Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data, where the unstructured data has multiple modes of semantically distinct data types and the unstructured data has time-varying data instances aggregated over time. An entity combining different sources of the unstructured data is formed. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models. These operations are repeated to form an aggregation of multimodal, time-varying entities and a corresponding index of individual entities and corresponding embeddings. Proximity searches are performed on embeddings within the index.

Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data. An entity is formed by combining one or more sources of the unstructured data, where the entity has relational data attributes. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models, including trunk models, where a trunk model is a machine learning model trained on data in a self-supervised manner. An enrichment model is created to predict a property of the entity. A query is processed to produce a query result, where the query is applied to one or more of the entity, the embeddings, the machine learning embedding models, and the enrichment model.

Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data. An entity is formed by combining one or more sources of the unstructured data, where the entity has relational data attributes. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models, including trunk models, where a trunk model is a machine learning model trained on data in a self-supervised manner. An enrichment model is created to predict a property of the entity. A query is processed to produce a query result, where the query is applied to one or more of the entity, the embeddings, the machine learning embedding models, and the enrichment model.

Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data, where the unstructured data has multiple modes of semantically distinct data types and the unstructured data has time-varying data instances aggregated over time. An entity combining different sources of the unstructured data is formed. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models. These operations are repeated to form an aggregation of multimodal, time-varying entities and a corresponding index of individual entities and corresponding embeddings. Proximity searches are performed on embeddings within the index.

Abstract: A non-transitory computer readable storage medium with instructions executed by a processor maintains a collection of data access connectors configured to access different sources of unstructured data. A user interface with prompts for designating a selected data access connector from the data access connectors is supplied. Unstructured data is received from the selected data access connector. Numeric vectors characterizing the unstructured data are created from the unstructured data. The numeric vectors are stored and indexed.

Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to maintain a repository of machine learning directed acyclic graphs. Each machine learning directed acyclic graph has machine learning artifacts as nodes and machine learning executors as edges joining machine learning artifacts. Each machine learning artifact has typed data that has associated conflict rules maintained by the repository. Each machine learning executor specifies executable code that executes a machine learning artifact as an input and produces a new machine learning artifact as an output. A request about an object in the repository is received. A response with information about the object is supplied.

Abstract: An extensible map module allows facilitates development of new map styles and presentation elements. Projection and presentation functions are at least partially separated, so that new styles or modes can be developed without impacting existing presentation element features. Similarly, new presentation elements can be developed independently from a particular mode or style implementation.

Abstract: A plurality of images may be combined around a central image to form one image. In one example, aerial photographs of a geographic area are taken and stored in a database, and a user requests to see some geographic region. The photo whose center is closest to the user's selected region is chosen as the center. If that photo encompasses the entire selected region, then that photo may be delivered to the user. If it takes several photos to contain the selected region, then photos from surrounding areas are chosen and are transformed to match the perspective of the central photo. The transformed photos are then stitched to the central photo and shown to the user. High-resolution photos may be delivered to a client from a server, and the transformation and stitching calculations may be performed by the client.

Abstract: An extensible map module allows facilitates development of new map styles and presentation elements. Projection and presentation functions are at least partially separated, so that new styles or modes can be developed without impacting existing presentation element features. Similarly, new presentation elements can be developed independently from a particular mode or style implementation.