Publications

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We want to organize a body of trajectories in order to identify, search for, classify and predict behavior among objects such as aircraft and ships. Existing compari- son functions such as the Fr'echet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as total distance traveled and distance be- tween start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally, these features can generally be mapped easily to behaviors of interest to humans that are searching large databases. Most of these geometric features are invariant under rigid transformation. We demonstrate the use of different subsets of these features to iden- tify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories, predict destination and apply unsupervised machine learning algorithms.

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This SAND report summarizes the activities and outcomes of the Network and Ensemble Enabled Entity Extraction in Information Text (NEEEEIT) LDRD project, which addressed improving the accuracy of conditional random fields for named entity recognition through the use of ensemble methods.

We present an algorithm to maintain the connected components of a graph that arrives as an infinite stream of edges. We formalize the algorithm on X-stream, a new parallel theoretical computational model for infinite streams. Connectivity-related queries, including component spanning trees, are supported with some latency, returning the state of the graph at the time of the query. Because an infinite stream may eventually exceed the storage limits of any number of finite-memory processors, we assume an aging command or daemon where "uninteresting" edges are removed when the system nears capacity. Following an aging command the system will block queries until its data structures are repaired, but edges will continue to be accepted from the stream, never dropped. The algorithm will not fail unless a model-specific constant fraction of the aggregate memory across all processors is full. In normal operation, it will not fail unless aggregate memory is completely full. Unlike previous theoretical streaming models designed for finite graphs that assume a single shared memory machine or require arbitrary-size intemediate files, X-stream distributes a graph over a ring network of finite-memory processors. Though the model is synchronous and reminiscent of systolic algorithms, our implementation uses an asynchronous message-passing system. We argue the correctness of our X-stream connected components algorithm, and give preliminary experimental results on synthetic and real graph streams.