Federated Search: Many Sources, One Query

Today, digital information is hard to find and manage, and comes from multiple sources (databases, files or content management systems, metadata, or other external sources). For instance, content such as HR policies, forms, or HR business tools might be housed away in an enterprise content management or file system. Information about an applications user-interface likely will be defined in metadata. Much more content will be contained in relational databases. Traditionally, each source has native search functionality — letting people enter a text query to retrieve information stored in their respective dataset. Federated search attempts to minimize this dysfunction by increasing the intelligence of a single search query – effectively eliminating the problem of searching differing data sources. It also has capabilities — like clustering (categorizing or gathering results into groups around a certain theme), and analyzing text for semantics, and patterns — that are unavailable in more traditional search applications. In a federated search, one query is distributed to many data sources or data types. The system hides the complexity of searches (data source, security, query format, data format, data location etc.) from the user. It also minimizes the required user input. Federated search moves beyond simply providing access to information. Now, access to the information has to be fast, easy, have minimal administrative overhead, produce relevant or accurate results, and be facilitated by a minimalist user interface.

Search tools that can filter mass quantities of data while connect people to the differing, yet relevant information – quickly, easily, and accurately – are highly valuable. At the same time, companies are under pressure to manage and organize data interactions and records in a timely manner — making technologies like Federated search a hot commodity.