Abstract: Generating sets of data from the columnar array for configuring guided navigation pages that include actionable elements that correspond to one or more dimension of the set of dimensions.

Abstract: Detecting a source business activity of an outlier of a business performance measure that falls outside of a statistical analysis-computed outlier boundary includes identifying the source business activity based on an impact rating indicating a likelihood that a business activity contributes a value to the computation of the outlier for a given dimension of business performance data representative of business activities.

Abstract: Automated discovery of dimensions of a data set that are relevant to continuing a plurality of business workflows may include generating guided navigation pages from data sources representing a plurality of domains organized by a set of dimensions for continuing the plurality of business workflows. The pages include actionable elements that correspond to one or more dimension of the set of dimensions. Each dimension of the set of dimensions is common across at least two domains of the plurality of domains. At least one dynamic dimension may be discovered based on an aspect of use of the dimensions for generating the guided navigation pages. In response to interaction by a user with an actionable element that represents the dynamic domain, a semantic request response play may be used to generate a set of page data for a new guided navigation page representing the dynamic dimension.

Abstract: Detecting a source business activity of an outlier of a business performance measure that falls outside of a statistical analysis-computed outlier boundary includes identifying the source business activity based on an impact rating indicating a likelihood that a business activity contributes a value to the computation of the outlier for a given dimension of business performance data representative of business activities.

Abstract: Automated guided navigation page generation across a plurality of devices in a network includes receiving a named request associated with a first guided navigation page from a computing device at a server, evaluating freshness for a plurality of freshness tokens referenced in a request response data structure that stores a plurality of freshness tokens for each named request, responsively generating named request response data as one or more of an updated columnar data structure and an updated set of cached data, an updated set of cache data, or a response to the request using the set of cached data, and responding to the computing device with the automatically generated named request response data to automatically generate the first guided navigation page.

Abstract: Providing user-controllable visualization of an impact of each of a set of dimensions for a set of data from which at least one outlier value is detected includes computing an outlier boundary for a dimension data value via statistical analysis for data organized as assisted analytics time frame data sets. Further a dimension data value outlier factor may be produced for a detected outlier based on a weighting associated with an assisted analytics time frame that corresponds to the outlier. Dimension outlier factors may be derived therefrom and mapped to a corresponding dimension impact rating value for the user-controllable visualization.

Abstract: Automated guided navigation page generation across a plurality of devices in a network includes receiving a named request associated with a first guided navigation page from a computing device at a server, evaluating freshness for a plurality of freshness tokens referenced in a request response data structure that stores a plurality of freshness tokens for each named request, responsively generating named request response data as one or more of an updated columnar data structure and an updated set of cached data, an updated set of cache data, or a response to the request using the set of cached data, and responding to the computing device with the automatically generated named request response data to automatically generate the first guided navigation page.

Abstract: Providing user-controllable visualization of an impact of each of a set of dimensions for a set of data from which at least one outlier value is detected includes computing an outlier boundary for a dimension data value via statistical analysis for data organized as assisted analytics time frame data sets. Further a dimension data value outlier factor may be produced for a detected outlier based on a weighting associated with an assisted analytics time frame that corresponds to the outlier. Dimension outlier factors may be derived therefrom and mapped to a corresponding dimension impact rating value for the user-controllable visualization.

Abstract: A computer-implemented method includes accessing data from distinct data domains corresponding to different data sources, and determining a core dimension of the accessed data common to first and second distinct data domains. The method further includes generating a data set from the data sources using a columnar data generation engine, and deriving, from the data set via stateless processing, a first guided page including actionable elements depicting data derived from the first distinct data domain. The method further includes responsive to a user interaction with one of the actionable elements, providing, dependent on the common core dimension, direct navigation from the first to the second distinct data domain, and predicting, from the data set based on machine learning and prior to the user interaction, a second guided page that depicts data derived from the second distinct data domain and is presented responsive to the direct navigation.

Abstract: Assisted analytics, facilitates responding to a user selection of a measure that is calculated from a data set that is characterized by a plurality of dimensions of data, populating, with a processor a set of dimensions of the data with dimensions that contribute at least one data value to the user selected measure by calculating, for each dimension of the data in the set of dimensions of data a measure outlier threshold for a set of timeframe-specific values of the measure. This outlier threshold is applied, for each dimension of the data in the set of dimensions of data to calculate a dimension-specific outlier factor by aggregating timeframe-specific outlier weights for each timeframe in which a timeframe-specific value in the set of timeframe-specific values exceeds the measure outlier threshold. The results of this aggregation can be displayed in a ranked list of dimensions based on the dimension-specific outlier factor.

Abstract: A computer-implemented method includes accessing data from distinct data domains corresponding to different data sources, and determining a core dimension of the accessed data common to first and second distinct data domains. The method further includes generating a data set from the data sources using a columnar data generation engine, and deriving, from the data set via stateless processing, a first guided page including actionable elements depicting data derived from the first distinct data domain. The method further includes responsive to a user interaction with one of the actionable elements, providing, dependent on the common core dimension, direct navigation from the first to the second distinct data domain, and predicting, from the data set based on machine learning and prior to the user interaction, a second guided page that depicts data derived from the second distinct data domain and is presented responsive to the direct navigation.

Abstract: A computer-implemented method includes generating, via a multi-user business collaboration platform, at least one guided navigation page based at least in part on data used to measure a goal associated with a workflow of a business. The method further includes facilitating direct navigation among a plurality of data domains in response to a user selection of one of a plurality of displayable actionable elements each associated with at least one core dimension of information predetermined to be pertinent to the continuation of the workflow of the business. The method further includes applying goal attainment algorithms to at least a portion of the data and recommending a candidate collaboration project in a user interface of the multi-user business collaboration platform. The candidate collaboration project includes team members selected from a list of team members associated with the workflow for the business.

Abstract: A measure factory for generating analytic measures includes data sets representing business activities arranged as columnar arrays with each column being associated with a distinct source rule that applies to the column when it is used as a data source. The measure factory includes factory rules that govern which operations on available data sources may be executed under what conditions in the measure factory, such as by taking into account the source rules and other applicable factory rules. A factory rule execution hierarchy governs the execution of ready factory rules that lack dependency on other factory rules before executing ready factory rules that have dependency on other factory rules. A script generation facility generates a script to process the plurality of factory rules according to the factory rule execution hierarchy.

Abstract: A computer-implemented method includes accessing data from distinct data domains corresponding to different data sources, and determining a core dimension of the accessed data common to first and second distinct data domains. The method further includes generating a data set from the data sources using a columnar data generation engine, and deriving, from the data set via stateless processing, a first guided page including actionable elements depicting data derived from the first distinct data domain. The method further includes responsive to a user interaction with one of the actionable elements, providing, dependent on the common core dimension, direct navigation from the first to the second distinct data domain, and predicting, from the data set based on machine learning and prior to the user interaction, a second guided page that depicts data derived from the second distinct data domain and is presented responsive to the direct navigation.

Abstract: Assisted analytics, facilitates responding to a user selection of a measure that is calculated from a data set that is characterized by a plurality of dimensions of data, populating, with a processor a set of dimensions of the data with dimensions that contribute at least one data value to the user selected measure by calculating, for each dimension of the data in the set of dimensions of data a measure outlier threshold for a set of timeframe-specific values of the measure. This outlier threshold is applied, for each dimension of the data in the set of dimensions of data to calculate a dimension-specific outlier factor by aggregating timeframe-specific outlier weights for each timeframe in which a timeframe-specific value in the set of timeframe-specific values exceeds the measure outlier threshold. The results of this aggregation can be displayed in a ranked list of dimensions based on the dimension-specific outlier factor.

Abstract: A measure factory for generating analytic measures includes data sets representing business activities arranged as columnar arrays with each column being associated with a distinct source rule that applies to the column when it is used as a data source. The measure factory includes factory rules that govern which operations on available data sources may be executed under what conditions in the measure factory, such as by taking into account the source rules and other applicable factory rules. A factory rule execution hierarchy governs the execution of ready factory rules that lack dependency on other factory rules before executing ready factory rules that have dependency on other factory rules. A script generation facility generates a script to process the plurality of factory rules according to the factory rule execution hierarchy.

Abstract: Assisted analytics, facilitates responding to a user selection of a measure that is calculated from a data set that is characterized by a plurality of dimensions of data, populating, with a processor a set of dimensions of the data with dimensions that contribute at least one data value to the user selected measure by calculating, for each dimension of the data in the set of dimensions of data a measure outlier threshold for a set of timeframe-specific values of the measure. This outlier threshold is applied, for each dimension of the data in the set of dimensions of data to calculate a dimension-specific outlier factor by aggregating timeframe-specific outlier weights for each timeframe in which a timeframe-specific value in the set of timeframe-specific values exceeds the measure outlier threshold. The results of this aggregation can be displayed in a ranked list of dimensions based on the dimension-specific outlier factor.

Abstract: A measure factory for generating analytic measures includes data sets representing business activities arranged as columnar arrays with each column being associated with a distinct source rule that applies to the column when it is used as a data source. The measure factory includes factory rules that govern which operations on available data sources may be executed under what conditions in the measure factory, such as by taking into account the source rules and other applicable factory rules. A factory rule execution hierarchy governs the execution of ready factory rules that lack dependency on other factory rules before executing ready factory rules that have dependency on other factory rules. A script generation facility generates a script to process the plurality of factory rules according to the factory rule execution hierarchy.