Dynamic Data Processing For A Semantic Data Storage Architecture

What is this patent about?

’007 is related to the field of data management, specifically addressing the challenge of integrating diverse data sources into a unified semantic data store. Traditional systems often struggle with data silos and incompatible schemas, requiring manual and error-prone integration efforts. The invention aims to automate and streamline this process, enabling more efficient data storage, retrieval, and analysis.

The underlying idea behind ’007 is to create a system that automatically ingests, transforms, and indexes dynamic data from various sources into a semantic data store . This involves receiving data as events, storing it initially in a raw format, then automatically determining associated entities, updating a semantic index, transforming the data into a normalized schema, and finally storing the curated data based on its semantic index. This automated process reduces manual effort and improves data consistency.

The claims of ’007 focus on a method, system, and computer-readable medium for storing dynamic data. The core of the claims covers receiving data from an external source via an event service, storing it in a raw data portion, updating a semantic index , transforming the data's original schema into a normalized data schema , and storing the transformed data in a curated portion of the data store, using the semantic index for organization.

In practice, the system operates by subscribing to an event service that receives data from various external sources. When a new data event occurs, the system stores the raw data and then uses a dynamic data manager to identify entities, transform the data schema, and update the semantic index. This allows for efficient retrieval of relevant data based on context and search terms, as the data is organized and indexed according to its semantic meaning.

This approach differentiates itself from prior solutions by automating the data curation process and using a semantic data store with a normalized schema. Instead of relying on manual integration and point-to-point mappings, the system automatically transforms and indexes data, making it easier to query and analyze. The use of a semantic index allows for more intelligent data retrieval, as the system can identify relevant data based on its meaning and relationships to other entities.