Abstract: This paper addresses the problem of causally predicting the top-k most likely next events over real-time event streams. Existing approaches have limitations—(i) they model causality in an acyclic causal network structure and search it to find the top-k next events, which does not work with real world event streams as they frequently manifest cyclic causality, and (ii) they prune out possible non-causal links from a causal network too aggressively and end up omitting many less frequent yet important causal links. We overcome these limitations using a novel event precedence model (EPM) and a run-time causal inference mechanism. The EPM constructs a Markov chain incrementally over event streams, where an edge between two events signifies a temporal precedence relationship between them, which is a necessary condition for causality. Then, the run-time causal inference mechanism performs causality tests on the EPM during query processing, and temporal precedence relationships that fail the causality test in the presence of other events are removed. Two query processing algorithms are presented. One performs exhaustive search on the model and the other performs more efficient reduced search with early termination. Experiments using two real data sets (cascading blackouts in power systems and web page views) verify efficacy and efficiency of the proposed probabilistic top-k prediction algorithms.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).