Gain flexibility, improve performance, and rise up cost-benefit!
Sure, you have lots of data but what good is data if you cannot get the information you need.
And what new information could you unlock if you could actually consolidate numerous disparate databases into one?
Relavance is bringing to market a technology that attains the ultimate goal in data management. It is the only truly associative atomic database model where each piece of information is atomic in nature and can be associated with any other piece of information.
Automation of Data Management
Currently, the process of managing data is entirely human-work intensive. From database and data warehouse designs to ETL efforts to populate them to complex inner and outer joins embedded in difficult SQL queries to endless map-reduce specifications and operations to find dataset results, putting data in and getting data out is anything but automated. Data has to be mapped into data structures of records in rows and columns to key-value pairs to XML to documents.
We are able to replace all that
We have recently developed a series of breakthrough AI technological capabilities to handle the complex processes associated with aggregating, correlating, storing, accessing and querying large and disparate data sets. These technologies work at various levels from the physical disk storage to the highest meta-abstraction available and everything in between and can work independently and autonomously to augment existing database and no-SQL systems or work together, in unison to replace these existing systems which are captive in their own inefficiencies.
Currently, this breakthrough AI capabilities can work with and augment the Associative Memory System technology we have previously developed at an overall cost of over $25 million dollars to date, which provides the basis for high-complexity, high-dimensionality, and high-volume data integration and correlation. These new technologies have been designed and developed to bring the power of total automation to the process of handling and managing data. Data is the stuff they put in tables. Information is data in meaningful relationships.
Working with No-SQL Data Stores (Hadoop, Mongo), Data Warehouses (Oracle), SQL Databases (SQL Server, MySQL, DB2), Triple Stores, etc., as the means to handle data, requires a high degree of human skill, oversight and administration, along with completely disparate methodologies for putting / accessing / locating data stored in the different storage systems.
SQL, Map-Reduce, SPARQL, each of which requires specialized technical knowledge of the technology employed and leads to ever-increasing costs. The reality is that the currently accepted and marketed ‘solutions’, although operational, are indeed challenged, inefficient and especially expensive and have no existing solutions to those challenges that aren’t just patches or bandages.