Abstract: A method may include collecting data from a variety of data sources associated with a user. The data sources may include personal data sources, corporate data sources, and public data source. The data collected from the variety of data sources may be enriched through categorization and aggregation. For example, browser history may be categorized based on types of website and aggregated to reflect the quantity of interactions with each category of website. A multi-dimensional digital profile may be generated based on the enriched data. For instance, the digital profile may include a social, emotional, spiritual, environmental, occupational, intellectual, and physical dimension. One or more recommendation corresponding to one or more of a burnout prediction, wellness recommendation, learning plan, skill gap, and personality type may be generated based on the digital profile. Related systems and computer program products are also provided.

Abstract: A method may include collecting data from a variety of data sources associated with a user. The data sources may include personal data sources, corporate data sources, and public data source. The data collected from the variety of data sources may be enriched through categorization and aggregation. For example, browser history may be categorized based on types of website and aggregated to reflect the quantity of interactions with each category of website. A multi-dimensional digital profile may be generated based on the enriched data. For instance, the digital profile may include a social, emotional, spiritual, environmental, occupational, intellectual, and physical dimension. One or more recommendation corresponding to one or more of a burnout prediction, wellness recommendation, learning plan, skill gap, and personality type may be generated based on the digital profile. Related systems and computer program products are also provided.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a set of audio files. Each audio file in the set of audio files includes audio from an audio call. The program further truncates each audio file in the set of audio files to a defined call length. For each audio call in the set of audio calls, the program also receives a transcript of the audio call based on the audio file of the audio call. For each audio call in the set of audio calls, the program further uses the transcript of the audio call as input to a machine learning model for the machine learning model to predict a category from a plurality of categories that is associated with the audio call.

Abstract: In various embodiments, Methods, systems, computer-readable storage media, and apparatuses are described for determining interactions between geometric objects stored on distributed computing nodes. Nodes may be configured to perform a join operation in response to a query. The worker nodes may perform a source stage where source geometric objects are identified and sent to other worker nodes. The worker nodes may perform a target stage a set of target geometric objects is identified and a join operation performed on the received source and target geometric objects. The worker nodes may also perform a join operation that is based on storage of geometric objects in a storage sieve tree structure (“SST”). A space-filling curve may be used to map levels of the SST to a one-dimensional interval. The join operation may be performed only on objects whose mapped portions of the interval overlap. Other embodiments, may be described and claimed.

Abstract: Methods, computer-readable storage media, and apparatuses for determining interactions between geometric shapes on a geoid may be described. In embodiments, queries about geometric shapes on a geoid may be received. Various interaction tests, such as point-point or point-segment intersection tests, may be identified that need to be determined to satisfy the query. Points for the geometric shapes may be projected onto an auxiliary sphere that is associated with the geoid. The tests may be performed using these projected points, and from the results of these tests, a response may be provided to the query. Approximate computations, such as limited-precision CPU-based computations, may also be utilized to reduce computational complexity during test performance. An error value may be determined during performance of these tests, and, if the error value is greater than a pre-determined error bound, the test may be performed using higher-precision computations. Additional embodiments may be described and claimed.

Abstract: An integrated genomic system is a software system that facilitates data management and analysis connected with integrated genomic research, such as statistical genetics. Reference information, biological and experimental, describes context from which experiments are made. Reference information, including annotations for genes, markers, study, individuals, and so on, is input into the integrated genomic system for consolidation, accessibility, and linkage with other data to aid researchers to view influences and interactions in biological systems.