Case Study - Data Mining Solutions

Data Mining : HP/IBM OSS

Objective

One of the leading telecom tower infrastructure management company in India, wanted to monitor their infrastructure status of all sites , create and monitor trouble ticker status.

Situation

Data of entire telecom sites , included many more infrastructure items over and above tower company requirement was available on HP and IBM OSS Platform ( Operation Support System). The need was to focus only on data of interest and analyze.

Additional Challenge

The network being live, site status changes to be monitored at regular intervals continuously.

Solutions Approach

  • Focusing on Relevant Fields of Information from TowerCo Perspective
  • Analyzing Status and Changes in Integrated way
  • Trouble Ticket Creation, Tracking and Analytics
  • Prescriptive Analytics.

Data Mining :Rolls Royce IoT COE Support

Objective

  • The company wanted their engineering teams should be abreast with latest IoT Solutions, Products which can be of interest in their domain
  • Normal Study of such IOT Devices on multiple web sites would waste their lots of time in
    • Visiting the sites
    • Going through products which are of same category, but specifications are beyond their requirement zone.
    • Also study the same repeated listing of same product multiple times across multiple sites
  • So they decided to develop a Web Section – IOT Centre of Excellence – to provide details on all relevant products at one place.
  • While identifying the products , the requirement was to focus on – Products of Relevance, Parameter Limits of Relevance, Option to Choose Brands of Relevance, Focus to look such details at prominent online sites
  • It was also required to avoid duplication of same product from multiple sites, ensure that sites are visited regularly so same product upgraded information is available, collect the pricing information and try to bring in unified currency values.
  • Pilot Phase I with study and analysis of data carried out across three online stores conducted, validated against client parameter boundaries and presented.
  • Future Stages :
  • Additional NLP Techniques to be deployed to enhance relevance of information collected/ collated.

Future Stages :

  • Additional NLP Techniques to be deployed to enhance relevance of information collected/ collated.

Thank You