This is an expansion of benefits listed in the main EIQ Server page

1. Universal and uniform near real-time external indexing, query processing, and integration of ALL structured, unstructured and semi-structured data and information in multiple databases, files, documents, and e-mail

   	Connects like a database driver
   	Single-point access
   	Intra-organization and inter-organization
   	Can combine database queries and unstructured text searches
   	Queries are processed “virtually” in the indexes with no temporary or interim tables for table-joins or range queries
   	Can use data and information from one system to find data and information in another, e.g., table-joins ACROSS databases – heuristic data mining ACROSS databases and other data sources
   	Extremely simple SQL statements to EIQ Server – “rocket science” on the back-end – do not need to specify data sources (but can) or how table-joins or queries are processed
   	Use of standards for field names
   	Can set up “SuperSchemas” which create custom views (tables) of standard data and information fields – can be relational
   	Brings some structure to unstructured data and information and MORE structure to less-structured data such as flat-file systems

2. Universal and uniform access to different platforms and locations

   	Use native drivers/access where possible
   	Use secure LANs, WANs, VPNs, and IP (Internet, intranets, and extranets)
   	Secure logins, passwords and access levels to specific agencies, data sources within agencies, and fields within data sources

3. Leave source data in place in original format

   	Use third-party replication/ETL tools to update indexes from transaction logs for databases – spiders/crawlers for other data
   	Only indexes conform to standards – data remains unchanged
   	ROWIDs, primary keys or other unique identifiers used to directly retrieve final result-set data
   	Mapping tables between standard field names and actual data source field names, and security access established and maintained by the data source owner – no loss of control of data

4. Continue to use legacy applications (and data), but enable modern application access to legacy data, and legacy application access to modern databases

   	Also use as transition/migration tool from legacy to modern systems
   	Can “virtually” normalize legacy flat-file systems to a certain extent and “virtually” “flatten” modern relational databases for legacy applications

5. Scale through multi-tiered access to independently maintained indexes in different agencies and organizations

   	Can use secure SOAP
   	Can lead to eventual (secure) XML and Web Services
   	Able to submit queries and integrate responses from non-EIQ Server data sources – a federated database technique

6. Extremely fast query processing – typically, 10 to over 100 times faster than other systems, particularly in a multi-user environment

7. Real-time updates that are immediately available to queries

8. Embedded Value Indexes™ allow additional data and information to be stored in the indexes – the original data source is not altered in any way

   	Improve query performance – faster table-joins (not join-indexes)
   	Add value to original data
   	Connect or group data and information
   	Create virtual data warehouses/data marts – de-normalization

9. Link analysis and mapping, which externally provides and maps links between, or groups, disparate data and information

10. Significant benefits compared to a federated database approach. Federated systems are:

    	As fast as the slowest data source
    	Limited to the data indexed in individual systems and HOW it is indexed
    	Additional query loads on systems
    	In need of a detailed understanding of the system – indexes, resource requirements, etc.
    	Not necessarily able to allow multiple indexes on the same data
    	Generally, limited to databases; not files, documents, e-mail, etc.
    	Unable to add external tables, data and information to original data source accessible through indexes
    	Unable to add value to the original data in the indexes like Value Indexes
    	Difficult to use data and information from one data source to find data and information in another – heuristic data mining ACROSS data sources
    	Difficult to merge and work with result datasets, depending on metadata dictionaries and mapping

Send To Printer