Abstract: Information related to a time series can be predicted. For example, a repetitive characteristic of the time series can be determined by analyzing the time series for a pattern that repeats over a predetermined time period. An adjusted time series can be generated by removing the repetitive characteristic from the time series. An effect of a moving event on the adjusted time series can be determined. The moving event can occur on different dates for two or more consecutive years. A residual time series can be generated by removing the effect of the moving event from the adjusted time series. A base forecast that is independent of the repetitive characteristic and the effect of the moving event can be generated using the residual time series. A predictive forecast can be generated by including the repetitive characteristic and the effect of the moving event into the base forecast.

Abstract: Disclosed are methods, system, and computer program products useful for generating summary statistics for data predictions based on the aggregation of data from past time intervals. Summary statistics such as prediction standard errors, variances, confidence limits, and other statistical measures, may be generated in a way that preserves the basic distributional properties of the original data sets, to allow, for example, a reduction of the multiple data sets through the aggregation process, which may be useful for a prediction process, while determining statistical information for the predicted data.

Abstract: Data sets for a three-stage predictor can be automatically determined. For example, multiple time series can be filtered to identify a subset of time series that have time durations that exceed a preset time duration. Whether a time series of the subset of time series includes a time period with inactivity can be determined. Whether the time series exhibits a repetitive characteristic can be determined based on whether the time series has a pattern that repeats over a predetermined time period. Whether the time series includes a magnitude spike with a value above a preset magnitude can be determined. If the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) has the magnitude spike with the value above the preset magnitude threshold, the time series can be included in a data set for use with the three-stage predictor.

Abstract: Various embodiments are generally directed to techniques for reducing syntax requirements in application code to cause concurrent execution of multiple iterations of at least a portion of a loop thereof to reduce overall execution time in solving a large scale problem. At least one non-transitory machine-readable storage medium includes instructions that when executed by a computing device, cause the computing device to parse an application code to identify a loop instruction indicative of an instruction block that includes instructions that define a loop of which multiple iterations are capable of concurrent execution, the instructions including at least one call instruction to an executable routine capable of concurrent execution; and insert at least one coordinating instruction into an instruction sub-block of the instruction block to cause sequential execution of instructions of the instruction sub-block across the multiple iterations based on identification of the loop instruction.

Abstract: Systems and methods for generating one or more graphs corresponding to a data set are included. A computing system may receive user input data indicating a data set, the data set including one or more variables. Variable selection data indicating a subset of the one or more variables included in the data set may be obtained for the data set. An electronic representation of a graph selection structure may be generated based on the variable selection data. One or more graphs may be generated using the graph selection structure.

Abstract: Systems and methods for performing analyses on data sets to display canonical rules sets with dimensional targets are disclosed. A cross-corpus rule set for a given Topic can be generated based on the entire corpus of data. A first dimensional rule set can be generated based on a first context (e.g., based on the same Topic but using a first sub-domain of the corpus of data). A second dimensional rule set can be generated based on a second context (e.g., based on the same Topic but using a second sub-domain of the corpus of data). Key dimensional differentiators (e.g., for each dimension, or context, of the Topic) can be determined based on a comparison of the general rule set, the first dimensional rule set, and the second dimensional rule set. A canonical rule set visualization can be displayed. The visualization can highlight the dimensional selectors (e.g., those tokens, or nodes, that differ between the first dimensional rule set and the second dimensional rule set).

Abstract: An apparatus comprising a processor component to: receive metadata of data organization within a data set; receive indications of which node devices will be storing the data set as multiple data blocks within a data file; and receive, from each node device, a pointer request to a location within the data file for storing a data set portion as a data block. In response to the data set including partitioned data, for each request for a pointer: determine the location within the data file; generate a map data map entry for the data block; generate therein a sub-block count of data sub-blocks within the data block; generate therein a sub-entry for each data sub-block including size and a hashed identifier derived from a partition label; and provide a pointer to the node device. In response to successful storage of all data blocks, store the map data in the data file.

Abstract: An apparatus includes a processor component caused to: retrieve metadata of organization of data within a data set, and map data of organization of data blocks within a data file; receive indications of which node devices are available to perform a processing task with a data set portion; and in response to the data set including partitioned data, compare the quantities of available node devices and of the node devices last involved in storing the data set. In response to a match, for each map data map entry: retrieve a hashed identifier for a data sub-block, and a size for each of the data sub-blocks within the corresponding data block; divide the hashed identifier by the quantity of available node devices; compare the modulo value to a designation assigned to each of the available node devices; and provide a pointer to the available node device assigned the matching designation.

Abstract: An apparatus comprising a processor component to: provide, to a control device, an indication of availability to perform a processing task with one or more data set portions as a node device; perform a processing task specified by the control device with the one or more data set portions; and request a pointer to a location at which to store the one or more data set portions as a data block within a data file. In response to the data set including partitioned data, for each data set portion, include a data sub-block size of the data set portion and a hashed identifier derived from a partition label of a partition in the request; receive, from the control device, the requested pointer to the location; and store each data set portion as a data sub-block within the data block starting at the location within the data file.

Abstract: Techniques to manage virtual classes for statistical tests are described. An apparatus may comprise a simulated data component to generate simulated data for a statistical test, statistics of the statistical test based on parameter vectors to follow a probability distribution, a statistic simulator component to simulate statistics for the parameter vectors from the simulated data with a distributed computing system comprising multiple nodes each having one or more processors capable of executing multiple threads, the simulation to occur by distribution of portions of the simulated data across the multiple nodes of the distributed computing system, and a distributed control engine to control task execution on the distributed portions of the simulated data on each node of the distributed computing system with a virtual software class arranged to coordinate task and sub-task operations across the nodes of the distributed computing system. Other embodiments are described and claimed.

Abstract: Various embodiments include a system having interfaces, storage devices, memory, and processing circuitry. The system may include logic to render a portion of a first layer and a portion of a second layer for presentation, determine parameters of tokens for the second layer based a result of the rendering of the second layer, the parameters to include at least one of token width values, token offset values, line height values, and line top values. The system also to align the first layer and the second layer based on the parameters of the tokens for the second layer, and present the first layer and the second layer on a display, the first layer to present tokens and the second layer to receive events.

Abstract: A system that validates an application programming interface (API) call is provided. A key and a value associated with the key are read from a test script containing a script. The key and the value are separated by a colon. The key is included in first double quotes, and the value is included in second double quotes. Whether the key matches a plurality of keys defined for an API call is determined. Based on the key matching the plurality of keys defined for the API call, the API call is configured using the key and the value without any of the colon, the first double quotes, or the second double quotes. The configured API call is executed.

Abstract: Systems, methods, and computer program products are provided that perform modeling and stress testing algorithms without the need for running simulations and that provide exact or approximate solutions for predicting outcomes of states and distributions of states for components of a structure. The disclosed systems, methods, and products may employ a Markov iteration approach, such as an exact Markov iteration approach or a reduced or simplified Markov iteration approach for predicting states and distributions of states for components of a structure using an algorithm that reduces solution complexity as compared to approaches that employ simulations.

Abstract: Systems, methods, and computer program products are provided that perform modeling and stress testing algorithms without the need for running simulations and that provide exact or approximate solutions for predicting outcomes of states and distributions of states for components of a structure. The disclosed systems, methods, and products may employ a Markov iteration approach, such as an exact Markov iteration approach or a reduced or simplified Markov iteration approach for predicting states and distributions of states for components of a structure using an algorithm that reduces solution complexity as compared to approaches that employ simulations.

Abstract: A computer-readable medium is configured to determine a support vector data description (SVDD). For each of a plurality of values for a kernel parameter, an optimal value of an objective function defined for an SVDD model using a kernel function, a read plurality of data points, and a respective value for the kernel parameter is computed to define a plurality of sets of support vectors. A plurality of first derivative values are computed for the objective function as a difference between the computed optimal values associated with successive values for the kernel parameter. A plurality of second derivative values are computed for the objective function as a difference between the computed plurality of first derivative values associated with successive values for the kernel parameter. A kernel parameter value is selected where the computed plurality of second derivative values first exceeds zero.

Abstract: Various embodiments include a system having interfaces, storage devices, memory, and processing circuitry. The system may be coupled with one or more storage devices and may receive episode information for a patient from a storage device via one or more wired or wireless links, the episode information includes a plurality of episodes associated with the patient, each of the plurality of episodes is a specific instance of a medical condition. The system may generate a candidate episode pairs list comprising a plurality of candidate episode pairs. Embodiments may also include the system generating a transition list comprising episode pairs from the plurality of candidate episode pairs in the candidate episode pairs list and determining attribute relationships between the plurality of episodes for the patient based on episode pairs in the transition list, the attribute relationships used to attribute items between the plurality of episodes.

Abstract: This disclosure describes methods, systems, computer-readable media, and apparatuses for calculating a summary statistic. Calculating the summary statistic can be performed by identifying multiple subsets of a set of variable observations and assigning the subsets to grid-computing devices such that no two of the subsets are assigned to a same one of the grid-computing devices. A parallel processing operation that involves multiple processing phases at each of the grid-computing devices is then coordinated. The parallel processing operation includes each of the grid-computing devices inventorying the respectively assigned subset and generating inventory information representative of the respectively assigned subset. Subsequently, the inventory information generated by the grid-computing devices is received, and a summary statistic is determined by synthesizing the received inventory information.

Abstract: This disclosure describes methods, systems, computer-readable media, and apparatuses for calculating a summary statistic. Calculating the summary statistic can be performed by identifying multiple subsets of a set of variable observations and assigning the subsets to grid-computing devices such that no two of the subsets are assigned to a same one of the grid-computing devices. A parallel processing operation that involves multiple processing phases at each of the grid-computing devices is then coordinated. The parallel processing operation includes each of the grid-computing devices inventorying the respectively assigned subset and generating inventory information representative of the respectively assigned subset. Subsequently, the inventory information generated by the grid-computing devices is received, and a summary statistic is determined by synthesizing the received inventory information.

Abstract: A computing device updates date values in a read dataset to support data analytics. Outlier and non-outlier date values are identified by, for each date value as a respective date value, reading a predefined number of neighboring date values relative to the respective date value; computing a median value and a median absolute deviation value of the predefined number of neighboring date values; computing a difference between the respective date value and the median value; dividing an absolute value of the difference by the median absolute deviation value to define a deviation value; comparing the deviation value to a threshold deviation value; and, based on the comparison, identifying the respective date value as an outlier or a non-outlier date value. Each identified non-outlier date value is updated with a new date computed using a date offset value. Each updated, identified non-outlier date value is replaced in a date updated dataset.

Abstract: Systems and methods for conflict resolution and stabilizing cut generation in a mixed integer linear program (MILP) solver are disclosed. One disclosed method includes receiving a mixed integer linear problem (MILP), the MILP having a root node and one or more global bounds; pre-processing the MILP, the MILP being associated with nodes; establishing a first threshold for a learning phase branch-and-cut process; performing, by one or more processors, the learning phase branch-and-cut process for nodes associated with the MILP, wherein performing the learning phase branch-and-cut process includes: evaluating the nodes associated with the MILP, collecting conflict information about the MILP, and determining whether the first threshold has been reached; responsive to reaching the first threshold, removing all of the nodes and restoring a root node of the MILP; and solving, with the one or more processors, the MILP using the restored root node and the collected conflict information.