@article{2143,
  author = {Deepak K Gangadhar},
  title = {Compass- Complex Event Processing Enabled By State Space Transformations},
  journal = {Progress in Computing Applications},
  year = {2016},
  volume = {5},
  number = {2},
  doi = {},
  url = {http://www.dline.info/pca/fulltext/v5n2/pcav5n2_1.pdf},
  abstract = {In a world inundated with information, it’s becoming increasingly difficult to filter out the unwanted, retain
what is needed, and finally garner insight from it. It’s not just information in the ‘traditional’ sense that is adding to this
deluge, but with the proliferation of affordable and versatile sensors, information chunks in the form of discrete events are
pouring in from places hitherto unthinkable... or unreachable. Compass focuses on taming this fire-hose towards providing
a smarter way to analyze and process events. Compass takes the n-dimensional event space through a series of transformation
which makes the event space more amenable for analysis. Firstly, it aggregates the discrete events across time and space
along the component axis. Secondly, it reduces the dimensionality of the state space by partitioning the state space into
several spatio-temporal Regions of Interests (RIs). The RIs are composed of one or more Space- Time Elements (STEs), which
are the lowest level units monitoring the incoming events. These RIs themselves cycle through various states like ‘hot’, ‘warn’,
‘alert’, which will enable remedial actions to be triggered. With these two transformations, Compass paves the way for
‘emergent’ behavior; where the system can be analyzed at macro-level states by making use of the micro-level events.},
}