The observational era of gravitational-wave astronomy began in the Fall of
2015 with the detection of GW150914. One potential type of detectable
gravitational wave is short-duration gravitational-wave bursts, whose waveforms
can be difficult to predict. We present the framework for a new detection
algorithm for such burst events -- \textit{oLIB} -- that can be used in
low-latency to identify gravitational-wave transients independently of other
search algorithms. This algorithm consists of 1) an excess-power event
generator based on the Q-transform -- \textit{Omicron} --, 2) coincidence of
these events across a detector network, and 3) an analysis of the coincident
events using a Markov chain Monte Carlo Bayesian evidence calculator --
\textit{LALInferenceBurst}. These steps compress the full data streams into a
set of Bayes factors for each event; through this process, we use elements from
information theory to minimize the amount of information regarding the
signal-versus-noise hypothesis that is lost. We optimally extract this
information using a likelihood-ratio test to estimate a detection significance
for each event. Using representative archival LIGO data, we show that the
algorithm can detect gravitational-wave burst events of astrophysical strength
in realistic instrumental noise across different burst waveform morphologies.
We also demonstrate that the combination of Bayes factors by means of a
likelihood-ratio test can improve the detection efficiency of a
gravitational-wave burst search. Finally, we show that oLIB's performance is
robust against the choice of gravitational-wave populations used to model the
likelihood-ratio test likelihoods
