Abstract: Embodiments include techniques to receive computer-executable query instructions to solve a MILP problem, the query instructions including a first expression conveying an objective function and side constraint that define a master problem of the MILP problem, a second expression conveying a mapping of graph data to a graph, and a third expression conveying a selection of a graph-based algorithm to solve a subproblem of the MILP problem; a subproblem component to replace the third expression with a fourth expression during decomposition of the MILP problem, the fourth expression including instructions to implement the graph-based algorithm to solve the subproblem; and an execution control component to perform iterations of solving the MILP problem that include executing the first expression to derive a solution to the master problem; and executing the fourth expression to derive a solution to the subproblem based on the mapping and the master problem solution.

Abstract: A regularizer process is started by a first thread executing at a computing device. The regularizer process is a separate process from a process executing on the first thread. The started regularizer process sends a presence announcement to a controller device and receives a presence acknowledgement from the controller device. The started regularizer process sends a notification to the first thread that it is ready to receive streamed data. The started regularizer process receives, from the controller device, an indicator of a data provider system that is to connect to it, receives a connection request from the data provider system, compares the received connection request to the received indicator, sends a connection acknowledgement to the data provider system when the received connection request matches the received indicator, accepts a connection from the data provider system, and receives the streamed data through the connection.

Abstract: A method of proportional highlighting of data is provided. A graph presented on a display includes a first axis, a second axis, and a first value marker that indicates a value determined from data selected for presentation. The first axis includes a minimum value and a maximum value. The second axis includes a plurality of category values. An indicator identifying a subset of the data is received. A proportional value is determined for the first value marker based on the received indicator. A second value marker indicating the proportional value is presented on the graph overlaid on the first value marker when the determined proportional value is between the minimum value and the maximum value. A scale adjustment marker is presented on the graph without adjusting the first axis when the determined proportional value is not between the minimum value and the maximum value.

Abstract: A computing device to perform incident identification on streamed event data is provided. An event block object is received. The event block object includes a measurement data value generated by a sensor. A plurality of measurement data values and associated time values are extracted for the plurality of event block objects that occur during a time window. A similarity measure is computed between the measurement data values and each of a plurality of reference incident signatures. A best matching reference incident signature is selected from the similarity measures. The similarity measure for the selected best matching reference incident signature is compared to a threshold value. When the selected similarity measure satisfies the threshold value, a type of incident for the extracted measurement data values is identified as a type of incident defined for the selected best matching reference incident signature and is output to an incident information subscribing device.

Abstract: A system for machine training can comprise one or more data processors and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: accessing a dataset comprising data tracking a plurality of features; determining a series of values for a regularization parameter of a sparse support vector machine model, the series including an initial regularization value and a next regularization value; computing an initial solution to the sparse support vector machine model for the initial regularization value; identifying, using the initial solution, inactive features of the sparse support vector machine model for the next regularization value; and computing a next solution to the sparse support vector machine model for the next regularization value, wherein computing the next solution includes excluding the inactive features.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.

Abstract: A method of determining a number of clusters for a dataset is provided. Centroid locations for a defined number of clusters are determined using a clustering algorithm. Boundaries for each of the defined clusters are defined. A reference distribution that includes a plurality of data points is created. The plurality of data points are within the defined boundary of at least one cluster of the defined clusters. Second centroid locations for the defined number of clusters are determined using the clustering algorithm and the reference distribution. A gap statistic for the defined number of clusters based on a comparison between a first residual sum of squares and a second residual sum of squares is computed. The processing is repeated for a next number of clusters to create. An estimated best number of clusters for the received data is determined by comparing the gap statistic computed for each iteration of the number of clusters.

Abstract: A method of creating an object store is provided. Node table information reading and link table information are read. The node table information includes node information for a plurality of nodes. The link table information includes link information between pairs of nodes of the plurality of nodes. An anchored network record is created for each node of the plurality of nodes based on the node information and the link information and a defined maximum degree of separation. The anchored network record includes anchor node information associated with an anchor node of the anchored network record and a node record for each node of the plurality of nodes that is within the defined maximum degree of separation from the anchor node of the anchored network record. The created anchored network record is stored for each node of the plurality of nodes.

Abstract: Systems and methods are included for adjusting a set of predicted future data points for a time series data set including a receiver for receiving a time series data set. One or more processors and one or more non-transitory computer readable storage mediums containing instructions may be utilized. A count series forecasting engine, utilizing the one or more processors, generates a set of counts corresponding to discrete values of the time series data set. An optimal discrete probability distribution for the set of counts is selected. A set of parameters are generated for the optimal discrete probability distribution. A statistical model is selected to generate a set of predicted future data points. The set of predicted future data points are adjusted using the generated set of parameters for the optimal discrete probability distribution in order to provide greater accuracy with respect to predictions of future data points.

Abstract: A method of identifying a set of parameters representative of a data set is provided. An eigen decomposition of a covariance matrix is calculated to form a decomposed matrix and an eigenvalue vector. The covariance matrix is calculated for a matrix of data including a plurality of data values for each of a plurality of parameters. The decomposed matrix includes a number of eigenvectors equal to a number of the plurality of parameters with each eigenvector including a coefficient for each parameter. The eigenvalue vector includes an eigenvalue defined for each eigenvector. A first matrix is created by rank ordering the coefficient within each parameter of the plurality of parameters for each of the plurality of parameters. A score is determined for each parameter using the created first matrix and the eigenvalue vector. A parameter set is identified based on the determined score for each parameter.

Abstract: A computing device presents a cluster visualization based on a neural network computation. First centroid locations are computed for first clusters. Second centroid locations are computed for second clusters. Each centroid location includes a plurality of coordinate values where each coordinate value relates to a single variable of a plurality of variables. Distances are computed pairwise between each centroid location. An optimum pairing is selected based on a minimum distance of the computed pairwise distances where each pair is associated with a different cluster of a set of composite clusters. Noised centroid location data is created. A multi-layer neural network is trained with the noised centroid location data. A projected centroid location is determined in a multidimensional space for each centroid location as values of hidden units of a middle layer of the multi-layer neural network. A graph is presented for display that indicates the determined, projected centroid locations.

Abstract: A computer-readable medium is provided that causes a computing device to serve data resources. A nozzle is instantiated for a resource based on a media type associated with both the nozzle and the resource and starts a subscriber thread and a rendering thread. The subscriber thread receives a block of streamed data from a publishing device, stores the block in a queue, and receives a request to drain the queue. The block includes a unique identifier of an event associated with the media type. The rendering thread reads the block from the queue, renders the block, and stores the rendered block in a pre-allocated block of memory based on the unique identifier. A reference to the pre-allocated block of memory is stored in a tree map based on the unique identifier. The instantiated nozzle sends the rendered block to a requesting event client system.

Abstract: A computing device to assign observations to clusters based on a statistical probability is provided. A first cluster assignment is defined by assigning the plurality of observations to a first set of clusters. A second cluster assignment is defined by assigning the plurality of observations to a second set of clusters. A set of composite clusters is defined based on the defined first set of clusters and the defined second set of clusters. For each observation, a statistical probability value for assigning an observation to each composite cluster of the defined set of composite clusters is computed based on the first and second cluster assignments and a composite cluster assignment is defined by assigning the observation to a cluster of the set of composite clusters based on the computed statistical probability value. The defined composite cluster assignment is stored.

Abstract: In a computing device supporting distributed stream processing, a request is received from a controller device to redistribute blocks storing streamed data. The request indicates that a number of blocks stored on the computing device be sent to a second computing device. The controller device controls distribution of analytic results to a data access system. The analytic results are computed from the streamed data. The indicated number of blocks are selected from the blocks storing the streamed data. The selected blocks are sent to the second computing device. Pointers to remaining blocks of the blocks storing the streamed data are updated.

Abstract: A computer-implemented method of determining actions outside of a norm is provided. The method comprises: generating an actor state vector and a peer group state vector, wherein the actor state vector identifies a characteristic for an actor in each of a plurality of categories and the peer group state vector identifies a characteristic for a peer group in each of the plurality of categories, transforming the actor state vector into a first sampled wave series representation using a first wave series transformation, transforming the population state vector into a second sampled wave series representation using a second wave series transformation, and filtering the first sampled wave series representation and the second sampled wave series representation to identify a deviation of the first wave series representation from the second wave series representation in a phase or a magnitude.

Abstract: In accordance with the teachings described herein, systems and methods are provided for clustering time series based on forecast distributions. A method for clustering time series based on forecast distributions may include: receiving time series data relating to one or more aspects of a physical process; applying a forecasting model to the time series data to generate forecasted values and confidence intervals associated with the forecasted values, the confidence intervals being generated based on distribution information relating to the forecasted values; generating a distance matrix that identifies divergence in the forecasted values, the distance matrix being generated based the distribution information relating to the forecasted values; and performing a clustering operation on the plurality of forecasted values based on the distance matrix. The distance matrix may be generated using a symmetric Kullback-Leibler divergence algorithm.

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: Systems and methods for identifying data files that have a common characteristic are provided. A plurality of data files are received. The plurality of data files include one or more data files having the common characteristic. A list of key terms is generated from the plurality of data files. Data files from the plurality of data files that have an association with a social community are identified, where the social community is defined by one or more features. The list of key terms is updated based on an analysis of the identified features. The updated list of key terms is used to identify other data files that have the common characteristic.

Abstract: Electronic communications can be normalized using feature sets. For example, an electronic representation of a noncanonical communication can be received, and multiple candidate canonical versions of the noncanonical communication can be determined. A first feature set representative of the noncanonical communication can be determined by splitting the noncanonical communication into at least one n-gram and at least one k-skip-n-gram. Multiple comparison feature sets can be determined by splitting multiple terms in training data into respective comparison feature sets. Multiple Jaccard index values can be determined using the first feature set and the multiple comparison feature sets. A subset of the multiple terms in the training data in which an associated Jaccard index value exceeds a threshold can be selected. The subset of the multiple terms can be included in the multiple candidate canonical versions.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.