The DSpace infrastructure for logging page-views and downloads has been limited to aggregations on communities, collections and items. While this already provides a wealth of aggregated information that is impossible to retrieve using Google Analytics, it still does not assist a repository manager in addressing questions such as: “How many downloads did Professor X get through Google Scholar last month?” Because authors are represented as metadata on items, tackling this challenge effectively means opening the potential to aggregate pageview and download statistics on any metadata field in the repository. By the time of the conference, functionality that addresses this need will be available as part of @mire’s Content and Usage analysis module. The metadata based usage statistics were realized in co­-development with the World Bank. Presentation created by Lieven Droogmans, Art Lowel and Ignace Deroost. Presented at Open Repositories 2015 by Ignace Deroost.

1. www.atmire.com
Metadata based
usage statistics

2. OVERVIEW
1. Why DSpace statistics?
2. Usage event vs. Item metadata
3. Generating metadata based statistics
4. Linking metadata to usage events
5. Performance
6. Problem solved?

3. Statistics solution that knows DSpace:
Structure
“Which are the most downloaded bitstreams in a collection”
Metadata
“Who are the most popular authors in terms of downloads?”
1 - WHY DSPACE STATISTICS?

4. USAGE EVENT VS. ITEM METADATA
2 types of metadata:
Usage event metadata
Additional information about the usage event
Item metadata
Additional information about the target of the usage event

5. USAGE EVENT METADATA
Additional information about the usage event
Not related to repository
Also possible with other statistics solutions:
• IP address
• Country
• User Agent
• HTTP Referrer
• ...

6. ITEM METADATA
Relate usage event to information stored in
your repository.
Allows statistics queries based on item
metadata.
→ Not possible with a statistics solution that
is not tied to the repository.

7. GENERATING METADATA BASED STATISTICS
How many downloads did
author "Barnes, Douglas F.”
get in the last year, grouped
by month

13. LINKING METADATA TO USAGE EVENTS
Solr Query
http://localhost:8080/solr/statistics/select?
facet=true&facet.offset=0&facet.mincount=1&facet.sort=
false&q=*:*&facet.limit=24&facet.field=dateYearMonth&f
acet.method=enum&fq=bundleName:ORIGINAL&fq=type:
+0&fq=statistics_type:view&fq=-isBot:true&fq=-
isInternal:true&fq=time:[2014-07-01T00:00:00.000Z+TO
+2015-06-06T00:00:00.000Z]&fq=+(author_mtdt:Barnes,
+Douglas+F.)+&wt=javabin&rows=0

14. LINKING METADATA TO USAGE EVENTS
facet.field=dateYearMonth
group by the field dateYearMonth
fq=type:+0
only include bitstream downloads
fq=bundleName:ORIGINAL
only include files in bundle “ORIGINAL”
fq=-isBot:true
filter out all bot statistics
fq=-isInternal:true
filter out all internal statistics
fq=time:[2014-07-01+TO+2015-06-06]
only include stats that are between Jul 1st 2014
and Jun 6th 2015
fq=+(author_mtdt:Barnes,+Douglas+F.)+
only include statistics that are by
author Barnes, Douglas F.

15. <response>
<lst name="responseHeader">
...
</lst>
<result name="response" numFound="164" start="0"></result>
<lst name="facet_counts">
<lst name="facet_fields">
<lst name="dateYearMonth">
<int name="2014-07">15</int>
<int name="2014-08">19</int>
<int name="2014-09">15</int>
<int name="2014-10">10</int>
<int name="2014-11">7</int>
<int name="2014-12">13</int>
<int name="2015-01">13</int>
<int name="2015-02">15</int>
<int name="2015-03">21</int>
<int name="2015-04">22</int>
<int name="2015-05">12</int>
<int name="2015-06">2</int>
</lst>
</lst>
</lst>
</response>

16. LINKING METADATA TO USAGE EVENTS
In a vanilla DSpace installation:
• Usage statistics only contain bitstream IDs: no
metadata
• The metadata is stored in the database

17. PROPOSED SOLUTION
1. Query the database for bitstream IDs
based on the author metadata
2. Use those IDs to query solr for statistics

18. PROPOSED SOLUTION: DOWNSIDES
• Two queries to answer one question
• The solr query can get very long and
inefficient to execute
• Inefficient but still possible

19. PROPOSED SOLUTION: DOWNSIDES
What if we want to show the 10 authors with
the most downloads?
• query the database for all authors
• query SOLR to get the number of usage events
for each author
• sort those counts, and return the 10 highest

20. PROPOSED SOLUTION: DOWNSIDES
Very inefficient!
• do a lot of queries
• throw away most of the results: we only
need top 10

21. SOLR FACETS
To do a facet query:
• specify ”facet.field” along with the
regular query
• results will be grouped by the values they have
for that field

22. SOLR FACETS: EXAMPLE
q=type:0&facet.field=owningItem
q=type:0
search for all usage events that are bitstream downloads
facet.field=owningItem
group these by item
count the # records in each group

23. OUR SOLUTION
• Add Item metadata to SOLR.
• Use built-in filtering and grouping

24. CHALLENGE: SIZE OF THE SOLR CORE
That solution creates new challenges
Metadata is duplicated in every statistical record
that takes up a lot of space
and it needs to be kept in sync

25. SIZE OF SINGLE USAGE EVENT
<doc>
<str name="ip">177.21.194.80</str>
<arr name="ip_search"><str>177.21.194.80</str></arr>
<arr name="ip_ngram"><str>177.21.194.80</str></arr>
<int name="type">0</int>
<int name="id">54</int>
<date name="time">2015-05-11T04:33:49.077Z</date>
<str name="dateYearMonth">2015-05</str>
<str name="dateYear">2015</str>
<str name="continent">SA</str>
<str name="countryCode">BR</str>
<float name="latitude">-10.0</float>
<float name="longitude">-55.0</float>
<arr name="bundleName"><str>ORIGINAL</str></arr>
<arr name="containerBitstream"><int>54</int></arr>
<arr name="owningItem"><int>1652</int></arr>
<arr name="containerItem"><int>1652</int></arr>
<arr name="owningColl"><int>14</int></arr>
<arr name="containerCollection"><int>14</int></arr>
<arr name="owningComm"><int>1</int></arr>
<arr name="containerCommunity"><int>1</int></arr>
<str name="uid">60fe8ebb-b8a9-454c-8eef-3f9f800d1399</str>
<bool name="isBot">false</bool>
<bool name="isInternal">false</bool>
<str name="statistics_type">view</str>
<long name="_version_">1501767933804675072</long>
</doc>
25 elements

26. <doc>
<str name="ip">177.21.194.80</str>
...
<arr name="author_mtdt">
<str>Khandker, Shahidur R.</str>
<str>Barnes, Douglas F.</str>
<str>Samad, Hussain A.</str>
</arr>
<arr name="subject_mtdt">
<str>ACCESS TO LIGHTING</str>
<str>ACCESS TO MODERN ENERGY</str>
<str>AGRICULTURAL LAND</str>
<str>AGRICULTURAL RESIDUE</str>
<str>AIR CONDITIONERS</str>
<str>AIR POLLUTION</str>
<str>ALTERNATIVE ENERGY</str>
<str>ALTERNATIVE SOURCES OF ENERGY</str>
<str>APPROACH</str>
<str>ATMOSPHERE</str>
<str>AVAILABILITY</str>
<str>BASIC ENERGY</str>
<str>BIOMASS</str>
<str>BIOMASS BURNING</str>
<str>BIOMASS COLLECTION</str>
<str>BIOMASS CONSUMPTION</str>
<str>BIOMASS ENERGY</str>
...
<str>WORLD ENERGY</str>
<str>WORLD ENERGY OUTLOOK</str>
</arr>
...
</doc>
SIZE OF SINGLE USAGE EVENT WITH METADATA
3 authors
140 subjects

27. KEEPING METADATA IN SYNC
When the metadata of an item changes
• a mistake was corrected
• extra info was added
the statistical records for that item need to be
updated as well

28. KEEPING METADATA IN SYNC
Item with 7,000 page visits and 5,000 downloads
→ that means updating 12,000 usage events.
• That takes time
• During that time, it takes longer to view other
statistical reports

29. PERFORMANCE
Size of single usage event
Metadata updates
Amount of events
Live search queries

30. PERFORMANCE ENHANCEMENT: SYNCING
Try to keep the load created by synching
metadata in the statistics as low as possible:
→ only sync while solr is idle
interrupt the operation when a search request
can’t be handled in time
interrupt the operation when Solr’s memory
usage nears its max

31. PERFORMANCE ENHANCEMENT: CACHING
Caching
store generated reports in a separate Solr core
retrieving them is very fast
invalidate cached reports after a set time
(e.g. 24 hours)

32. PERFORMANCE ENHANCEMENT: CACHING
Don’t delete expired cached reports
If a user requests a report that is cached
→ show the outdated version
In the mean time
→ generate a new version
Automatically show new report when it’s done

33. EXAMPLE: CACHE MISS

34. EXAMPLE: CACHE MISS

35. PROBLEM SOLVED?
Additional complexity
Number of usage events
keeps growing
Name variants
Different names for one author

36. “Who are the Most
Popular Authors in terms
of downloads?”
NAME VARIANTS USE CASE

37. https://openknowledge.worldbank.org/most-popular/author

38. Ferreira, Francisco H. G.
Ferreira, Francisco H.G.
Ferreira, Francisco
3 name variants:

40. SOLUTION FOR NAME VARIANTS
include all name variants in Solr query:
author_mtdt:
(Ferreira, Francisco H. G.) OR
(Ferreira, Francisco H.G.) OR
(Ferreira, Francisco)

41. ALTERNATIVE SOLUTION
If you have unique IDs (e.g. ORCID)
Index, and search for them instead

42. www.atmire.com
Thank you!
Questions?

43. Desktop view Phone view

44. Desktop view
Phone view

45. Desktop view
Phone view