Abstract: Sparse data handling and/or buffer sharing are implemented. Data may be buffered in reusable buffer arrays. Data may comprise fixed or variable length vectors, which may be represented as sparse or dense vectors in a values array and indices array. Data may be materialized from a dataview comprising a non-materialized view of data in a machine-learning pipeline by cursoring over rows of the dataview and calling delegate functions to compute data for rows in an active column. A buffer and/or its set of arrays storing a first vector may be reused for a second and additional vectors, for example, when the length of buffer arrays is equal to or greater than the length of the second and additional vectors, which may be selectively stored as sparse or dense vectors to fit the array set. Shared buffers may be passed as references between delegate functions for reuse.

Abstract: Methods, systems, and computer products are herein provided for lazy evaluation of input data by a machine learning (ML) framework. An ML pipeline receives input data and compiles a chain of operators into a chain of dataviews configured for lazy evaluation of the input data. Each dataview in the chain represents a computation over data as a non-materialized view of the data. The ML pipeline receives a request for column data and selects a chain of delegates comprising one or more delegates for one or more dataviews in the chain to fulfill the request. The ML pipeline processes the input data with the selected chain of delegates. The ML pipeline performs delegate chaining on a dataview. A feature value for a feature column of the dataview is determined based on the delegate chaining and provided to an ML algorithm to predict column data.

Abstract: Embodiments provide a machine learning framework that enables developers to author and deploy machine learning pipelines into their applications regardless of the programming language in which the applications are structured. The framework may provide a programming language-specific API that enables the application to call a plurality of operators provided by the framework. The framework provides any number of APIs, each for a different programming language. The pipeline generated via the application is represented as an execution graph comprising node(s), where each node represents a particular operator. When a pipeline is submitted for execution, calls to the operators are detected, and nodes corresponding to the operators are generated for the execution graph.

Abstract: An efficient, streaming-based, lazily-evaluated machine learning (ML) framework is provided. An ML pipeline of operators produce and consume a chain of dataviews representing a computation over data. Non-materialized (e.g., virtual) views of data in dataviews permit efficient, lazy evaluation of data on demand regardless of size (e.g., in excess of main memory). Data may be materialized by DataView cursors (e.g., movable windows over rows of an input dataset or DataView). Computation and data movement may be limited to rows for active columns without processing or materializing unnecessary data. A chain of dataviews may comprise a chain of delegates that reference a chain of functions. Assembled pipelines of schematized compositions of operators may be validated and optimized with efficient execution plans. A compiled chain of functions may be optimized and executed in a single call. Dataview based ML pipelines may be developed, trained, evaluated and integrated into applications.

Abstract: Embodiments provide a machine learning framework that enables developers to author and deploy machine learning pipelines into their applications regardless of the programming language in which the applications are structured. The framework may provide a programming language-specific API that enables the application to call a plurality of operators provided by the framework. The framework provides any number of APIs, each for a different programming language. The pipeline generated via the application is represented as an execution graph comprising node(s), where each node represents a particular operator. When a pipeline is submitted for execution, calls to the operators are detected, and nodes corresponding to the operators are generated for the execution graph.

Abstract: Sparse data handling and/or buffer sharing are implemented. Data may be buffered in reusable buffer arrays. Data may comprise fixed or variable length vectors, which may be represented as sparse or dense vectors in a values array and indices array. Data may be materialized from a dataview comprising a non-materialized view of data in a machine-learning pipeline by cursoring over rows of the dataview and calling delegate functions to compute data for rows in an active column. A buffer and/or its set of arrays storing a first vector may be reused for a second and additional vectors, for example, when the length of buffer arrays is equal to or greater than the length of the second and additional vectors, which may be selectively stored as sparse or dense vectors to fit the array set. Shared buffers may be passed as references between delegate functions for reuse.

Abstract: A business intelligence (BI) document preserves references to identities and formats of remote data sources and allows a local computing device to offload analytical operations to remote data sources. The BI document specifies a graph of entities connected by directed edges from the output of one entity to an input of another entity. An entity, for example, can represent without limitation a data structure, an external data source, a control element, an external event source, a visualization, or an update service. The entities of a BI document at a local computing device can reference data at an original data source—rather than extracting data from the original data source to a preferred local datastore. An entity of the BI document can direct a remote data source to execute transformations on the remote data before returning a solution to the local computing device.

Abstract: A business intelligence document provides functionality for testing a hypothesis on aggregated data in a business intelligence document (e.g., a spreadsheet-like document), wherein one or more of the input data values and transformation properties are designated as constrained (e.g., invariant or constrained within a range, set, enumeration, or domain). The hypothesis, which is articulated as a data mining assertion, is input through the user interface of the business intelligence document (e.g., via an expression interface or properties of a row, column, or cell) and solved over the aggregated data. The solution is then presented through the user interface of the spreadsheet-like document, such as in a table, graph, histogram, etc.

Abstract: Techniques for representing and publishing an interactive document useful for analyzing data. The document may be represented as a directed acyclic graph of entities interconnected by edges. The entities may be of multiple types. Yet, a broad range of interactive documents may be represented by a limited number of types of entities and the capabilities to interconnect entities of different types and to share a data schema across entities of different types. A tool may enable a user to author such documents. The tool may also facilitate publishing of the document. For publishing, the document may be converted to an executable form. Prior to such a conversion, the graph may be modified for more efficient processing. The graph may also be partitioned such that portions of the graph, when distributed across tiers of a computing system, such as a cloud-based platform, execute on computing devices that provide efficient operation.

Abstract: Techniques for representing and publishing an interactive document useful for analyzing data. The document may be represented as a directed acyclic graph of entities interconnected by edges. The entities may be of multiple types. Yet, a broad range of interactive documents may be represented by a limited number of types of entities and the capabilities to interconnect entities of different types and to share a data schema across entities of different types. A tool may enable a user to author such documents. The tool may also facilitate publishing of the document. For publishing, the document may be converted to an executable form. Prior to such a conversion, the graph may be modified for more efficient processing. The graph may also be partitioned such that portions of the graph, when distributed across tiers of a computing system, such as a cloud-based platform, execute on computing devices that provide efficient operation.

Abstract: Techniques for representing and publishing an interactive document useful for analyzing data. The document may be represented as a directed acyclic graph of entities interconnected by edges. The entities may be of multiple types. Yet, a broad range of interactive documents may be represented by a limited number of types of entities and the capabilities to interconnect entities of different types and to share a data schema across entities of different types. A tool may enable a user to author such documents. The tool may also facilitate publishing of the document. For publishing, the document may be converted to an executable form. Prior to such a conversion, the graph may be modified for more efficient processing. The graph may also be partitioned such that portions of the graph, when distributed across tiers of a computing system, such as a cloud-based platform, execute on computing devices that provide efficient operation.

Abstract: A business intelligence document provides functionality for testing a hypothesis on aggregated data in a business intelligence document (e.g., a spreadsheet-like document), wherein one or more of the input data values and transformation properties are designated as constrained (e.g., invariant or constrained within a range, set, enumeration, or domain). The hypothesis, which is articulated as a data mining assertion, is input through the user interface of the business intelligence document (e.g., via an expression interface or properties of a row, column, or cell) and solved over the aggregated data. The solution is then presented through the user interface of the spreadsheet-like document, such as in a table, graph, histogram, etc.

Abstract: A business intelligence (BI) document preserves references to identities and formats of remote data sources and allows a local computing device to offload analytical operations to remote data sources. The BI document specifies a graph of entities connected by directed edges from the output of one entity to an input of another entity. An entity, for example, can represent without limitation a data structure, an external data source, a control element, an external event source, a visualization, or an update service. The entities of a BI document at a local computing device can reference data at an original data source—rather than extracting data from the original data source to a preferred local datastore. An entity of the BI document can direct a remote data source to execute transformations on the remote data before returning a solution to the local computing device.

Abstract: A business intelligence document provides functionality for testing a hypothesis on aggregated data in a business intelligence document (e.g., a spreadsheet-like document), wherein one or more of the input data values and transformation properties are designated as constrained (e.g., invariant or constrained within a range, set, enumeration, or domain). The hypothesis, which is articulated as a data mining assertion, is input through the user interface of the business intelligence document (e.g., via an expression interface or properties of a row, column, or cell) and solved over the aggregated data. The solution is then presented through the user interface of the spreadsheet-like document, such as in a table, graph, histogram, etc.

Abstract: A business intelligence (BI) document preserves references to identities and formats of remote data sources and allows a local computing device to offload analytical operations to remote data sources. The BI document specifies a graph of entities connected by directed edges from the output of one entity to an input of another entity. An entity, for example, can represent without limitation a data structure, an external data source, a control element, an external event source, a visualization, or an update service. The entities of a BI document at a local computing device can reference data at an original data source—rather than extracting data from the original data source to a preferred local datastore. An entity of the BI document can direct a remote data source to execute transformations on the remote data before returning a solution to the local computing device.

Abstract: An interactive recommendation system generates one or more recommendations (e.g., recommended products, travel destinations, etc.) for a user based on a recommendation model. The recommendation model includes one or more criteria that are used to analyze a datastore of user characteristics (e.g., a user's age, location, past online behavior, etc.) and generate one or more recommendations based thereon. The interactive recommendation system further presents a user interface that allows the user to interactively modify the criteria of the recommendation model and to apply the modified recommendation model to the datastore in order to generate one or more modified recommendations. In this manner, for example, the user can customize the recommendations he or she receives by interacting with the recommendation system to modify the recommendation model used to generate such recommendations.

Abstract: A compiler supporting a language in which selected semantic objects are represented as data objects. The data objects may be used in multiple ways to expand the capabilities of the programming language. Data objects may be passed to applications and used to create executable instructions for that application. In this way, instructions written in the native language of the compiler may be used to control applications that accept programs in a language inconsistent with the native language of the compiler. The syntax checking and variable binding capabilities of the compiler may be used for those instructions that will be executed by an application separate from the object code generated by the compiler. The semantic objects represented as data objects may be selected based on express operations included in the source code or may be based on implicit type conversion.

Abstract: A business intelligence (BI) document preserves references to identities and formats of remote data sources and allows a local computing device to offload analytical operations to remote data sources. The BI document specifies a graph of entities connected by directed edges from the output of one entity to an input of another entity. An entity, for example, can represent without limitation a data structure, an external data source, a control element, an external event source, a visualization, or an update service. The entities of a BI document at a local computing device can reference data at an original data source—rather than extracting data from the original data source to a preferred local datastore. An entity of the BI document can direct a remote data source to execute transformations on the remote data before returning a solution to the local computing device.

Abstract: Techniques for representing and publishing an interactive document useful for analyzing data. The document may be represented as a directed acyclic graph of entities interconnected by edges. The entities may be of multiple types. Yet, a broad range of interactive documents may be represented by a limited number of types of entities and the capabilities to interconnect entities of different types and to share a data schema across entities of different types. A tool may enable a user to author such documents. The tool may also facilitate publishing of the document. For publishing, the document may be converted to an executable form. Prior to such a conversion, the graph may be modified for more efficient processing. The graph may also be partitioned such that portions of the graph, when distributed across tiers of a computing system, such as a cloud-based platform, execute on computing devices that provide efficient operation.

Abstract: A business intelligence document provides functionality for testing a hypothesis on aggregated data in a business intelligence document (e.g., a spreadsheet-like document), wherein one or more of the input data values and transformation properties are designated as constrained (e.g., invariant or constrained within a range, set, enumeration, or domain). The hypothesis, which is articulated as a data mining assertion, is input through the user interface of the business intelligence document (e.g., via an expression interface or properties of a row, column, or cell) and solved over the aggregated data. The solution is then presented through the user interface of the spreadsheet-like document, such as in a table, graph, histogram, etc.