BIG DATA ANALYTICS TUTORIAL - TO CREATE POWER OUTAGE REPORTING SYSTEM IN K.P.L.C IN NAIROBI COUNTY

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USE OF BIG DATA ANALYTICS TUTORIAL - TO CREATE POWER OUTAGE REPORTING SYSTEM THROUGH TWITTER IN K.P.L.C IN NAIROBI COUNTY

Main Objective

Automated Reporting system.

GENERAL OBJECTIVES
1To mine big data from twitter on power outages.
2 To automate data cleaning process by geo-coding.
3. To automate process generating maps of outage incidences.
4. To visualize results of data mined via twitter on a web application.

Abstract
Kenya power Lighting Company (KPLC) is a utility service provider company dealing with power transmission and distribution across Kenya. One of the greatest challenge that a power transmission faces is scenario of power blackout. Customer will be start calling, emailing and complaining in social media, as an inconvenience caused by the power outage in their life. Due to overload of complains it become hard for KPLC to attend and respond to all the customers complaints. Hence, the requirement of a reporting system that filters only relevant complains from social media that have locational aspect. Complains from twitter have their geo-location properties like specific co-ordinates or locational aspects.
Kenya power Lighting Company (KPLC) require a reliable outage reporting system as compared to the existing situation where a customer has to walk to their offices, text # 95551 or call customer care in situation of reporting of a power outage. Hence, the need for an automated reporting system that enable easy communication between customer service department and maintenance. Kenya power Lighting Company [KPLC ] also require a system that can keep track on a specific staff personnel working on certain reported incidents and also status on each incident case.
The main aim of this project was to harness social media data to gain an insight to assist in fastening resolution process of a power outage The research main intent was to design a system that automate reporting system in Kenya power Lighting Company [ KPLC] by incident case management. The researcher was to crowd source social media and harvest data from twitter on power outage reporting. The mined tweets were filtered using a certain criteria that would only remain with relevant tweets. The filtered tweets were geocoded using nominatim engine and once their co-ordinates were got, then the system would map then out. Other tweets that had a meter number were automatically mapped out since Kenya power Lighting Company [KPLC] had a database with all meter numbers geo-referenced. Customers were advised to tweet their complaint and attach a meter number which would automatically geo-reference the tweet, hence suitable for mapping out. A web application was designed where a Business Process Model Notation (BPMN), Flowable engine was integrated that would assist in case management. Case management system enabled customer care department to easily communicate with maintenance department. Case management added the reporting system with a functionality that Kenya power Lighting Company [KPLC] was able to keep track of status of the power blackout restoration process. All the reported cases with relevant outage information and location aspect were mapped out in the web application.
The methodology approached to design this outage system was a simple incorporation of different Application Interfaces (APIs) to achieve a common objective. First, the project used tweepy for authentication of consumer keys and access tokens. Restful API (application interface) enables us consumer twitter data. .
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