Abstract: A size reducer for tabular data models. As the tabular data model is being created, the size reducer evaluates one or more columns of the tabular data model. For a given column, the memory burden and data type of the column are determined. Based on this information, the size reducer automatically determines at least one modification that can be made to the column (as compared to the source column at the data source) in order to reduce the size of the column's burden in the tabular data model. Example modifications might include splitting of column as compared to its source column in the data source, removing information (e.g., rounding) from a column as compared to its source column, and even eliminating columns from the tabular data model that are present in the external data source.

Abstract: In some implementations, sensors provide sensor data reflecting user activity detected by the sensors. An event analyzer generates an impact score for a change to an event associated with a user based on routine-related aspects generated from one or more user routine models associated with the user. The one or more user routine models are trained based at least in part on interaction data comprised of the sensor data. The impact score may be generated by analyzing the event attributes with respect to the routine-related aspects. The impact score is generated based on determining a difference in a level of deviation caused by the change, between one or more event attributes and routine-related aspects and based on comparing a time of the event to a reference time. The impact score can be used to determine which changes to events are important to the user.

Abstract: In some implementations, sensors provide sensor data reflecting user activity detected by the sensors. An event analyzer generates an unusualness score for an event associated with a user based on routine-related aspects generated from one or more user routine models associated with the user. The one or more user routine models are trained based at least in part on interaction data comprised of the sensor data. Event attributes of the event can be received that include a time of the event and attendees of the event. The unusualness score may be generated by analyzing the event attributes with respect to the routine-related aspects. The unusualness score is generated to quantify a level of deviation between the event attributes and the routine-related aspects. Service content can be generated for the user based at least in part on the unusualness score generated for the event.

Abstract: A size reducer for tabular data models. After the tabular data model is being created, the size reducer evaluates one or more columns of the tabular data model. For a given column, the data type of the column is determined. Based on this information, the size reducer automatically determines at least one modification that can be made to the column (as compared to the source column at the data source) in order to reduce the size of the column's burden in the tabular data model. Example modifications might include splitting of column as compared to its source column in the data source, removing information (e.g., rounding) from a column as compared to its source column, and even eliminating columns from the tabular data model that are present in the external data source.

Abstract: A size reducer for tabular data models. As the tabular data model is being created, the size reducer evaluates one or more columns of the tabular data model. For a given column, the memory burden and data type of the column are determined Based on this information, the size reducer automatically determines at least one modification that can be made to the column (as compared to the source column at the data source) in order to reduce the size of the column's burden in the tabular data model. Example modifications might include splitting of column as compared to its source column in the data source, removing information (e.g., rounding) from a column as compared to its source column, and even eliminating columns from the tabular data model that are present in the external data source.