Abstract: A scheduler is provided for tall algorithms that include control flow statements that are dependent on tall computations. Thunks may be defined for the tall algorithms and the boundaries or limits of the thunks may be determined by places where tall variables are required to be gathered. The scheduler executes the tall algorithms by calling individually the thunks from the tall algorithms and pausing when a gather operation is required. The scheduler collectively gathers the tall variables from different tall algorithms by using an optimizer to combine I/O operations for the tall algorithms. The scheduler may put back the gathered variables into the context of each tall algorithm, and the scheduler may resume with the next thunk for each tall algorithm. This process may be repeated until all tall algorithms are finished.

Abstract: A device may include one or more processors. The device may receive program code that identifies operations to be performed with regard to a data set to determine an output value. The device may identify a plurality of read operations corresponding to the operations. The plurality of read operations may be performed to obtain respective inputs of the operations. The device may generate a first data structure that identifies the operations and the plurality of read operations. The device may identify two or more operations, of the operations, that receive a particular input. The device may generate a second data structure that includes a combined operation. The combined operation may be generated based on the two or more operations, and may be associated with fewer read operations than the two or more operations. The device may determine the output value based on the second data structure.

Abstract: A computing device-implemented method includes receiving an additive tree; assigning data associated with the additive tree to one or more initial clusters; partitioning the additive tree into one or more pairs of additive sub-trees corresponding to one or more binary segmentations; computing a set that includes partitions resulting from a combination of the one or more initial clusters and the one or more pairs of additive sub-trees; evaluating one or more partitions of the set with one or more cluster validation criteria; storing one or more evaluation results for the one or more partitions; selecting at least one partition from the one or more partitions of the set that satisfies the one or more cluster validation criteria, where the at least one partition is associated with an optimal evaluation result; and removing at least one of the binary segmentations that corresponds to the at least one partition.

Abstract: A method for processing data in two data sets may include executing a constrained time domain warping (CDTW) algorithm to compare the data in the two data sets. The CDTW algorithm may use a band constraint that defines a function used to identify potential matches between observations in the two data sets. The CDTW algorithm may also use a width constraint that defines a number of matched observations that are to be scored.

Abstract: A method of displaying the probabilities associated with a Markov model as a series of interactive plots is disclosed. Three dimensions of probability data associated with a Markov model are presented to a user in a concise manner. Heat maps are used to display probability data from a Markov model so that the user can identify and understand hidden patterns and information in the data such as state and emission transitions which are not possible to see with conventional state diagram plots.