Avi Rappoport on Federated vs. Aggregated Search Architectures
The Federated Search Blog brings an important set of observations about search to our attention. Search pundit Avi Rappoport makes it clear that it’s not so clear that discovery services are the holy grail of search nor the answer to everybody’s search problem. Search is complicated. As Sol Lederman writes in the Federated Search Blog article, “One size does NOT fit all.”
In May, search consultant Avi Rappoport delivered a presentation at the Enterprise Search Summit: Federated vs. Aggregated Search Architectures.
Avi Rappoport is an enterprise search consultant, helping companies improve search engine functionality for websites and intranets. She has a degree from UC Berkeley’s (then) School of Library and Information Science and spent 10 years in software development before becoming a search consultant. She is the editor of SearchTools.com and a frequent speaker and author, providing a strong focus on search usability in the broadest sense and sharing her conviction that search engines can always be better.
Avi created a web page with a summary of and links to a couple of versions of her presentation. I greatly appreciate Avi’s consideration of the pluses and minuses of federation aggregation (i.e. discovery service) in a world that is often polarized about one approach being better in all cases.
My research for this presentation indicated that each is useful in specific circumstances (I know, no surprise there). Many data sources are obviously best accessed by one or the other, but it’s the corner cases that are tricky. Aspects to consider include:
- size of the content in the source
- how often your users need that content
- content change rate
- importance of real-time access control permissions changes
- content licensing rules
- available tools for indexing / querying
- difficulty of extracting and indexing
- quality of the internal search engine
- difficulty of sending queries and receiving results
The final slide has some sage advice:
Be open-minded, analyze the benefits of each approach for each data source.
One size does NOT fit all.
