Federated search is a technique that allows a user to make a single query which in turn acquires aggregated search results from multiple sources of information into a single interface. In other words, the same query is executed against multiple databases simultaneously, and the federated search results to these queries are presented together. An example of federated search in action can be seen at Kayak, the travel site that searches for flight and hotel availability across thousands of different websites and returns results that allow the searcher to comparison shop for the best price and availability.
At a high level, the benefits of federated search are clear. In the case of searching for flights there are a limited number of choices between two destinations, but it’s still helpful to compare departure times and prices. But for booking a hotel, a federated search is dramatically faster and enormously more convenient - most destinations have hundreds if not thousands of hotels and motels to choose from.
There are federated solutions for search in many domains in addition to travel. In law, PACER simultaneously searches the dockets of all the United States federal courts. In e-commerce, Amazon federates search queries across merchandise they sell directly and the inventories of their merchant partners. And many large websites deploy enterprise federated search so that visitors can do a single search and find results about people, products, investment information, partnerships, marketing brochures, events, news, and anything else they might be interested in that can be found on the company’s website. ResoluteAI is a federated search engine that focuses on providing scientific information for research, technology landscaping, and analysis.
Specific advantages of federated search include:
- Data consolidation and improved experience
Users often search for information from several data sources, including internal data repositories, websites, intranets, and network drives. With a federated search, these users can browse for this data simultaneously from a consistent, unified interface.
This type of technology is instrumental in opening up siloed information from different organization departments. For instance, a human resources officer can search for disparate information on employee performance across various departments from their familiar HR portal, or a scientist can search for information in an Electronic Lab Notebook (ELN) and a Laboratory Information System at the same time.
With data consolidation, you no longer have to manage multiple platforms or sites. The work is done for you.
- Security and reliability
Federated search can find and acquire information that may be hidden behind gated sources. This is possible by having the search query also send user credentials, which allow the user to see information that wasn’t formerly accessible from a typical web search.
Universities and institutions of higher learning are excellent federated search examples. They can deploy a federated search tool to provide students with secure access to subscription-based academic journals. Students can view the results from disparate sources as a combined list without logging in each time.
Also, if a user isn’t permitted to access information from a particular source, the results won’t be shown to them. Therefore, two users can access the same interface and still have different results based on their access permissions.
As you can see, features like security and reliability are central to this technology
- Improved data visibility
Federated search tools give the searcher the ability to weigh sources according to their relevance to the information they’re seeking. Consequently, they can adjust the results to provide searchable information that meets their particular needs.
Consider a researcher investigating a new medical device or procedure. Their search might include articles that appear in PubMed, which largely contains peer reviewed articles. But a federated search might also return results from a preprint server, which may be relevant but not yet as authoritative as articles from PubMed.
By improving data visibility, a company can request and gain access to a wealth of resources that can transform your service, product, and solutions.
Why Is Artificial Intelligence Becoming a Key Component to Federated Search?
AI-powered search (also known as intelligent search or cognitive search) can provide better, more comprehensive search results and can more effectively answer questions that are specific to a user’s needs.
Artificial intelligence provides the tools that offer the ability to:
- Classify and organize content into relevant categories.
Named Entity Extraction and Natural Language Processing (NLP) techniques can classify and tag text unstructured data into relevant, predefined categories such as products, organization, names of people, or even concepts.
- Apply machine learning.
Machine learning can provide seamless and immediate query suggestions such as auto-complete and semantic expansion that improve search queries’ precision and relevance over time, thereby predicting what information will provide value to users.
- Understand document structure.
Machine learning can be trained to grasp the visual structure of documents within an organization’s domain space. This can help identify document elements such as headers, tables, charts, and footers. These ML-driven capabilities can accurately recognize documents such as invoices, contracts, and purchase orders and parse information contained therein
- Understand human language.
Natural language processing enables intelligent search applications to interpret and query digital content from several data sources. Business data is often documented in domain-specific terminology. Contextual understanding and semantic search methods allow AI-powered search to break down synonyms, linguistic nuances, and relations inside complex documents.
- Ask a question.
Natural language understanding (NLU) can allow users to ask the enterprise search engine questions in natural language, much like SIRI or Alexa, so that the user doesn’t need to touch a keyboard.
How can federated search help commercial science enterprises?
Companies whose products are often the result of scientific experimentation generate an enormous amount of structured and unstructured content. This data and information is produced constantly and is frequently searched upon. Combined with external research sources such as academic publications, clinical trials, patents, regulatory and compliance data, and more, the ability for users to search these numerous sources simultaneously saves time and money, and yields better search results.
Federated search software, therefore, assists commercial science enterprises in the following ways:
- Research on research
With federated search, knowledge workers in the sciences can conduct more thorough research and combine search results from datasets in the public domain with results from datasets that reside behind the company’s firewall. These searches can include results from content that is either structured or unstructured and give the searcher a more comprehensive view of the information available on a given subject.
- Ready, relevant information
Federated search is a key component of a knowledge management strategy, making information that was previously unfindable more accessible to the employees who need it. Machine learning can reduce the need to manually classify and tag information, increasing the return on investment for a federated or enterprise search solution.
- Decision promoting insights
Unstructured text data contains many hidden insights. If natural language processing techniques are incorporated in intelligent search applications, then correlations, connections, and meaning across many data sources (like lab notes, academic research, customer feedback etc.) can reveal real-time insights effectively and efficiently.
ResoluteAI offers a federated search engine for commercial science organizations. In the words of one customer, “The benefit of ResoluteAI compared to other tools that we have is that it searches simultaneously across multiple data sources. It's AI-enabled and it includes simple but useful visual analytics that allow you to drill down into the data... It hits that sweet spot.”
Customers span healthcare and life sciences to chemicals, material science, and consumer products. We offer a combined solution that incorporates federated search for publicly available databases such as clinical trials, academic research, and patents, as well as a search capability for internal data that can exist in any format - documents, video, audio, webinars, presentations, etc.
- Nebula Enterprise Search is a federated searching solution for internal knowledge and information. We add value to a company’s institutional scientific knowledge through consistent tagging and classification as well as image recognition, document cracking, transcription, and optical character recognition. Nebula users can, for example, find a graph in a PowerPoint slide by searching for the terms in the label the Y axis.
- Foundation Scientific Discovery Engine empowers scientific research and analysis by making information from disparate sources available through a powerful search interface. We use machine learning to create consistent, structured metadata for over a dozen databases composed of unstructured information. This results in a robust search experience that highlights meaningful connections across databases that add value to users’ research
ResoluteAI’s Foundation Scientific Discovery engine is regularly adding new data sources and continues to upgrade its tagging capabilities with new taxonomies and controlled vocabularies.
Not all federated search is the same
ResoluteAI focuses on intelligence search to assist commercial science organizations in using data to help make their next big discovery. We specialize in science - our data, machine learning, analytics, and data connection capabilities are focused on scientific research and development, from ideation to post market surveillance to pharmacovigilance.