no": "AZKF.S75 (H)",
"call_no_2": "B Floor Red Zone",
"collection": "MAIN",
"rowid": 3212
}]
13. How does it work?
• GET http://lib-ldiv.lancs.ac.uk:8080/ploans/0010215?start=45&number=1&format=xml
• Nginx configuration maps REST url to database query
location ~ /ploans/(?<patron>w+) {
## collect and/or set default parameters
rewrite ^ /ploans_paged/$patron:$start:$nrows.$fmt;
}
location ~ /ploans_paged/(?<patron>w+):(?<start>d+):(?<nrows>d+).json {
postgres_pass database;
rds_json on;
postgres_query HEAD GET "
select * from ploans where patron = $patron
and row >= $start and row < $start + $nrows";
}
14. Proxy for making Alma Analytics
API requests
• e.g. Analytics report which produces
• nginx configuration
location /aa/patron_count {
set $b "api-na.hosted.exlibri … lytics/reports";
set $p "path=%2Fshared%2FLancas … tron_count";
set $k "apikey=l7xx6c0b1f6188514e388cb361dea3795e73";
proxy_pass https://$b?$p&$k;
}
• So users of our API can get data
directly from Alma Analytics and
we manage the interface they use
and shield them from any API
changes at Ex Libris.
15. Re-thinking approaches
• Requirements workshops
• Application development
• Data provider via API interfaces
• RDF/SPARQL capability
• LDIV – Library Data, Information and Visualisation
• Still experimenting
• Imported data from ezproxy logs, GeoIP databases, student
data, primo logs, a small amount of Alma data
• Really need Shibboleth and uResolver data
• Tableau as the dashboard to these data sets
