Abstract: The present invention is directed towards systems and methods for decomposing a complex problem or task into one or more constituent components, operating in parallel over a plurality of computing devices in communication over a network. A system according to the present invention comprises one or more pipelets. A given pipelet comprises a read data interface operative to receive incoming data, one or more functions for processing the incoming data, and a write data interface operative to make the processed incoming data available as output data to be further processed. The system according to the present embodiment further comprises a controller operative to receive a pipeline specification that identifies the one or more pipelets as belonging to a pipeline, generate a dependency map that identifies an order in which to execute the one or more pipelets and execute the pipelets according to the dependency map to generate a result.

Abstract: In one implementation, a method is provided for increasing relevance of database search results. The method includes receiving a subject query string and determining a trained edit distance between the subject query string and a candidate string using trained cost factors derived from a training set of labeled query transformations. A trained cost factor includes a conditional probability for mutations in labeled non-relevant query transformations and a conditional probability for mutations in labeled relevant query transformations. The candidate string is evaluated the for selection based on the trained edit distance. In some implementations, the cost factors may take into account the context of a mutation. As such, in some implementations multi-dimensional matrices are utilized which include the trained cost factors.

Abstract: In one implementation, a method is provided for increasing relevance of database search results. The method includes receiving a subject query string and determining a trained edit distance between the subject query string and a candidate string using trained cost factors derived from a training set of labeled query transformations. A trained cost factor includes a conditional probability for mutations in labeled non-relevant query transformations and a conditional probability for mutations in labeled relevant query transformations. The candidate string is evaluated the for selection based on the trained edit distance. In some implementations, the cost factors may take into account the context of a mutation. As such, in some implementations multi-dimensional matrices are utilized which include the trained cost factors.

Abstract: In one implementation, a method is provided for increasing relevance of database search results. The method includes receiving a subject query string and determining a trained edit distance between the subject query string and a candidate string using trained cost factors derived from a training set of labeled query transformations. A trained cost factor includes a conditional probability for mutations in labeled non-relevant query transformations and a conditional probability for mutations in labeled relevant query transformations. The candidate string is evaluated for selection based on the trained edit distance. In some implementations, the cost factors may take into account the context of a mutation. As such, in some implementations multi-dimensional matrices are utilized which include the trained cost factors.

Abstract: A screening device includes a screen and frame, and is useful in permitting fishermen to detect conditions of the water to be fished for optimum bait and lure selection. The screening device allows a fisherman to collect aquatic insects rising through the water to be fished, thus alerting the fisherman as to the food being seen and eaten by the fish, and identify what depth the insects are in the water. With this knowledge, the fisherman can select the proper lure or fly to catch the fish present in the tested waters.

Abstract: In one implementation, a method is provided for increasing relevance of database search results. The method includes receiving a subject query string and determining a trained edit distance between the subject query string and a candidate string using trained cost factors derived from a training set of labeled query transformations. A trained cost factor includes a conditional probability for mutations in labeled non-relevant query transformations and a conditional probability for mutations in labeled relevant query transformations. The candidate string is evaluated the for selection based on the trained edit distance. In some implementations, the cost factors may take into account the context of a mutation. As such, in some implementations multi-dimensional matrices are utilized which include the trained cost factors.