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Why DBXten?

 For a database application that stores and accesses very large sequences of data, why not use a server extension that, on average:

•    retrieves your data more than 10 times faster, for single and concurrent queries
•    decreases your database size by a factor of 25, reducing your storage requirements
•    generates indexes more than 60 times faster
•    speeds up insertion of rows by a factor of 3

You can achieve all these results using DBXten, a patented DataBase eXtension technology developed by BCS.

How does it work?

Rather than store the data values in separate rows, or even in external files, our technology brings them all into the database in a modular and highly efficient way. To provide fast search and retrieval capabilities, DBXten takes advantage of this storage mechanism to generate fast and compact indexes on the stored data.

DBXten is ideal for applications that need to generate, access, and process high-volume temporal or spatial sequence data, such as:

•    geophysical prospecting (multidimensional pressure, acceleration data)
•    financial analysis (bid value, ask value data)
•    GIS (vector, 2D and 3D polygon, DEM data)
•    resource management (grid-based environmental data)
•    meteorology (multidimensional grids of pressure, humidity, velocity data)
•    acoustic array analysis (multi-channel pressure data)
•    tracking (multi-channel location data)
•    ocean observatories (conductivity, temperature, depth, current data)

Which databases can DBXten run on?

•    IBM Informix,
•    Oracle, and
•    PostgreSQL

What standards will DBXten be compliant with?

We are currently working towards ensuring compliance of DBXten with the following standards:
    •    Open Geospatial Consortium’s Sensor Web Enablement Framework
            o    Sensor Observation Service
            o    Sensor Markup Language
            o    Observation & Measurement
    •    Open Geospatial Consortium’s Catalogue Services for the Web

When to use DBXten?

DBXten will produce dramatic improvements in efficiency for your database solutions whenever you are dealing with so-called “weak entity tuple data”; the high-volume data applications listed above are of this type. The “weak entities” correspond to data items (such as stock prices or temperatures) that only exist when a parent entity (a particular stock or sensor) exists. The “tuples” denote the relation between certain components of a data sequence; e.g., {time, latitude, longitude} in a location-tracking application.

Where can I get more information?

    •    DBXten Programmer's Guide (Linux) for PostgreSQL.
    •    DBXten Programmer's Guide (Linux) for IBM Informix.
    •    Click here to read more about DBXten and see some test results.

To find out how to use DBXten with your application, contact BCS.