Federated search refers to the practice of retrieving information from multiple distributed search engines and databases — all from a single user interface. Consider it to be a one-stop shop for data search.
The user interface acts as a centralized site that connects siloed information sources and search engines. Every search query, from every user, aims to find distinct pieces of information and serve them with the highest precision of relevance.
In general, we can compare federated search to a single database system like so:
Now let’s go a bit deeper and see exactly how federated search works. While it’s an important goal for overall user experience, it is not without challenges.
A federated search system can consist of the following phases:
First, the query is transformed into the right syntax and broadcasted to all search engines. At this stage, the query does not associate to a particular text, since that will require searching into the entire database.
Combined with delays in network transmission, an efficient discovery process is adopted to select regions of interest in the database systems.
A variety of methods may be used to represent search engine resources:
Once the resources are discovered, they are ranked in order of relevance and precision. At this time, multiple resources may point to similar or duplicate text results. The goal is to collectively optimize search result precision across the best search engines.
The quality of output is compared and the best search engines are selected for the query. The query is performed and relevant search data is extracted.
Here, merging results from combining several search engines. Common types of merging are:
Combining relevant results and presenting them to the end-user through a unified interface. The results are sorted according to precision scores or other metrics that better describe relevance of the output, such as results from similar search queries, use base, location, context, industries and time.
Any federated search system, the technology aims to solve two key problems:
Now, where federated search relies on AI and machine learning, which is increasingly the case, these two key issues are even more difficult to solve. Here are some of the reasons behind these challenges.
Looking back at the two problems: understanding search queries and developing an efficient classification system. In context of the challenges described above, solving the first problem is a matter of going beyond traditional federated search practice.
The search system must incorporate advanced AI capabilities that help associate context to a search query. The search process needs to be personalized and relevant, yes, but returning the most relevant search results is not simply a matter of fixing data output based on score metrics.
A mature federated search system satisfies search results based on context, stitching the search journey using relevant information in a secure and privacy-friendly environment. It is also unified across digital channels, platforms and devices. A reactive federated search result only includes data responses to the query — a mature search system returns recommendations and personalized results to complement the expected search output.
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