The Telecommunications Industry, like the Financial Services Industry, has to process large volumes of rapidly changing data. The whole industry is concerned with capturing and communicating data, often in real-time, and transmitting the data to one or more subscribers that are often physically very far from the publishers.

Examples of Data Sources

• Telephone call detail records (CDRs) to capture telephone call.

• Internet usage records: bytes, service payloads, quality of service, etc.

• The current location of handsets or subscribers

• The current availability or other status of the network or service

• The current accounts of subscribers

• The customer care records and activities of its subscribers

Database Limitations

Databases are excellent repositories of historical records of the above Telecomm data sources, but have a lot of limitations in terms of being able to react in real-time or near real-time to changes in such data. There might be potentially thousands of queries to be run or other processing tasks that are required to be performed upon new or changed service data.

Here are some of the activities that might need to be performed:

• Data aggregation – combining the data to make more meaningful and actionable data

• Data validation, cleansing and correction

• Fraud detection

• Service outage or issue detection

• Cross selling detection

• Advertising relevancy

• Location-triggered services

• Time-triggered or time sensitive services

• Usage-triggered activities such as billing

• Alerting

SQLstream's RAMMS provides a much more scalable and responsive SQL-based engine for managing, reusing, transforming and auctioning data than RDBMS.