When it comes to performing analysis on unstructured data we usually forget how much data there is to perform computation on and how much it relies on scale computing. The terms like Big Data, Map-Reduce, and Parallel programming get thrown around commonly nowadays and many engineers are working towards building computer architecture which will help enable efficient computation of unstructured data and tackle large-scale problems. Intel and Katana Graph Team have started a collaboration to port and optimize the Katana Graph Engine on Intel Xeon for scalable processing. What this will do is enable users to exploit high-performance, scale-out parallel computing and compute large problems with unstructured data such as network analysis with unmatched efficiency. In the modern era unstructured datasets are very common in social network analysis, security and authentication, biomedical and pharma applications and many more.
One key usage of large unstructured data sets and performing network analysis that we all are aware of by now is epidemiological studies for modeling the spread of infectious diseases like COVID-19. The Katana Graph Engine performs computation on any problem involving connected data and hence all network analysis will be enabled through this upgrade. The Katana Graph Engine has been widely used as a major defense contractor to implement a system that will detect real-time intrusion in computer network(s) and performs heavy data mining on how users interact in the network and analyze their patterns. All of these tasks take long time if you consider the number of users to be in billions and which is why concepts such as Parallel Programming and Big Data exists where we enable the use of extra hardware to increase efficiency.
The collaboration of Intel and Katana Graph Engine is just one example of how many companies are looking ahead into the future and see the need for heavy network analysis on unstructured data. Many are starting to adapt to idea of using such technologies to gain better information through Machine Learning and Artificial Intelligence to improve their software. Through network analysis and quick network analysis it will enable us to discover interrelations between sets of metrics which is not possible today and there are layers upon layers of insights that can be derived from a single data point. There is data hidden inside data and there are unique connections between these layers depending on how one perceives it. Therefore I believe this is an exciting collaboration of hardware and software which could revolutionize how we perceive network analysis the extent to which it can be performed.
References
Group, K., 2020. Katana Graph Collaborating With Intel On Enterprise-Scale Computing On Unstructured Data :: Katana Graph. [Accessed 22 October 2020] https://katanagraph.com/news/katana-graph-intel-collaboration
H. (2020, October 19). Intel and Katana Graph Team on Large-scale Graph Analytics. Retrieved October 22, 2020, from https://www.hpcwire.com/off-the-wire/intel-and-katana-graph-team-on-large-scale-graph-analytics/