Federated search is a method of retrieving information from many different sources using a single search query. It allows users to access content stored across separate databases or applications without needing to search each one individually. A hospital might search clinical notes and diagnostic reports, while a bank could query customer records and compliance logs simultaneously. 

It belongs to the broader field of enterprise search, which helps organizations locate information across large and complex systems. Enterprise search typically involves indexing content to make it easier to find later. Federated search extends this by enabling searches across multiple systems, whether by querying them live or drawing from a central index built from distributed sources.

As businesses adopt AI to enhance discovery, federated search plays a growing role in unlocking productivity through techniques like retrieval-augmented generation (RAG). 

How does federated search work?

Federated search follows a staged process that allows a single query to retrieve and return information from many different sources. The way data is accessed and returned depends on whether the system uses search-time merging, index-time merging, or hybrid federated search.

Here is a broad overview of how it works:

Preparing and submitting the query

A user begins by entering a search term into the federated search interface. The system checks the format of the query in a process called query transformation, often utilizing natural language processing (NLP) so that it matches the requirements of each target system. In search-time and hybrid models, this allows the query to be sent directly to external systems. For index-time systems, the query is used to search a central index that was built in advance using extracted data from each source.

Selecting sources and running searches

Next, the system determines which sources or indexes to include. Search-time models send the query to each live source, where independent searches are run. Index-time models retrieve results from a pre-processed internal index. Hybrid systems may do both, depending on how the data is structured and how current it needs to be.

Aggregating and processing results

Results from all sources are collected and converted into a consistent format through normalization, which relies on an effective orchestration layer. The system also performs deduplication, where repeated results across sources are removed. Filtering excludes content that the user is not authorized to view. Search-time and hybrid models may have each live source assign its own relevance scores, while index-time models rely on internal scoring methods defined during indexing and applied during search.

Refining and delivering the output

The system prepares the final results for display. Index-time models rank based on scores from the central index. In search-time and hybrid models, scores from different sources may need to be adjusted to ensure results are ranked fairly. The output is shown in a single interface, with access controls to restrict visibility based on user permissions.

Types of federated search 

Federated search systems use different methods to respond to queries. The choice of approach depends on whether companies want to prioritize performance, relevance, or freshness.

Here is an overview of the three different types of federated search:

Search-time merging

Search-time merging retrieves results directly from each source after a query is submitted. Each source performs its own search and returns a ranked list, which the federated system then combines in real time. 

As data remains in its original system, search-time merging is well suited to environments where storing copies is not possible or desirable. However, results can be slower to load and may be harder to compare or rank consistently across sources.

Index-time merging

Index-time merging gathers data from all connected sources and stores it in a central index before any search takes place. When a user submits a query, results are returned quickly from this unified index. 

Although faster, the approach requires regular synchronization to ensure the index reflects current data and does not miss recent updates.

Hybrid federated search blends both methods by indexing some sources in advance, while querying others live at search time.

Organizations use this model to balance speed and freshness, especially when working with systems that have different technical constraints or update schedules.

Federated search use cases

Federated search enables professionals in various sectors to find information quickly and securely, even when it is spread across disconnected tools.

Here are some examples of how federated search supports real-world business tasks:

Clinical record access

In healthcare, patient data can be fragmented across different systems, such as electronic health records and diagnostic tools. Federated search, integrated with Privacy-Enhancing Technologies (PETs), enables cross-system searches for overviews without merging data. Healthcare providers gain a clearer overview of patient history, so they can coordinate across services to provide consistent care. 

Financial data retrieval

Regulatory requirements in finance demand fast and traceable access to customer data and transaction records. Such information is often siloed across systems such as encrypted archives and cloud-based tools. When workers use federated search, they unlock visibility across all relevant sources without the need to migrate or duplicate content. They can meet time-sensitive requests and investigation deadlines with ease.

Product data in the retail industry is often distributed across separate platforms for purposes including inventory tracking and customer support. With federated search, retailers can query systems together and return a unified view of product details regardless of where the information is stored. They gain faster access to data so they can perform tasks such as answering queries or updating listings.

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