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# Roadmap
Contributions would be helpful to implement:
- spam/abuse mitigation
- "work aggolomeration" interfaces, for merging related releases under the same work
- import (bulk and/or continuous updates) for more metadata sources
- better handling of work/release distinction in, eg, search results and
citation counting
- de-duplication (via merging) for all entity types
- matching improvements, eg, for references (citations), contributions
(authorship), work grouping, and file/release matching
- internationalization of the web interface (translation to multiple languages)
- accessibility review of user interface
Possible breaking API and schema changes:
- new entity type for research institutions, to track author affiliation. Use
the new (2019) ROR identifier/registry
- container nesting, or some method to handle conferences (event vs. series)
and other "series" or "group" containers
Other longer term projects could include:
- bi-directional synchronization with other user-editable catalogs, such as
Wikidata
- generic tagging of entities. Needs design/scoping; a separate service?
editor-specific? tag by slugs, free-form text, or wikidata entities?
"delicious for papers"?. Something as an alternative to traditional
hierarchal categorization.
## Known Issues
- changelog index may have gaps due to PostgreSQL sequence and transaction
roll-back behavior
## Unresolved Questions
How to handle translations of, eg, titles and author names? To be clear, not
translations of works (which are just separate releases), these are more like
aliases or "originally known as".
Should contributor/author affiliation and contact information be retained? It
could be very useful for disambiguation, but we don't want to build a huge
database for "marketing" and other spam.
Can general-purpose SQL databases like Postgres or MySQL scale well enough to
hold several tables with billions of entity revisions? Right from the start
there are hundreds of millions of works and releases, many of which having
dozens of citations, many authors, and many identifiers, and then we'll have
potentially dozens of edits for each of these. This multiplies out to `1e8 * 2e1
* 2e1 = 4e10`, or 40 billion rows in the citation table. If each row was 32
bytes on average (uncompressed, not including index size), that would be 1.3
TByte on its own, larger than common SSD disks. I do think a transactional SQL
datastore is the right answer. In my experience locking and index rebuild times
are usually the biggest scaling challenges; the largely-immutable architecture
here should mitigate locking. Hopefully few indexes would be needed in the
primary database, as user interfaces could rely on secondary read-only search
engines for more complex queries and views.
There is a tension between focus and scope creep. If a central database like
Fatcat doesn't support enough fields and metadata, then it will not be possible
to completely import other corpuses, and this becomes "yet another" partial
bibliographic database. On the other hand, accepting arbitrary data leads to
other problems: sparseness increases (we have more "partial" data), potential
for redundancy is high, humans will start editing content that might be
bulk-replaced, etc.
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