Clustered Information Processing and Searching with Structured-Unstructured Database Bridge

Abstract

Systems and methods for indexing information and for performing searches are disclosed. In these systems and methods information is “ingested” into the system by clustering the information using a clustering algorithm such as k-means or k-medoids clustering. During the clustering process, a hybrid distance measurement is used that allows the systems and methods to determine similarity across a number of different types of information. Once the information is clustered, it is stored and “mirrored” both in a structured (e.g., relational) data repository and in an unstructured data repository. Methods according to the invention allow the retrieval of both direct search results and search results including related concepts. After clustered information is stored, future searches can be performed by searching the stored results in whichever data repository is most appropriate for the context.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application is a continuation of U.S. patent application Ser. No. 13 / 857,816, filed Apr. 5, 2013, which claims priority to U.S. Provisional Patent Application No. 61 / 736,464, filed Dec. 12, 2012, and to U.S. Provisional Patent Application No. 61 / 621,970, filed Apr. 9, 2012. The contents of all of those applications are incorporated by reference herein in their entireties.BACKGROUND OF THE INVENTION[0002]1. Field of the Invention[0003]The invention relates to computer-based searching, information retrieval, indexing, and storage.[0004]2. Description of Related Art[0005]Decades ago, large amounts of data were stored in a variety of different formats, depending on the application programs that were intended to access the data, the data types, and the preferences of the programmers who created the programs. In 1970, E. F. Codd, working at the IBM Research Laboratory, wrote a seminal paper, “A Relational Model of Data for Large Shared Dat...

AI Technical Summary

Benefits of technology

Enables efficient organization and retrieval of information by clustering search results, including both literal and related terms and concepts, across various data repositories, enhancing data understanding and accessibility.

Problems solved by technology

However, their feature sets may not be as robust or as standardized as SQL-based relational databases.

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