A Knowledge Graph Driven Approach for Edge Analytics
Additionally , in a situation where the modification or removal of an edge device is required , that change must be communicated throughout the system due to the inability of a vertically integrated solution to handle disparate heterogeneous deployments because there isn ’ t a filtering , normalization and routing , of messages based on ecosystem topology . Even robust horizontally-integrated systems such as the Cisco ® IOx platform require some degree of hardware standardization ( networking infrastructure ). The knowledge graph driven approach requires solely software level standardization , namely the ability to run containers .
Client Use Case Description
Following the generalized use case are two on-going specific use case examples , in the Oil and Gas ( O & G ) and Mining sectors , from clients for whom we are currently developing our edge analytics framework .
Large O & G Services Company
Consider the use case of a large multinational O & G field services provider with a complex organizational and operational structure . This client has been digitizing and networking their industrial assets in on-shore and off-shore oil / gas fields for decades and has garnered a mature understanding of and experience with Industrial IoT technologies and tools . In the process of doing so the client has accumulated technological “ debt ” over the years ranging from proprietary solutions , heterogeneous approaches and dated hardware involving several business units in pursuit of gathering data and running analytics on the edge . Thus , the client is dependent on and manages legacy technology with redundancies in hardware capabilities and software functionality across the stack . This leads to an opportunity to optimize on operational costs , increase efficiency by standardizing methodologies and adopt a modernized enterprise-wide Edge Analytics platform .
Challenges
The Brownfield nature of this client ’ s operational environment means any edge framework must be flexible enough to support existing operations and at the same time provide a seamless path forward toward ecosystem modernization , technology migration and device deployment . The diversity of needs for this organization means it must support multiple edge hardware devices , operating systems ( OS ’ s ), variety of data-processing and analytical requirements , data storage and data format needs .
In-flight Implementation Requirements
The O & G client stipulated the following key solution requirements :
1 ) Heterogeneous Software ( SW ) Support : support for applications capabilities ranging from Complex Event Processing ( CEP ) and rules engine for data processing , as well as employment of Artificial Intelligence ( AI ) open source library frameworks to reduce licensing costs .
2 ) Extensibility and Agnostic Support : expansion capability for vendor and other 3 rd party application integration , i . e ., support ecosystem of players
- 46 - September 2017