Abstract: Processing gesture input includes obtaining hand tracking data based on a set of camera frames, determining a hand pose based on the hand tracking data, and determining an intentionality classification for a gesture based on the hand pose. An input action corresponding to the gesture is enabled based on the hand pose and the intentionality classification. An occlusion classification is determined for the hand based on the hand pose and the input gesture can be determined based on the occlusion classification.

Abstract: Various implementations disclosed herein include devices, systems, and methods that detect and interpret a user activity using a resource-heavy process that is triggered or guided by determinations made by a resource-light process. For example, a method may include performing a first process to produce an output. The first process may include detecting events based on a first set of sensor data; identifying a subset of the events as human-relevant events corresponding to one or more predetermined classes depicted in the first set of sensor data; and collecting information regarding the human-relevant events based on the first set of sensor data. Based on the output of the first process, a second process may be performed to interpret a user activity. The second process may include obtaining a second set of sensor data and interpreting the user activity using the second set of sensor data.

Abstract: Enabling gesture input includes obtaining hand tracking data based on one or more camera frames, detecting a contact event between a first finger and a second finger based on the hand tracking data, and determining a first contact location on the first finger and a second contact location on the second finger. In accordance with a determination that the first contact location and the second contact location are within a first predefined gesture zone for a first gesture, an input action is enabled corresponding to the first gesture.

Abstract: Enabling gesture input includes obtaining hand tracking data based on one or more camera frames, detecting a contact event between a first finger and a second finger based on the hand tracking data, and determining a first contact location on the first finger and a second contact location on the second finger. In accordance with a determination that the first contact location and the second contact location are within a first predefined gesture zone for a first gesture, an input action is enabled corresponding to the first gesture.

Abstract: In accordance with an embodiment, described herein is a system and method for use of a dynamic flow process (referred to herein in some examples as Query Processing Dynamic Flow (QPDF)) in a multidimensional database computing environment. The dynamic flow process enables hybrid use of, for example, aggregate storage option, block storage option, or other types of storage containers, and provides a common flow to process a received input query in bottom-up mode. The approach can be used to reduce the size of the cube, which provides for efficient calculation of dynamic members.

Abstract: Enabling gesture input includes obtaining hand tracking data based on one or more camera frames, detecting a contact event between a first finger and a second finger based on the hand tracking data, and determining a first contact location on the first finger and a second contact location on the second finger. In accordance with a determination that the first contact location and the second contact location are within a first predefined gesture zone for a first gesture, an input action is enabled corresponding to the first gesture.

Abstract: Various implementations disclosed herein include devices, systems, and methods that interpret user activity as user interactions with user interface (UI) elements positioned within a three-dimensional (3D) space such as an extended reality (XR) environment. Some implementations enable user interactions with virtual elements displayed in 3D environments that utilize alternative input modalities, e.g., XR environments that interpret user activity as either direct interactions or indirect interactions with virtual elements.

Abstract: In accordance with an embodiment, described herein is a system and method for supporting queries having sub-select constructs in a multidimensional database computing environment. The system enables a sub-select construct to be provided as part of an input query, for example using a Multidimensional Expressions (MDX), or other type of query. The inner sub-select, specified by the input query, is not executed by the system before the main query, but is used to restrict the data space for execution of the main select. The approach to processing the sub-select enables support for security-sensitive or other types of aggregation use cases.

Abstract: Processing gesture input includes obtaining hand tracking data based on a set of camera frames, determining a hand pose based on the hand tracking data, and determining an intentionality classification for a gesture based on the hand pose. An input action corresponding to the gesture is enabled based on the hand pose and the intentionality classification. An occlusion classification is determined for the hand based on the hand pose and the input gesture can be determined based on the occlusion classification.

Abstract: In accordance with an embodiment, described herein is a system and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment. A cube schema inference component can successively perform column-wise splits to calculate information gain ratios between each pair of a plurality of columns in the tabular data. A cross correlation matrix can be constructed from the information gain ratios between each pair of columns. The system can determine relationships among the plurality of columns based on the cross correlation matrix; create a hierarchy directed graph to represent the relationships; and further transform the hierarchy directed graph into a cube schema that can be used to create a cube for loading the tabular data, or to map the tabular data into an existing cube in the multidimensional database environment.

Abstract: In accordance with an embodiment, described herein is a system and method for providing sandboxing support in a multidimensional database environment. A plurality of sandboxes and a baseline data can be provided in a multidimensional database server, with each sandbox created for a particular “what-if” analysis, and configured to store one or more changes to the baseline data. When a request is received for a report on a particular “what-if” analysis from a client/user, the multidimensional database server can split the request into a first query and the second query, with the first query directed to a sandbox associated with the client/user, and the second query directed to the baseline data. The multidimensional database server can merge results from the first query and the second query, and perform aggregations and calculations on the merged data, before sending the merged data to a client.

Abstract: Aspects of the subject technology provide improved techniques for gesture recognition. Improved techniques may include detecting and/or classifying an interaction between the body part and another object in a scan of the body part, and then controlling recognition of a gesture based on the interaction. In an aspect, recognition parameters may be selected based on the interaction classification that disable recognition of one or more gestures while not disabling recognition of other gestures.

Abstract: In accordance with an embodiment, described herein is a system and method for aggregating values through risk dimension hierarchies to create risk models in a multidimensional database. The system can include a cube that stores a plurality of standard risk measures organized into different risk dimension hierarchies, and a pluggable calculation engine executing a plurality of scripts to dynamically operate on any value in the cube. The system can aggregate values through the plurality of risk dimensional hierarchies, and either store an aggregated value against a dimension member, or dynamically calculate the aggregated value on demand. By aggregating values through the risk dimension hierarchies, a plurality of risk models can be built to measure a variety of risks.

Abstract: In accordance with an embodiment, described are systems and methods for real time data aggregation in a virtual cube in a multidimensional database. A method can provide, at a computer that includes one or more microprocessors, a multidimensional database server executing on the computer, wherein the multidimensional database server supports at least one multidimensional cube, a data source associated with the multidimensional cube, and at least one source of real-time data accessible by the data source. The method can query, by the multidimensional cube, the data source, the query comprising an identification of the at least one source of real-time data. The method can provide, by the data source, on-demand access to the multidimensional cube to the at least one source of real-time data. The method can use targeted data from the at least one source of real-time data within the multidimensional cube without being stored therein.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with an embodiment, described herein is a system and method for use of a dynamic flow process (referred to herein in some examples as Query Processing Dynamic Flow (QPDF)) in a multidimensional database computing environment. The dynamic flow process enables hybrid use of, for example, aggregate storage option, block storage option, or other types of storage containers, and provides a common flow to process a received input query in bottom-up mode. The approach can be used to reduce the size of the cube, which provides for efficient calculation of dynamic members.

Abstract: Systems and methods for supporting bottom-up aggregation in a multidimensional database computing environment. A dynamic flow, coupled with a data retrieval layer or data fetching component, which in some environments can incorporate a kernel-based data structure, referred to herein as an odometer retriever, or odometer, that manages pointers to data blocks, contains control information, or otherwise operates as an array of arrays of pointers to stored members, enables bottom-up aggregation of cube data which, for example with pure aggregating queries, provides considerable run time improvement.

Abstract: In accordance with an embodiment, described herein is a system and method for use of a dynamic flow process (referred to herein in some examples as Query Processing Dynamic Flow (QPDF)) in a multidimensional database computing environment. The dynamic flow process enables hybrid use of, for example, aggregate storage option, block storage option, or other types of storage containers, and provides a common flow to process a received input query in bottom-up mode. The approach can be used to reduce the size of the cube, which provides for efficient calculation of dynamic members.

Abstract: In accordance with an embodiment, the system supports bottom-up aggregation in a multidimensional database computing environment. A dynamic flow, coupled with a data retrieval layer or data fetching component, which in some environments can incorporate a kernel-based data structure, referred to herein as an odometer retriever, or odometer, that manages pointers to data blocks, contains control information, or otherwise operates as an array of arrays of pointers to stored members, enables bottom-up aggregation of cube data which, for example with pure aggregating queries, provides considerable run time improvement.