Big Data Analytics (bibliographical data)

We try to analyse bibliographical data using big data technology (flink, elasticsearch, metafacture).

Here a first sketch of what we're aiming at:

We use bibliographical metadata:

Swissbib bibliographical data https://www.swissbib.ch/

Catalog of all the Swiss University Libraries, the Swiss National Library, etc.
960 Libraries / 23 repositories (Bibliotheksverbunde)
ca. 30 Mio records
MARC21 XML Format
→ raw data stored in Mongo DB
→ transformed and clustered data stored in CBS (central library system)

edoc http://edoc.unibas.ch/

Institutional Repository der Universität Basel (Dokumentenserver, Open Access Publications)
ca. 50'000 records
JSON File

crossref https://www.crossref.org/

Digital Object Identifier (DOI) Registration Agency
ca. 90 Mio records (we only use 30 Mio)
JSON scraped from API

Swissbib

Librarian:

- For prioritizing which of our holdings should be digitized most urgently, I want to know which of our holdings are nowhere else to be found.

- We would like to have a list of all the DVDs in swissbib.

- What is special about the holdings of some library/institution? Profile?

Data analyst:

- I want to get to know better my data. And be faster.

→ e.g. I want to know which records don‘t have any entry for ‚year of publication‘. I want to analyze, if these records should be sent through the merging process of CBS. Therefore I also want to know, if these records contain other ‚relevant‘ fields, defined by CBS (e.g. ISBN, etc.). To analyze the results, a visualization tool might be useful.