1. Appendices: Introduction to Business Intelligence DATE

2. Current landscape Solution Architecture A PPENDIX A

3. Current Architecture - LIME

4. Target Architecture - LIME

5. Current Architecture - Bahamas

6. Target Architecture - Bahamas

7. EDS scope and roadmap A PPENDIX B

8. Phase 1 Comverse SLU CDRs: Voice, SMS, MMS, content, mobile data Liberate unbilled (preload) and billed Itemized Messages (IMs). Archived Liberate IMs: 18-24 months billed IMs Phase 2 Network raw CDRs (i.e. fixed and mobile). Network DPI data (mobile data usage detail) Archived Comverse call history records Archived RTBS call history records (offload of legacy reporting DB) Phase 3 (possible candidates) Portal and self-service app activity logs LIMEWare and Gateway logs Radio network probe data (radio coverage and quality KPIs) Event Data Store –Phasing of data sources Introduction to Business Intelligence | Appendix B8 EDS will store up to 7 years detailed usage online for search and queries

9. To meet the full set of EDS requirements, and to standardize on a technology approach, we are leveraging a fully-integrated Apache stack comprising Cassandra, Hadoop and Hive. Cassandra provides the underlying resilient and fast database storage, Hadoop provides the bulk-analytics and search, with Hive providing the SQL Layer. Selected Solution from Cartesian/TMNG This cost-effective approach reduces risk by using some of the most popular deployed open-source Big- Data technologies available. Introduction to Business Intelligence | Appendix B9

10. EDS - Integration Architecture Digital Route’s Mediation Zone provides the mediation layer Architectural approach permits easy extension for further data sources such as mobile data usage and raw network CDRs EDS is deployed in Virtacore EDS versus ODS EDS keeps full record contents whereas ODS takes stripped down record sets EDS keeps records for up to 7 years, whereas ODS keeps usage detail for 3 months and then summarises ODS produces regular reports & KPIs Introduction to Business Intelligence | Appendix B10

11. Data Storage: Datastax and HDFS To improve data storage capacity per node and reduce cost a parallel data store is to be configured next to each Datastax data store. This data store will not be a part of the Datastax storage limit of 4TB and can provide another 4TB of storage per node. (Before replication and compression) Datastax provides job and task scheduling for each node. Regardless of the data store. Therefore Datastax is required on each node. (Datastax controls what tasks the processors should be working on) Concurrent tasks will impact performance. No nodes are reserved for Real-Time searches Each node contains Datastax controller software, Datastax Data store (Cassandra) and a HDFS data store Each node contains Datastax controller software, Datastax Data store (Cassandra) and a HDFS data store HDFS Store is controlled by the Datastax Job/Task Trackers. Datastax ‘OpsCenter’ will track running map reduce tasks on either store Datastax software is required on each node for the node to contribute to processing requests Introduction to Business Intelligence | Appendix B11

12. Searches and Queries Introduction to Business Intelligence | Appendix B12

13. Extended Search Screen Shot

14. Phase 2 Architecture Introduction to Business Intelligence | Appendix B14