Abstract: Techniques are disclosed herein for selecting a predictive model to perform on a set of hierarchical data. A selection of first time series data representing activity observed in a current period in a first hierarchy is received. Second time series data representing activity observed in a prior period in the first hierarchy is retrieved. Predictive models are performed using the second time series data as input, where each predictive model generates statistical outcomes for the current period. A score is generated in each of the plurality of predictive models based on a comparison of the statistical outcomes with the first time series data. Each of the predictive models is ranked based on the generated scores.

Abstract: Techniques are disclosed for generating a visualization of an organized hierarchy structure. An editor tool may receive, via a user interface displaying a graphical representation of a tree structure of a plurality of elements, a selection of an element. The tree structure is generated from a plurality of subtrees based on reference data associated with the underlying organized hierarchy structure. The editor tool identifies one or more hierarchy paths in the tree structure associated with the selected element. A visualization of the identified hierarchy paths is generated and presented via the user interface.

Abstract: Techniques are disclosed herein for selecting a predictive model to perform on a set of hierarchical data. A selection of first time series data representing activity observed in a current period in a first hierarchy is received. Second time series data representing activity observed in a prior period in the first hierarchy is retrieved. Predictive models are performed using the second time series data as input, where each predictive model generates statistical outcomes for the current period. A score is generated in each of the plurality of predictive models based on a comparison of the statistical outcomes with the first time series data. Each of the predictive models is ranked based on the generated scores.

Abstract: Techniques are disclosed for comparing data between dissimilar data hierarchies. Techniques provide an entity pool comprising multiple entities having established relationships and hierarchies. A user selects data from one hierarchy, and a mapping to a node in a structure that provides a normalized hierarchy is found. After identifying a node mapped to the data selection, elements corresponding to a second hierarchy that also maps to the same node (or otherwise obtained from using known natural language processing techniques) are identified. Doing so allows comparable elements of otherwise dissimilar hierarchies to be identified.

Abstract: Techniques are disclosed for generating a visualization of an organized hierarchy structure. An editor tool may receive, via a user interface displaying a graphical representation of a tree structure of a plurality of elements, a selection of an element. The tree structure is generated from a plurality of subtrees based on reference data associated with the underlying organized hierarchy structure. The editor tool identifies one or more hierarchy paths in the tree structure associated with the selected element. A visualization of the identified hierarchy paths is generated and presented via the user interface.

Abstract: Techniques are disclosed for generating an organized hierarchy from a set of related data. A request is received to generate an organized hierarchy from a data set. The data set includes labels and contextual cues associated with each of the of labels. For each label, one or more candidate labels are identified based on a probabilistic model generated from a plurality of known ontological hierarchies. The label is matched and assigned to one of the candidate labels. Hierarchy paths associated with the assigned candidate labels are joined with one another to build the organized hierarchy.

Abstract: Embodiments presented herein disclose techniques for generating an entity pool, a hierarchical structure of related nodes that assists with classification and comparison of dissimilar data sets. To generate the entity pool, text references and metadata are collected from a public source, such as an online encyclopedia or other text source that provides dense and structured data that focuses on identified terminology. The text references are assigned similarity scores based on contextual information provided by the metadata. The text references are clustered into nodes based on similarity. Relationships between the nodes are defined based on edges generated between the nodes.

Abstract: Techniques are disclosed for comparing data between dissimilar data hierarchies. Techniques provide an entity pool comprising multiple entities having established relationships and hierarchies. A user selects data from one hierarchy, and a mapping to a node in a structure that provides a normalized hierarchy is found. After identifying a node mapped to the data selection, elements corresponding to a second hierarchy that also maps to the same node (or otherwise obtained from using known natural language processing techniques) are identified. Doing so allows comparable elements of otherwise dissimilar hierarchies to be identified.