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What is Federated Search?

Federated search is a business tool that enables a user to search multiple data sources using a single query and search interface. Federated search allows the user to search different data sources and content at the same time using a single search query. This allows improved engagement and experience for the customers and makes it easier for a business to manage its data. Federated search is a one-stop-shop when it comes to data search.

Federated Search Diagram

In any business or company, the database, product catalogs, and other kinds of information grow every day. With this constant increase, ensuring that both internal users and customers can find relevant and timely information is a challenging task. This is where federated search becomes an essential part of operations.

Once a query is made in a federated search, the result is gathered from multiple search engines and all results are presented to the user in a single interface. The user interface becomes a centralized point that connects search engines and information sources functioning in silos.

People encounter examples of federated search in day-to-day functions without even realizing it. For example, if a query is put into Windows Search or MacOS Spotlight, the search engine pools information related to that query from multiple sources including applications, web pages, documents, and contacts. This is a type of federated search.

Federated search allows external customers and the internal business team to locate relevant search results quickly and easily by enabling them to run a search from a single search bar. Users then get centralized search results from sources across the entire system, making data and content more accessible and useful. It provides a better experience for customers as it allows them to find top results, as well as products with lesser page views and clicks, improving the conversion rate and allowing more customers to engage with the business.

In the case of businesses, federated search allows employees to find information that they require with less effort than traditional search methods, which are often limited or unproductive. This increases efficiency and boosts the discoverability, which is why federated search is very useful for organizations that have multiple information and data sources both in the cloud and on-site.

There are different types of businesses where federated search solutions can prove to be very helpful. Ecommerce is one such sector where it can allow users or customers to quickly search through hundreds and thousands of products, reviews, guides, and other information. This ability directly helps to create a high rate of conversion and increased sales.

Federated search can also be very useful at the enterprise level. Corporations may have separate websites or sections of websites for customers, investor relations, recruitment, social responsibility, and brand awareness. If a user reaches the wrong website, they will not be able to find what they are looking for and leave disappointed, leading to business losses. Federated search allows the user to search through all the websites of the corporation and redirects the user to the website that hosts relevant answers for their search query.

Federated search helps users search through all available content, situated anywhere, at the same time, directing them to the appropriate required information. This provides a better experience for the user and increases brand engagement.

Different Types of Federated Search

There are two types of federated search.

Search time merging and index time merging are different, both conceptually and technically, with their own advantages and disadvantages.

Search Time Merging

Search time merging is also known as query time merging. In this type of search, a separate search engine is used by every individual data source. The query received by the federator is sent out to all the search engines. Once the results from all search engines are gathered, the federator then aggregates them and presents the single list to the user.

One advantage of using this type of search federation is that it is simpler to implement. There is no requirement for data standardization while using a search time merging system. It searches each index separately and can handle different formats of data. Also, it does not require building a unified index for all data, as the federator uses the existing search index systems of every individual data source.

The biggest drawback of search time merging from a user’s perspective is that the response time of this type of federator is slower since the final results cannot be presented until every individual search engine responds. This means that the federator is as slow as the slowest search engine. As a result, it may not meet the real-time expectations of users. Another challenge is ranking the combined results because every search engine scores relevancy differently.

A solution for this problem is that each search engine presents its result separately in different tabs. Another solution is to sort the data using agreed-upon categories like date, price, or location.

While search time merging does not require a unified index, an organization must still maintain search tools for every individual data source. If any data doesn’t have its own search tool, it won’t show up in the federated search results.

Index Time Merging

In index time merging, there are no separate search tools for every data source. Instead, there is one unified index for all searchable data. This requires creating a large central data index, so the initial investment time is higher. The system becomes efficient only after all content has been acquired by the central index. However, once this is complete, the search becomes faster and more relevant. It also allows the users to tap into all the data sources, even if they do not have their local search tools.

The biggest advantage of the index time merging system is that the results are much faster, as it does not have to depend on individual search engines to respond. It also allows for the possibility of using content or data that does not have its own search engine, spreading the search wider. It provides the user with a better experience as the centralized index allows the use of relevancy algorithms and sophisticated query enhancement. As a result, the search results are ranked based on relevancy. This system also comes with features like auto-complete, filtering, and more.

The biggest drawback of index time merging is the initial time and effort required to create the centralized index. This means a longer time for implementation. Also, maintaining the centralized index can become a complicated procedure over time as the data needs to be reread every time there is any change.

The best solution for any business depends on its data environment and users’ needs. However, any of the two types of federated search solutions can help users find results in a faster and easier way.

Phases of Federated Search

The federated search functions in six phases.

1. Query Transformation and Broadcasting

The first phase of a federated search function is query transformation. The query of the user is transformed into the correct syntax and then broadcast on all search engines. Here, the query is not associated with any particular text as it will require searching the entire database. An efficient discovery process is used to select the region of interest in the entire database system.

2. Resource Representation

The next phase is the representation of search engine resources. Different methods are used for this, such as extraction of search terms from the query interface of the search engine, generation of a summary of the content on pages listed by a search engine, and query-based sampling to find relevant resource descriptions.

3. Resource Ranking

After the discovery of the resources, the next phase is ranking them in order of precision and relevance. Here, different resources can have similar or duplicate results. In this phase, the search result is collectively optimized for precision.

4. Distributed Search

Based on the query, the quality of the output of search engines is compared and the best search engines suited for the query are selected. The query search is then performed, and relevant data is extracted.

5. Merging

The next phase is merging the results of different search engines to produce the best results. Search time merging and index time merging systems are used here.

6. Presentation

The final phase is combining all the relevant search results and presenting them to the user that raised the query via a unified interface. The results are ranked according to the metrics that describe the relevance of output like use base, context, industry, and time, and the final list is presented to the user.

What Are the Benefits of Federated Search?

Implementing federated search on an organization’s website can prove to be helpful for any business. Some key benefits include:

Improved Customer Experience

Federated search allows users to find exactly what they are looking for on a business website by typing in a query. They can search a vast pool of data and find what they are looking for with far fewer clicks, saving a lot of time and energy. Even users who are new to federated search can use one keyword or phrase as a query and still discover a wide range of content matching their queries. The fewer clicks and pageviews required to find a product or a service, the better the customer experience. This results in a greater customer happiness quotient and higher chances of conversion.

Manageable Website Expansion

Adding new content and data to the website means a more in-depth website and a much bigger content repository. The new data can be easily integrated with the federated search tool, which means organizations won’t need to set up a search tool for every new piece of content, hence making website expansion manageable.

Better Browsability

A centralized federated search solution allows the business to add, restructure, or modify data while still keeping it searchable. In federated search, every part of the user interface can be adapted to showcase the content, which in turn helps users navigate and interpret different categories of information easily. This enhances the browsing experience and increases user engagement.

Improved Security and Maintenance

Federated search means the business only needs to manage one search engine. This increases the reliability and safety of the system as monitoring, securing, maintaining, and troubleshooting problems for one search engine is much easier than handling multiple search tools for different types of data.

Increased Relevance of Results

Federated search gives businesses the option to optimize the relevance of each type of search content. This can be done by considering different parameters to rank content. With a bigger database to draw information from, search results become more relevant and accurate. Once the business can understand each user’s search patterns, it becomes easier to provide more suitable and helpful content, which greatly enhances the search experience.

Challenges of Federate Search

Federated search technology aims to solve two major problems—understanding the search query in the right context, and classifying data based on relevance. However, there are certain challenges posed when solving these problems. Some of these challenges include:

Search Language

At times, search queries are not self-explanatory. The queries users make might not be easy to understand for the federated search system. To understand obscure or incorrect searches, the system would need to consider the nuances of language, which might not be part of its functionality. This means that the results can be confusing or irrelevant.

Data Structure

There can be different formats of data showing up as the search result. In such a situation comparing the data content for relevance can become challenging. For instance, selecting whether a text result is better than a video result can be difficult for the system.

Selection of Scoring Metrics

Federated search systems rank the search content based on the selected scoring metric. In such situations, different metrics selection can lead to different result rankings, which might not be suitable for the user.

Query Features

While some search engines allow the use of special characters like question marks, hyphens, and quotation marks to better describe the intention of a search query, not all search engines support these features. The lack of a standardized and unified system, especially in the case of the search time merging system, can lead to a reduction in the effectiveness of the search process.


Users expect results within seconds of putting in the query. If a search engine is taking too long to come up with the result, it may be left out even if the content is more important than the other results.

The Need for Artificial Intelligence (AI) in Federated Search

Search powered by AI is also known as cognitive search or intelligent search. It can provide comprehensive and better results and can answer questions more effectively, depending on the needs of the users.

AI integration in federated search provides the following abilities to the system:

Better Classification of Content

Natural language processing (NLP) and named entity extraction (NEE) techniques can help in classifying and tagging unstructured data into predefined, relevant categories like organization, products, names of people, and concepts.

Machine learning (ML) application

AI can help in providing immediate and seamless query suggestions like semantic expansion and auto complete. This functionality improves the search query relevance and precision over time, predicting what kind of information will be valuable for the users.

Understanding Document Structure

Businesses can train ML to grasp the visual structure of any document within its domain space. This can help identify elements within documents like tables, headers, footers, and charts. ML capabilities can help accurately recognize documents like contracts, invoices, and purchase orders, while simultaneously analyzing the information contained in them.

Understanding Human Language

NLP through AI allows search applications to interpret and raise queries from digital content and data sources. Usually, business data is documented in a specific terminology. NLP provides the federated search system with a contextual understanding of the query put in by people and breaks down things like linguistic nuances and synonyms to provide better search results.

Asking Questions

Natural language understanding (NLU) through AI allows users to raise queries in the natural language, similar to Alexa or SIRI, so the user does not need to use the keyboard.

Federated Search Makes Finding What You Need Easy

With the ability to search every relevant article, page, and item across a range of sources, federated search turns searching into finding. Customers and employees can access what they need with no wasted time or incorrect information. For large enterprises or growing organizations, this search function is a basic addition to operations that will save countless hours and improve customer satisfaction.

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