Organizations are overwhelmed by data streams from products, assets, cloud services, apps & IT infrastructure.  Most data is only ephemerally useful, but streams never stop. How can they derive continuous intelligence and automate decisions without a store-then-analyze architecture?

This talk will present an Apache 2.0 licensed platform for continuous intelligence (SwimOS)  that uses stateful in-memory processing for continuous analysis, learning and prediction.  Swim apps

  • Always have an answer:  Algorithms have been to be adapted to analyze, train and predict continuously - with computation driven by data. 
  • Continuously analyze:
    • Each event is statefully processed in memory, in real-time, offering 6 orders of magnitude performance win over database accesses.
    • Analysis is continuous because data streams are boundless. Insights are necessarily “up to now”, and also form a real-time stream 
  • Analyze in context: Fluid relationships between real-world data sources - like containment or geospatial proximity, and computed relationships like correlation - are critical for applications that reason about the meaning of events.  SwimOS allows algorithms to continuously compute system-wide insights for contextually related 'things', even as those relationships change

The talk will demonstrate an application that processes > 4PB/day of signal data from mobile cell towers to enable the operator to optimize connection quality on-the-fly for over 150M mobile devices.

SwimOS is a set of small extensions to Java reminiscent of the actor model, that uses an application-state cache-coherency protocol called WARP that ensures that concurrent in-memory actors - called web agents - stay in sync, even when distributed. SwimOS uses streaming data to build a scaled-out graph of stateful, concurrent web agents that are analytical “digital twins” of data sources.  Each statefully processes raw data from a single source.  Agents link to each other based on context discovered in the data, building a graph that reflects real-world relationships.  Linked agents see each other’s state changes in memory, in real-time. Agents concurrently compute on their own states and that of others they are linked to.  They analyze, learn and predict, and continuously stream enriched insights & responses to users, UIs and enterprise applications. 

Simon Crosby is the CTO of SWIM.AI. Previously, Simon was a co-founder and CTO of Bromium, a security technology company. At Bromium, Simon built a highly secure virtualized system to protect applications. Prior to Bromium, Crosby was the co-founder and CTO of XenSource before its acquisition by Citrix, and later served as the CTO of the Virtualization and Management Division at Citrix. Previously, Crosby was a principal engineer at Intel. Crosby was also the founder of CPlane, a network-optimization software vendor. Simon has been a tenured faculty member at the University of Cambridge.



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