The advanced era of gravitational-wave astronomy, with data collected in part by the LIGO gravitational-wave interferometers, has begun as of fall 2015. 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 -- called \textit{oLIB} -- that can be used in relatively low-latency to turn calibrated strain data into a detection significance statement. This pipeline consists of 1) a sine-Gaussian matched-filter trigger generator based on the Q-transform -- known as \textit{Omicron} --, 2) incoherent down-selection of these triggers to the most signal-like set, and 3) a fully coherent analysis of this signal-like set using the Markov chain Monte Carlo (MCMC) Bayesian evidence calculator \textit{LALInferenceBurst} (LIB). These steps effectively compress the full data stream into a set of search statistics for the most signal-like events, and we use elements from information theory to minimize the amount of information regarding the signal-versus-noise hypothesis lost during this compression. We optimally extract this information by using a likelihood-ratio test (LRT) to map these search statistics into a significance statement. Using representative archival LIGO data, we show that the algorithm can detect gravitational-wave burst events of realistic strength in realistic instrumental noise with good detection efficiencies across different burst waveform morphologies. We also demonstrate that the combination of search statistics by means of an LRT can improve the detection efficiency of our search. Finally, we show that oLIB's performance is robust against the choice of gravitational-wave populations used to model the LRT likelihoods.
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