Search-based applications use semantic technologies to aggregate, normalize and classify unstructured, semi-structured and/or structured content across multiple repositories, and employ natural language technologies for accessing the aggregated information.
Search-based applications have proven popular and effective because they provide a dynamic, scalable access infrastructure that can be integrated with other features that information workers need: task-specific, and easy to use work environments that integrate features that are usually designed to be used as separate applications, collaborative features, domain knowledge, and security.
They have been optimally engineered to facilitate access to information, not to record and store transactions.
In addition, the mathematical and statistical processors integrated to date into search engines remain relatively simple.
At present, therefore, databases still provide a more effective structure for complex analytical functions.