Today’s software solutions are hampered by the complexity, overhead and expense inherent to harvesting information from data as it currently exists in conventional data management architectures.

Today most data management and retrieval solutions very often add constraints to already complex operational solutions such as:

• Complex and time consuming set-up of the infrastructures
• Performance weaknesses when handling distributed or high-volume data
• Set-up restrictions when reporting requirements change
• Long or recurring planning and set-up cycles
• No or slow on-the-fly analytics capabilities if data is not indexed

Thus speed, flexibility, smart processing and scalability are the imperatives of current data management and retrieval solutions.

Most industry standard data is non numerical text information. Computers process such text information per every single character. This requires millions of computer instructions to find, examine, and manipulate text information. To compensate for this indexing schemes have been created for each ‘most sought for’ field they want to find and retrieve quickly. Each individual word is now assigned an arbitrary number resulting in the complete loss of the semantic information that causes an even more significant loss: that of the context in which words were used. Without context there is no information! Though the contextual information remains hidden in the original text, to process the original text without indexing would be so time consuming that it would bring the fastest computer to its knees. In summary, traditional technology is confronted with a dilemma. The choice is between two mutually exclusive phenomena: speed but loss of context on the one hand, and context but loss of speed on the other.

The Hilbert Engine, together with the other components of the Hilbert Architecture (the Hilbert Space and –Network), resolves the dilemma faced by conventional BI solutions - that speed and data context are mutually exclusive It also solves the five major challenges in ultra high-speed performance analytics: integrating distributed data from multiple sources, high speed analysis of data, post-implementation flexibility and the application of smart algorithms to achieve a fast and economical implementation.

The Hilbert Engine has two components, the Hilbert Processor and the Hilbert Space, it supports all operations necessary to access, manipulate, store and analyze data within the Hilbert Space. The Hilbert Space itself is the container of all data in a total numerical representation and therefore can be accessed by mathematical algorithm to maximize data storage, manipulation and analysis. The Hilbert Engine also coordinates the connection of multiple Hilbert Engines into a networked Hilbert Infrastructure.

To make data from databases (structured data) or text from text files (unstructured data) available to the Hilbert Space and to access and analyze it, the Hilbert Engine performs three major steps:

Migration Parsing information from table format into single-word files Quantification Converting numbers and text data into unique integers and storing the data along with its context in a multi-dimensional data representation (the Hilbert Space) Access & Analysis Finding and accessing data and their contextual relationships in Hilbert Space by applying advanced mathematical operations, and re-converting them to their original representational form.

The conversion of text and numbers to a unique integer representation and the flexibility in set-up and post-set-up environments clearly positions the Hilbert Technology far in the forefront of information science, and delivers processing speed and analytical performance unmatched by any conventional solution that uses traditional database approaches. The numerical representation of all data in a multidimensional space allows unmatched performance gains through the use of highly efficient mathematical operations.

• High speed data access and analysis through the use of highly efficient mathematical algorithms
• Ability to analyze data in ways unthinkable before Hilbert
• All data can be loaded, no need for pre-cleansing, de-duping or format transformation
• No data load restrictions for analysis and reporting
• Ad-hoc queries without any re-indexing or performance degradation
• Maximum end-user flexibility in creating custom reports and analyses
• Support of multi-data store processing environments, data can be viewed as an integrated whole
• Data consolidation capability with no interruption of operational databases
• No complex up-front (ETL) process of forcing all data into a common format and common structure (and no loss of data that ‘doesn’t fit’ or is ‘too dirty’)
• Low cost of ownership and rapid time-to-benefit
• Data representation in Hilbert Space independent of specific reporting requests
• Maximum end-user flexibility in creating reports and analyzes