Abstract: In an approach for transforming workflows based on goals and constraints, a processor receives a workflow definition and one or more events for a workflow output by a workflow orchestrator, wherein the one or more events are at least one of a parsing event and a scheduling event. A processor determines, using one or more transformers, one or more transformations to the workflow based on a set of goals and constraints, wherein the one or more transformations is at least one of addition of a new node, addition of a new graph, modification of at least one existing node, modification of at least one existing graph, removal of at least one existing node, and removal of at least one existing graph. A processor dynamically applies, using one or more enforcers, the one or more transformations to the workflow based on the set of goals and constraints.

Abstract: Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically updated with one or more changes to a codebase.

Abstract: Various embodiments are provided for dynamically factoring and composing workflows in a computing environment by one or more processors in a computing system. Subgraphs (e.g., blocks) of workflows stored in a workflow library may be identified. The subgraphs may be functional blocks such as, for example, the functional blocks may perform a logical task. Similarities and relationships may be identified between one or more of the blocks of one or more workflows. One or more blocks may be suggested for use in workflow opportunities of target workflows based on the identified associated similarities and relationships.

Abstract: In an approach for storage, search, acquisition, and composition of a digital artifact, a processor obtains the digital artifact in a digital marketplace platform. The digital artifact is a collection of digital data with automatically generated and verifiable provenance and usage data. A processor transforms the digital artifact to define an access privilege. A processor shares the digital artifact in the digital marketplace platform by providing a view of a catalogue including the digital artifact. A processor authorizes a usage request based on the access privilege. A processor rewards a source of the digital artifact based on the usage of the digital artifact.

Abstract: A computer implemented method for optimizing performance of workflow includes associating each of a plurality of workflow nodes in a workflow with a data cache and managing the data cache on a local storage device on one of one or more compute nodes. A scheduler can request execution of the tasks of a given one of the plurality of workflow nodes on one of the one of more compute nodes that hosts the data cache associated with the given one of the plurality of workflow nodes. Each of the plurality of workflow nodes is permitted to access a distributed filesystem that is visible to each of the plurality of compute nodes. The data cache stores data produced by the tasks of the given one of the plurality of workflow nodes.

Abstract: A computer-implemented method and system for facilitating processing of project data is provided. An electronic project document is associated with one or more document sources. The one or more content segments are analyzed to identify entities of interest comprised in the one or more content segments. Aggregated information is retrieved from a data source. The aggregated information is associated with one or more of the identified entities of interest and the one or more document sources. In response to receiving the user input selecting at least a first content segment of the one or more content segments, a displayed user interface is modified to display, in a first panel, the selected at least a first content segment and to concurrently display, in a second panel, retrieved aggregated information associated with a first entity of interest comprised in the first selected content segment.

Abstract: Embodiments are provided for providing workflow pipeline optimization in a computing environment. Execution of a workflow containing dependencies between one or more subject nodes and one or more observer nodes may be dynamically optimized by determining a wait time between successive executions of the workflow for the one or more observer nodes.

Abstract: Workflow memoization can include generating an embedding associated with a node in a workflow. The embedding can be generated by encoding at least the node's executable and input data to the node. A matching embedding can be retrieved from a database of embeddings, which matches the generated embedding according to a match criterion. The database of embeddings can store embeddings associated with previously run nodes. Output data associated with the matching embedding can be retrieved. The output data can be used as the node's output without having to run the node in the workflow.

Abstract: A computer implemented method for optimizing performance of workflow includes associating each of a plurality of workflow nodes in a workflow with a data cache and managing the data cache on a local storage device on one of one or more compute nodes. A scheduler can request execution of the tasks of a given one of the plurality of workflow nodes on one of the one of more compute nodes that hosts the data cache associated with the given one of the plurality of workflow nodes. Each of the plurality of workflow nodes is permitted to access a distributed filesystem that is visible to each of the plurality of compute nodes. The data cache stores data produced by the tasks of the given one of the plurality of workflow nodes.

Abstract: A computer implemented method for automatically creating and executing a test harness for a workflow includes creating a workflow package including a version identification and a workflow graph definition and creating a workflow instance as a copy of the workflow package. The workflow instance is executed to generate output data and the output data is saved into the workflow instance. The workflow instance is saved, with the output data, as a test archive. One or more components of an updated workflow package are tested with the test archive; and an indication of pass or fail of the testing of the updated workflow package is outputted.

Abstract: Two sets X2 and X1 of histograms of words, and a vocabulary V are accessed. Each of the two sets is representable as a sparse matrix, each row of which corresponds to a histogram. Each histogram is representable as a sparse vector, whose dimension is determined by a dimension of the vocabulary. Two phases compute distances between pairs of histograms. The first phase includes computations performed for each histogram and for each word in the vocabulary to obtain a dense, floating-point vector y. The second phase includes computing, for each histogram, a sparse-matrix, dense-vector multiplication between a matrix-representation of the set X1 of histograms and the vector y. The multiplication is performed to obtain distances between all histograms of the set X1 and each histogram X2[j]. Distances between all pairs of histograms are obtained, based on which distances between documents can subsequently be assessed.

Abstract: Embodiments are provided for providing workflow pipeline optimization in a computing environment. Execution of a workflow containing dependencies between one or more subject nodes and one or more observer nodes may be dynamically optimized by determining a wait time between successive executions of the workflow for the one or more observer nodes.

Abstract: Two sets X2 and X1 of histograms of words, and a vocabulary V are accessed. Each of the two sets is representable as a sparse matrix, each row of which corresponds to a histogram. Each histogram is representable as a sparse vector, whose dimension is determined by a dimension of the vocabulary. Two phases compute distances between pairs of histograms. The first phase includes computations performed for each histogram and for each word in the vocabulary to obtain a dense, floating-point vector y. The second phase includes computing, for each histogram, a sparse-matrix, dense-vector multiplication between a matrix-representation of the set X1 of histograms and the vector y. The multiplication is performed to obtain distances between all histograms of the set X1 and each histogram X2[j]. Distances between all pairs of histograms are obtained, based on which distances between documents can subsequently be assessed.

Abstract: Methods and apparatus are provided to determine entities and attributes dependencies for creating recommendations of items or entities using a highly scalable architecture. For example, a user may be recommended an item if a probability model of the method determines that the user relates to the item although the user has no contact to the item before the method is performed. The methods and apparatus provide a data structure representing a matrix having rows representing entities and columns representing attributes of the entities. Each entity of the entities of the data structure may include a user and each attribute of the attributes of the data structure may include an item. A cell of the matrix may be formed by a component pair including an entity and an attribute. In this manner, the methods and apparatus provide an efficient way for processing the probability model.

Abstract: Methods and apparatus are provided to determine entities and attributes dependencies for creating recommendations of items or entities using a highly scalable architecture. For example, a user may be recommended an item if a probability model of the method determines that the user relates to the item although the user has no contact to the item before the method is performed. The methods and apparatus provide a data structure representing a matrix having rows representing entities and columns representing attributes of the entities. Each entity of the entities of the data structure may include a user and each attribute of the attributes of the data structure may include an item. A cell of the matrix may be formed by a component pair including an entity and an attribute. In this manner, the methods and apparatus provide an efficient way for processing the probability model.

Abstract: Embodiments include processing a data structure representing a dependency matrix having columns representing respective first components and rows representing respective second components. Aspects include assigning each cell of the matrix a value indicative of the level of dependency or indicative of an unknown dependency of a pair of first and second components forming the cell and assigning each component of the first and second components an affiliation vector indicative of the strength of affiliation of the component to N predefined initial clusters of cells of the matrix. Aspects also include determining a probability model using the affiliations vectors parameters and estimating the parameters of the probability model for a plurality of different numbers of clusters starting from the initial number N of clusters. Aspects further include computing a score for the parameters of the probability model estimated and selecting the parameters of the probability model with the highest computed score.

Abstract: Embodiments include processing a data structure representing a dependency matrix having columns representing respective first components and rows representing respective second components. Aspects include assigning each cell of the matrix a value indicative of the level of dependency or indicative of an unknown dependency of a pair of first and second components forming the cell and assigning each component of the first and second components an affiliation vector indicative of the strength of affiliation of the component to N predefined initial clusters of cells of the matrix. Aspects also include determining a probability model using the affiliations vectors parameters and estimating the parameters of the probability model for a plurality of different numbers of clusters starting from the initial number N of clusters. Aspects further include computing a score for the parameters of the probability model estimated and selecting the parameters of the probability model with the highest computed score.

Abstract: Embodiments include processing a data structure representing a dependency matrix having columns representing respective first components and rows representing respective second components. Aspects include assigning each cell of the matrix a value indicative of the level of dependency or indicative of an unknown dependency of a pair of first and second components forming the cell and assigning each component of the first and second components an affiliation vector indicative of the strength of affiliation of the component to N predefined initial clusters of cells of the matrix. Aspects also include determining a probability model using the affiliations vectors parameters and estimating the parameters of the probability model for a plurality of different numbers of clusters starting from the initial number N of clusters. Aspects further include computing a score for the parameters of the probability model estimated and selecting the parameters of the probability model with the highest computed score.