Abstract: A method and system are presented of automatically suggesting rules for data stored in a table, with the table comprising a plurality of columns. The table is profiled to identify a content type for each of one or more of the plurality of columns. A rule knowledge base is accessed to locate rules specified for identified content types. Then, one or more of the located rules specified for identified content types are presented as suggestions. Acceptance of one or more of the suggested rules is received from a user, and the received validations are stored in the rule knowledge base. The accepted rules are applied to data for quality detection and monitoring. Embodiments are also described where columns are suggested based on a given rule.

Abstract: Examples of auto-monitoring and adjusting dynamic data visualizations are provided herein. A data visualization based on initial data can be generated. A series of data updates can be received. The data visualization can be updated based on the series of data updates. Various performance metrics can be monitored, and data updates and/or the updated data visualization can be adjusted accordingly. Performance metrics can include at least one of: a data visualization rendering time; a data transfer time; or a data update generation time. Upon determining that one or more performance metrics exceed a threshold: a time between data updates of the series of data updates can be increased; sampled data can be requested for subsequent data updates; and/or a time-dimension extent of the updated data visualization can be reduced.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program executable by at least one processing unit of a first device. The program establishes a collaboration session with a second device. The program also receives, from the second device, analytics data shared through the collaboration session and contextual data associated with the analytics data. The program further provides additional data for display on the first device based on the contextual data associated with the analytics data.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems, including a method for generating executable components. One method includes identifying a user request to create a new function based pre-existing algorithms, the new function to be used in an application used by a user; providing a set of available algorithms from an algorithm library; receiving a selection by a user of an algorithm from the available algorithms; providing a set of available parameters associated with the selected algorithm; receiving an election by the user of one or more parameters from the set of available parameters; generating an executable component in response to receiving the selection of the algorithm and the election of the one or more parameters, the executable component performing the selected algorithm using at least the elected one or more parameters; and storing the executable component for subsequent execution in response to the requested new function.

Abstract: Techniques are described for providing a unified client to interact with a distributed processing platform such as a Hadoop cluster. The unified client may include multiple sub-clients each of which is configured to interface with a particular subsystem of the distributed processing platform, such as MapReduce, Hive, Spark, and so forth. The unified client may be included in an application to provide, for the application, a single interface for communications between the application and the distributed processing platform during a unified communication session.

Abstract: A computer-implemented method for generating a plurality of data suggestions is described. The method includes receiving a textual input in a user interface of a software application implementing a plurality of business processes, determining a query context associated with the textual input, a user, and data that the user is viewing in the software application, and computing a plurality of statistical metrics for the query context, the statistical metrics being computed using information obtained from datasets associated with the query context. The method also includes determining a plurality of candidate data combinations, the data combinations including a plurality of dimensions, measures, and filters compatible with the query context, ranking the plurality of candidate data combinations according to one of the plurality of statistical metrics, and generating at least one data suggestion using the plurality of candidate data combinations and providing the at least one data suggestion in the user interface.

Abstract: Embodiments relate to techniques for performing data blending operations across multiple different data sets comprising data structures with columns and rows. The data sets may be classified and displayed in a visualization (i.e., chart) in a client interface. Columns and rows from the blended data sets may be mapped together (i.e., linked). Updates to the visualization, including adding elements from the data sets, may trigger a data blending process on the backend server in communication with a database. The server may blend the specified data by generating a runtime artifact representing a calculation graph for the blend operation and query the database to retrieve a resulting data set. The data blending operation may comprise collapsing dimensions of a primary data set with linked dimensions of a secondary data sets into a blended column and aggregating values of measures in rows of the blended column of the resulting data structure.

Abstract: The present disclosure includes techniques pertaining to computer implemented systems and methods for processing semi-structured data. In one embodiment, semi-structured data in a data source is analyzed and structural information about the data is generated. The semi-structured documents may comprise a plurality of fields having associated values. Documents may be analyzed to determine particular fields in the plurality of semi-structured documents. Statistics may be generated to determine, for example, a frequency that each particular field appears in the plurality of semi-structured documents. Particular fields and associated frequencies and other statistics may be stored in a memory, for example.

Abstract: A raw dataset including measures and dimensions is processed, by a preprocessing module, using an algorithm that produces a preprocessed dataset such that at least one type of statistical analysis of the preprocessed dataset yields equal results to the same type of statistical analysis of the raw dataset. The preprocessed dataset is then analyzed by a statistical analysis module to identify subsets of the preprocessed dataset that include a non-random structure or pattern. The analysis of the preprocessed dataset includes the at least one type of statistical analysis that produces the same results for both the preprocessed and raw datasets. The identified subsets are then ranked by a statistical ranker based on the analysis of the preprocessed dataset and a subset is selected for visualization based on the rankings. A visualization module then generates a visualization of the selected identified subset that highlights a non-random structure of the selected subset.

Abstract: Techniques are described for performing a time series analysis using a clustering based symbolic representation. Implementations employ a clustering based symbolic representation applied to time series data. In some implementations, the time series data is discretized into subsequences with regular time intervals, and symbols encoding the time intervals may be derived by performing clustering algorithms on the subsequences. In the new representation, a time series is transformed into a sequence of categorical values. The symbolic representation is suitable to perform time series classification and forecast with higher accuracy and greater efficiency compared to previously used techniques. Through use of the symbolic representation, a dimension reduction is applied to transform the time sequences to a feature space with lower dimensions. As output of such transformation, a new representation is obtained based on the original time series.

Abstract: Embodiments relate to performing data blending operations across multiple different data sets comprising data structures with columns and rows. Columns of data sets to be blended may be linked together. Filters may be applied to data sets before the data blend operation is performed to specify which columns to be displayed in a visualization at a client interface. A direct filter may be applied to one of the data sets to obtain a filtered resulting data set. Data elements of the filtered resulting data set can be identified that correspond to the linked columns of the data sets to be blended. The results of applying the direct filter may then be used as the filtering criteria for an indirect filter to filter a second data set. The results of applying the direct and indirect filters may then be combined together in the data blending operation.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program executable by at least one processing unit of a first device. The program generates, at the first device, a snapshot of a digital content page being presented by a second device. The program also provides a set of annotation tools for annotating the snapshot of the digital content page. The program further adds a set of annotations to the snapshot of the digital content page using the set of annotation tools.

Abstract: The present disclosure describes methods, systems, and computer program products for enabling advanced analytics with large datasets.

Abstract: In one embodiment, a method receives a query for data in a database system and calls a plurality of engines to analyze information for the query. A calculation graph is generated from at least a portion of the plurality of engines where each of the at least a portion of the plurality of engines add a node to the calculation graph based on analyzing of the information for the query. Then, the method executes the query by calling the nodes of the calculation graph. Each node uses metadata added to the node from a respective calculation engine to perform a calculation for the node. Then, a result of the query is output based on the respective calculations performed by the nodes.

Abstract: Embodiments relate to transforming data views for analysis and/or collaboration purposes. An original data view is displayed on a document page in response to a database query. A filter operation is performed by selecting a specific subset of that original data view. A copy of that transformed data view portion including a tuple, is displayed in a separate analysis space (e.g., on the same document page, on another page, or within a collaboration panel). In one example, a user clicks/drags a subset of a table data view for transformation and copying. When the selected and transformed data view portion is copied to the analysis space, relevant associated context information (e.g., the underlying query, measures, dimensions, chart legends, formatting) may be retained for later access. To conserve the limited space typically available to a collaboration panel, certain embodiments may simplify presentation by reproducing only the characters of the transformed data portion.

Abstract: Embodiments provide for querying and visualization of query results of graph data. An engine processes graph data to create metadata (e.g., in JSON format) identifying at least different node types and edge types that are present in a graph database. An overview visualization simplifies presentation of graph data by depicting only various different node types and graph types. The overview visualization may form the basis for formulating queries including the metadata, which are then promulgated to the graph database. Returned query results may be visualized as an overview or on a detailed node level, promoting insight and formulation of additional queries including node/edge type metadata. The engine may convert graph data query results into tabular form for consumption by relational database analytical tools. According to particular embodiments, an engine of an in-memory database may be particularly suited to perform graph data visualization, querying, and/or tabular conversion tasks.

Abstract: Data blending operations are disclosed configured to blend data from a primary data set and a secondary data set. Primary and secondary data structure models are generated reflecting selected dimensions and measures of the primary and secondary data structure models upon which the data blending operation is to be performed. Each dimension and measure in the primary and secondary data structure models includes a local identifier. Unique global identifiers are assigned to each local identifier of the primary and secondary data structure models. Client queries are generated and communicated to a backend server to perform data blending on the selected dimensions and measures of the primary data set and the secondary data set. The client queries may comprise the global identifiers and mapping information between the global identifiers and local identifiers. The backend system retrieves a resulting data set in response to the data blending operation based on this mapping.

Abstract: The present disclosure includes techniques pertaining to computer implemented systems and methods for processing semi-structured data. In one embodiment, semi-structured data in a data source is analyzed and structural information about the data is generated. Structural information may be accessed and used to generate a visual representation of the document structures. User selections may be received of particular fields to be included in an output data set. Data for selected fields may be stored in the output data set as a two-dimensional data structure.

Abstract: A computer-implemented method for rendering a document on any size device includes, during runtime, rendering a document for display on a device, where the document includes multiple elements arranged in one or more rows and one or more columns. The method includes if a size of the device is greater than a threshold size, scaling the document as a whole to fit the size of the device. If the size of the device is less than the threshold size, scaling the document by stacking the columns containing elements into a single column, removing empty space from the single column, and scaling the elements in the stacked single column.

Abstract: An improved model-based approach for undoing actions in an application that was not previously configured with an undo feature is disclosed. Object models are constructed for each object invoked by the application. Snapshots of the object model are captured after every action to preserve the object model state at different points in time. The object model includes an object tree data structure having multiple nodes comprising data and metadata for the object. The object model is frozen and editing of the object is only permitted via an undo management engine. In response to edits from the application, the undo management engine responds by unfreezing the path of object nodes from leaf node to root node in the object tree data structure. Edits are applied to the object model at the leaf node. The object model can then be re-frozen to maintain the state of the object after each action.