Abstract: Data processing can be managed in a distributed computing environment (DCE). In one example, the DCE can receive a sequence of computing operations to be consecutively executed in the DCE. For each computing operation in the sequence, the DCE can receive input data for the computing operation, partition the input data into subsets, and determine whether the computing operation is linear or non-linear. The DCE can then apply different processing techniques to the subsets depending on whether the computing operation is linear or non-linear.

Abstract: A compressed data structure is disclosed for storing collected data and delivering ad-hoc data analysis. The compressed data structure can reduce hardware requirements, such as data storage requirements, and decrease processing requirements during ad-hoc data analysis. Raw data can be pre-aggregated to a base level of aggregation (base cell level) that is determined by the unique combination of attributes available for aggregation. Rather than storing the raw data, the entire distribution is stored at the base cell level along with a selection of pre-computed statistics of the raw data. At the time of ad-hoc analysis, pre-computed results based on the raw data can be retrieved or computed from the pre-computed statistics, or analysis of higher-level data can be computed on-demand based on the pre-aggregated base level data.

Abstract: A compressed data structure is disclosed for storing collected data and delivering ad-hoc data analysis. The compressed data structure can reduce hardware requirements, such as data storage requirements, and decrease processing requirements during ad-hoc data analysis. Raw data can be pre-aggregated to a base level of aggregation (base cell level) that is determined by the unique combination of attributes available for aggregation. Rather than storing the raw data, the entire distribution is stored at the base cell level along with a selection of pre-computed statistics of the raw data. At the time of ad-hoc analysis, pre-computed results based on the raw data can be retrieved or computed from the pre-computed statistics, or analysis of higher-level data can be computed on-demand based on the pre-aggregated base level data.

Abstract: An apparatus includes a processor and a storage storing instructions causing the processor to receive representation metadata indicating features of representation data to be generated from a plurality of representation portions, receive a command to generate at least one row of the representation data, determine a subset of data blocks of a data blob required to generate the at least one row, and a subset of node devices that store the subset of data blocks, for each node device of the subset of node devices, derive a node block map identifying at least one data item of a data block for generating a representation portion, transmit the node block maps to the subset of node devices; and transmit a command to the subset of node devices to each generate at least one row of one of the plurality of representation portions.

Abstract: An apparatus includes a processor and storage storing instructions causing the processor to store, at a node device of a grid of node devices, a data block of a data blob, receive data blob metadata indicative of an organization of data items within the data blob, receive a command to generate, from the data block, at least one row of a representation portion of a plurality of representation portions from which a 2D representation of the data blob is to be generated, use the data blob metadata and a node block map indicative of which data items of the data block are required to generate the representation portion to derive one or more transforms to be performed with the data block to generate the at least one row of the representation portion, and perform the one or more transforms with the data block to generate the at least one row.

Abstract: An apparatus includes a processor and a storage storing instructions causing the processor to determine whether an analysis routine is multi-threaded and calls a library function of a non-threadsafe library, and if so, causes the processor to: instantiate an analysis process for executing the analysis routine on multiple threads; instantiate an instance of the library for execution within a isolated library process; instantiate another instance of the library for execution within another isolated library process; retrieve library metadata providing a function prototype of the library function; employ the function prototype to generate an instance of a bridge routine to enable a call from the analysis routine on a first thread to the library function; employ the function prototype to generate another instance of the bridge routine to enable a call from the analysis routine on a second thread to the library function; and begin execution of the analysis routine.

Abstract: An apparatus includes a processor and storage storing instructions causing the processor to store, at a node device of a grid of node devices, a data block of a data blob, receive data blob metadata indicative of an organization of data items within the data blob, receive a command to generate, from the data block, at least one row of a representation portion of a plurality of representation portions from which a 2D representation of the data blob is to be generated, use the data blob metadata and a node block map indicative of which data items of the data block are required to generate the representation portion to derive one or more transforms to be performed with the data block to generate the at least one row of the representation portion, and perform the one or more transforms with the data block to generate the at least one row.

Abstract: An apparatus includes a processor and a storage storing instructions causing the processor to receive representation metadata indicating features of representation data to be generated from a plurality of representation portions, receive a command to generate at least one row of the representation data, determine a subset of data blocks of a data blob required to generate the at least one row, and a subset of node devices that store the subset of data blocks, for each node device of the subset of node devices, derive a node block map identifying at least one data item of a data block for generating a representation portion, transmit the node block maps to the subset of node devices; and transmit a command to the subset of node devices to each generate at least one row of one of the plurality of representation portions.

Abstract: An apparatus includes a processor and a storage storing instructions causing the processor to determine whether an analysis routine is multi-threaded and calls a library function of a non-threadsafe library, and if so, causes the processor to: instantiate an analysis process for executing the analysis routine on multiple threads; instantiate an instance of the library for execution within a isolated library process; instantiate another instance of the library for execution within another isolated library process; retrieve library metadata providing a function prototype of the library function; employ the function prototype to generate an instance of a bridge routine to enable a call from the analysis routine on a first thread to the library function; employ the function prototype to generate another instance of the bridge routine to enable a call from the analysis routine on a second thread to the library function; and begin execution of the analysis routine.

Abstract: Possible outcomes can be determined by combining simulation methods on a pool of input variables. Certain members of the pool are identified as members of a first set of variables (e.g., priority set), and certain other members of the pool of input variables are identified as members of a second set of variables (e.g., non-priority set). A first set of possible values for the first set of variables can be generated by applying a first simulation method. A second set of possible values for the second set of variables can be generated by applying a second simulation method that differs from the first simulation method in various ways, such as accuracy, completion time, and computational expense. A copula data structure can be used to maintain correlations between the variables of the pool of input variables when generating a hybrid set of simulated values based on the first and second simulation.

Abstract: Systems and methods are provided for providing secure transmission of software code, which includes a mathematical function, from a first computer to a second computer so that the mathematical function's content cannot be determined at the second computer. A method includes generating a secure container, where the secure container includes an encrypted representation of the mathematical function and metadata identifying the mathematical function encrypted in the secure container. The method further includes providing the secure container from the first computer to the second computer over a communication transmission medium, where the secure container is accessed at the second computer using the metadata to identify the mathematical function, and where the mathematical function contained within the secure container is decrypted and incorporated into program code in a compiled form so that the mathematical function can be used but the mathematical function's content cannot be determined at the second computer.

Abstract: Systems and methods for determining a future conversion curve between a first metric and a second metric are provided. A system and method can include receiving a first metric and a second metric. A plurality of conversion curves are retrieved, where each conversion curve has a corresponding weight. One or more paths between the first metric and the second metric are calculated using two or more conversion curves, where each path begins with the first metric and ends with the second metric, and where each path is associated with a path weight based upon the sum of the weights used to calculate the path. A shortest path between the first metric and the second metric is determined, where the shortest path is the path having the least weight, and a future conversion curve is generated by collapsing the two or more conversion curves of the shortest path.

Abstract: Computer-implemented systems and methods are provided for generating a simulated forecast based on members of a pool of input risk factor variables. Certain members of the pool of input risk factor variables are identified as members of a first set of variables, and certain other members of the pool of input risk factor variables are identified as members of a second set of variables. A first simulation is generated via a first simulation method using the first set of variables, and a second simulation is generated via a second simulation method that differs from the first simulation method using the second set of variables. The first simulation and the second simulation are generated using correlations among variables in the first set of variables and variables in the second set of variables.

Abstract: Systems and methods are provided for providing secure transmission of software code, which includes a mathematical function, from a first computer to a second computer so that the mathematical function's content cannot be determined at the second computer. A method includes generating a secure container, where the secure container includes an encrypted representation of the mathematical function and metadata identifying the mathematical function encrypted in the secure container. The method further includes providing the secure container from the first computer to the second computer over a communication transmission medium, where the secure container is accessed at the second computer using the metadata to identify the mathematical function, and where the mathematical function contained within the secure container is decrypted and incorporated into program code in a compiled form so that the mathematical function can be used but the mathematical function's content cannot be determined at the second computer.