Abstract: A computer-implemented method, including: accessing, from a hardware storage device, metadata specifying fields of one or more datasets stored in the hardware storage device and data specifying allowed operations; generating a schema in accordance with the accessed metadata and the accessed data, with the schema specifying which of the fields are accessible in responding to a request; receiving, from a client device by a computer system, a request for data based on the one or more datasets; transmitting the request and the schema to an artificial intelligence engine; receiving from the artificial intelligence engine a dataset specification that includes a structured, machine-readable representation of the request that has been generated in accordance with the schema; compiling the dataset specification into a query in accordance with the schema such that the query is executable to perform operations specified by the schema and access data specified by the schema.

Abstract: Generating a synthetic dataset from road vehicle positioning data. The method involves iteratively selecting a seed record and determining a set of candidate partner records based on matching bigrams between the selected seed record and potential partner records. The method further includes determining subsequences for the candidate partner records and selected seed record based on locations of the matching bigrams. A candidate partner record is selected based on whether a length difference, in terms of time, distance, or number of road events, between the subsequences is less than a defined threshold. The subsequence of the selected seed record is replaced with the subsequence of the selected candidate partner record to produce a mutated seed record. The mutated seed records to produce a synthetic dataset.

Abstract: Generating a synthetic dataset from road vehicle positioning data. The method involves iteratively selecting a seed record and determining a set of candidate partner records based on matching bigrams between the selected seed record and potential partner records. The method further includes determining subsequences for the candidate partner records and selected seed record based on locations of the matching bigrams. A candidate partner record is selected based on whether a length difference, in terms of time, distance, or number of road events, between the subsequences is less than a defined threshold. The subsequence of the selected seed record is replaced with the subsequence of the selected candidate partner record to produce a mutated seed record. The mutated seed records to produce a synthetic dataset.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Generating a synthetic longitudinal dataset includes identifying subsequence patterns in records defining event sequences for patients. Feature vectors are determined, each characterizing a corresponding one of the records, based on the subsequence patterns. The feature vectors are embedded in a lower dimension space. A seed record is iteratively selected from among the records and in each iteration: subsequence patterns are identified in a subset of the records. Instances of subsequence patterns in the seed record are replaced with instances of similar subsequence patterns identified in the subset of the records to form a modified seed record. The iterations are repeated until all of the records have been selected as the seed record. The modified seed records are combined to form the synthetic dataset.

Abstract: A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

Abstract: A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Methods for making collections of small particles having spin-lattice relaxation times greater than about 5 minutes are described. The long-T1 particles are useful as imaging agents for nuclear magnetic resonance imaging. In one embodiment, bulk silicon wafers are reduced to particles in a machining process, and the particles processed to obtain a collection of particles having an average size of about 300 nanometers and a T1 relaxation time of about 15 minutes. The particles can be subjected to post-fabrication processing to alter their surface composition or the chemical functionality of their surface. In certain embodiments, porous particles produced by the inventive methods can be loaded with pharmaceutical drugs and used to track and evaluate delivery and effectiveness of drugs.