Abstract: Various embodiments herein each include at least one of systems, methods, and software for instantiating, executing, and operating dynamic hybrid computing environments, such as in cloud computing. Some such embodiments include allocating computing resources of a first server cluster to instantiate a first cluster and to establish a computing session. This embodiment may then initiate execution of a program within the first cluster that offloads at least one computing task to a second cluster, when the second cluster is instantiated, to leverage high-computing speed performance capabilities of the second cluster with regard to certain computing operations. Upon completion of program execution, the second cluster is then deallocated.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for instantiating, executing, and operating dynamic hybrid computing environments, such as in cloud computing. Some such embodiments include allocating computing resources of a first server cluster to instantiate a first cluster and to establish a computing session. This embodiment may then initiate execution of a program within the first cluster that offloads at least one computing task to a second cluster, when the second cluster is instantiated, to leverage high-computing speed performance capabilities of the second cluster with regard to certain computing operations. Upon completion of program execution, the second cluster is then deallocated.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for instantiating, executing, and operating dynamic hybrid computing environments, such as in cloud computing. Some such embodiments include allocating computing resources of a first server cluster to instantiate a first cluster and to establish a computing session. This embodiment may then initiate execution of a program within the first cluster that offloads at least one computing task to a second cluster, when the second cluster is instantiated, to leverage high-computing speed performance capabilities of the second cluster with regard to certain computing operations. Upon completion of program execution, the second cluster is then deallocated.

Abstract: A platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have well known plug types. The building blocks can be composed build complex compositions. Input and output files are converted to a standard data type so that modules are pluggable.

Abstract: An interoperable platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have few well known plug types. The building blocks can be composed to build complex compositions. Interoperability between data formats, metadata schema and interfaces to machine learning (ML) functions and trained machine learning models can be provided with no loss of information. A cloud runtime environment can be provided in which the composed workflows can be hosted as REST API to run in production.

Abstract: A machine learning problem assessment system that identifies potential machine learning problems in a machine learning system in which learning code evaluates data to correlate estimated learned data with data patterns. An accessing component accesses the learning code and/or the data that the learning code evaluates. A problem identifies component estimates, based on the accessed code and/or data, that there is a potential problem with machine learning system. A rectification component at least partially automatically rectifies the identified potential problem with the machine learning system by performing a computerized action on the machine learning system. The identified potential problem may affect quality (e.g., appropriateness of conclusions) and/or performance (e.g., speed) of the learning of the machine learning system.

Abstract: A sparse dataset structure is created by creating column vectors for one or more columns in a dataset that have at least one significant value. Each column vector includes data values for columns of the dataset. Each column vector that is a sparse column vector includes a look-up index array and a value array. Entries in the look-up index array represent columns. The value array includes values for a row in a column. Each entry in the value array points to a row entry in the look-up index array. A side structure includes a row index and a column index. The row index includes a location for an entry for each row where entries point to a location in the column index that identifies a column that has a first significant entry for a row. Alternatively a sparse dataset could be constructed with sparse rows.

Abstract: Object serialization is used to communicate references regarding shim objects. Shim objects are instantiated on one or more ranks of a distributed software application. The shim objects store a registration object in a distributed object cache for each rank. The registration object includes a unique identifier for a distributed array object and a reference to a local portion of the distributed array. The shim objects are serialized for communication of the stored references from a master rank of the distributed application to one or more worker ranks of the distributed application. Upon serializing the shim objects, the shim object's stored references are communicated from the distributed object cache for that rank to the one or more worker ranks of the distributed application. The shim objects are subsequently removed so that references to the underlying distributed array object are also removed, and memory previously allocated to the unique identifier is recoverable.

Abstract: A platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have well known plug types. The building blocks can be composed build complex compositions. Input and output files are converted to a standard data type so that modules are pluggable.

Abstract: A sparse dataset structure is created by creating column vectors for one or more columns in a dataset that have at least one significant value. Each column vector includes data values for columns of the dataset. Each column vector that is a sparse column vector includes a look-up index array and a value array. Entries in the look-up index array represent columns. The value array includes values for a row in a column. Each entry in the value array points to a row entry in the look-up index array. A side structure includes a row index and a column index. The row index includes a location for an entry for each row where entries point to a location in the column index that identifies a column that has a first significant entry for a row. Alternatively a sparse dataset could be constructed with sparse rows.

Abstract: Computerized singular value decomposition of an input complex matrix. A real-value matrix representation of the input complex matrix is provided to a singular value decomposition module, which correctly obtains a singular value representation of the real-value matrix representation. However, the result is not provided in a form for convenient conversion back into a valid singular value decomposition solution for the original input complex matrix, as the upper left half and lower right half of the diagonal of the diagonal matrix are not identical. A correction module corrects by formulating a corrected diagonal matrix that represents the value of the diagonal of the first diagonal matrix, but shuffled so that the upper left half of the diagonal of the second diagonal matrix is the same as the lower right half of the diagonal of the second diagonal matrix. Corrected unitary matrices may also be formed.

Abstract: A platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have well known plug types. The building blocks can be composed build complex compositions. Input and output files are converted to a standard data type so that modules are pluggable.

Abstract: Embodiments are directed to establishing registration objects for distributed processes, to managing memory on worker processes of a distributed software application and to using object serialization to communicate references to shim objects. In one scenario, a computer system accesses distributed process instances in a distributed runtime and creates a registration object for each of the process instances in the distributed runtime. The registration object includes a key value pair, where the key includes a unique identifier (ID) that identifies a distributed array instance associated with the distributed process, and the value includes a reference to a local portion of the distributed array instance. The computer system then maintains a mapping between the unique ID and the distributed array instance using the registration object. As such, the key value refers to the local portion of the same distributed array instance on each distributed process of the distributed runtime.

Abstract: A sparse dataset structure is created by creating column vectors for one or more columns in a dataset that have at least one significant value. Each column vector includes data values for columns of the dataset. Each column vector that is a sparse column vector includes a look-up index array and a value array. Entries in the look-up index array represent columns. The value array includes values for a row in a column. Each entry in the value array points to a row entry in the look-up index array. A side structure includes a row index and a column index. The row index includes a location for an entry for each row where entries point to a location in the column index that identifies a column that has a first significant entry for a row. Alternatively a sparse dataset could be constructed with sparse rows.

Abstract: Embodiments are directed to establishing registration objects for distributed processes, to managing memory on worker processes of a distributed software application and to using object serialization to communicate references to shim objects. In one scenario, a computer system accesses distributed process instances in a distributed runtime and creates a registration object for each of the process instances in the distributed runtime. The registration object includes a key value pair, where the key includes a unique identifier (ID) that identifies a distributed array instance associated with the distributed process, and the value includes a reference to a local portion of the distributed array instance. The computer system then maintains a mapping between the unique ID and the distributed array instance using the registration object. As such, the key value refers to the local portion of the same distributed array instance on each distributed process of the distributed runtime.

Abstract: Systems and methods to process vehicle operation data are described. A data module associated with a vehicle can collect a set of metrics relating to the operation of the vehicle, as well as events related to an operator's interaction with the vehicle. The data module can correlate the set of metrics with the events to generate a correlated set of data. A user can request various contexts in which to view the data, such as via a vehicle context or an operator context. The data module can generate, using the correlated set of data, a data view according to the request. Further, the correlated set of data and the various contexts can be updated on a real-time basis.

Abstract: An interoperable platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have few well known plug types. The building blocks can be composed to build complex compositions. Interoperability between data formats, metadata schema and interfaces to machine learning (ML) functions and trained machine learning models can be provided with no loss of information. A cloud runtime environment can be provided in which the composed workflows can be hosted as REST API to run in production.

Abstract: A platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have well known plug types. The building blocks can be composed build complex compositions. Input and output files are converted to a standard data type so that modules are pluggable.

Abstract: A method and system for collecting, analyzing and displaying fleet performance data are described. An embodiment of the system and method includes receiving a plurality of data streams comprising disparate data, storing, in real-time, the disparate data in a first database, organizing the disparate data stored in the first database into an organized structure in a second database, the data in the second database organized according to a predefined parameter, exposing the organized data in the second database as a subset of data stored in a third database, querying the subset of data in the third database according to a script, the script configured to associate the subset of data according to the predefined parameter, and providing the associated subset of data in the third database to an analysis engine configured to display the associated subset of data from a perspective defined by the predefined parameter.

Abstract: Implementations relate to systems and methods for processing workflow data and providing location-based and/or entity-based workflow statistics. A processing module or other logic can receive workflow data related to operations of vehicles though a supply chain network comprising a set of locations. The processing module can process the workflow data by generating various context views, filtering out some of the data, and other functions. A user or entity can request the processing module for different views or results of the workflow data to gauge efficiencies or inefficiencies in the supply chain. The user or entity can perform modifications to components of the supply chain based on results of the workflow data.