Abstract: A technology for building and displaying interaction relationships between visual components of a dashboard is provided. In accordance with one aspect, interaction relationships are defined between the components of the dashboard using a data grid. A relationship may be defined by associating a component to a grid cell and defining a formula in the grid cell based on one or more other grid cells which are further associated to one or more other components. In accordance with another aspect, information of a dashboard including dashboard components, input data and output data in the dashboard is converted into dashboard data models. An interaction relationship graph may be generated based on interactions of the input data and output data of the components. The interaction relationship graph comprises source-to-target relationships between source and target components of the dashboard.

Abstract: A system, computer-readable storage medium including instructions, and a computer-implemented method for manipulating one or more objects in a graphical user interface for a display device is presented. A start of a path selection mode is detected. A first path traversed by a cursor in the graphical user interface is detected, with the first path intersecting one or more objects in a plurality of objects displayed in the graphical user interface of the display device. A curve corresponding to the first path is displayed in the graphical user interface. An end of the path selection mode is detected. A selection state of the one or more objects is updated based on the curve, the selection state including a selected state and a deselected state.

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

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: 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: 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: The present disclosure involves systems, software, and computer-implemented methods for preventing overplotting for data visualization. An example method includes identifying a request to generate a chart including a plotting area indicating a set of data points to be displayed in the plotting area of the generated chart; determining that the requested chart will be unsuitable for display; dividing the plotting area into a plurality of bins, each having a shape configured to tessellate the plotting area in response to determining that the requested chart will be unsuitable for display; assigning each data point to one of the plurality of bins based on a position of each data point within the plotting area of the requested chart; and generating a modified chart including the plotting area of the requested chart including the plurality of bins each configured to indicate a number of data points are assigned to the particular bin.

Abstract: On-chart data filtering on computing devices such as, e.g., touch-enabled mobile devices can be enabled by methods, systems, and computer programs that facilitate detecting a filtering gesture performed on an axis element (such as a value range, category label, or category-label row) of a data chart displayed on-screen and, in response to the filtering gesture, filtering data associated with the axis element and updating the displayed data chart based on the filtering.

Abstract: Some examples of visualizing relationships between survey data can be implemented by displaying a user interface including a survey question region and a percentage region. In response to detecting a selection of a survey data set, multiple question objects are displayed in the survey question region. Each question object represents a survey question and an answer option for the survey question. In response to inputs to interact with the multiple question objects, survey results such as a percentage of selections of a first answer option to a first survey question represented by a first question object can be determined and displayed in the user interface. Also, interrelationships between answers to survey questions can be displayed by positioning question objects at different locations in the user interface. In response to a selection of a question object, other question objects can be presented according to the correlation with the selected question object.