Abstract: A system and method utilizes masked language models in order to provide data-type detection, such as (but not limited to) prediction of columnar headings. Two masked language models are pre-trained on example columnar text. One model predicts missing data at the entity level (e.g., masked entity names that may be made up of whole words), while the other predicts missing data at the character level (e.g., masked individual characters). The table with missing column headings is fed into both models, and the output is contextual word embeddings and contextual character embeddings. These results are merged, and then fed into a neural network classifier to then predict the column names.

Abstract: This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.

Abstract: This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.

Abstract: A computerized method for implementing a matching platform for entities includes a real-time processing layer using real-time linking on an input data stream, and a high-throughput layer linking entities from a batch data source stream. The real-time linking results are stored in a state change store and the high-throughput linking results are stored in a state store. On-demand linking uses the state change store and the state store. A lambda switch ensures that the entity states remain consistent when the on-demand linking switches from an older generation of the state store and the stage change store to a newer generation of the state store and the state change store. These stores are implemented in an entity database that parses queries into instructions and creates an instruction stream to control entity matching.

Abstract: This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.

Abstract: A computerized method for implementing a matching platform for entities includes a real-time processing layer using real-time linking on an input data stream, and a high-throughput layer linking entities from a batch data source stream. The real-time linking results are stored in a state change store and the high-throughput linking results are stored in a state store. On-demand linking uses the state change store and the state store. A lambda switch ensures that the entity states remain consistent when the on-demand linking switches from an older generation of the state store and the stage change store to a newer generation of the state store and the state change store. These stores are implemented in an entity database that parses queries into instructions and creates an instruction stream to control entity matching.

Abstract: In one aspect, a computerized method for implementing a matching platform for entities includes the step of, in a real-time data processing layer, implementing a real-time linking on an input event stream. The method includes storing an output of the real-time linking in a state change store. The method includes the step of, in a high-throughput layer, implementing a high-throughput linking of entities from a batch data source stream. The method includes storing an output of the high-throughput linking of entities in a state store to generate a unified and consistent view of the entities across a different representation of the entities. The method includes implementing an on-demand linking using the state change store and the state store.

Abstract: This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.

Abstract: This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.

Abstract: This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.

Abstract: In one aspect, a computerized method for implementing a matching platform for entities includes the step of, in a real-time data processing layer, implementing a real-time linking on an input event stream. The method includes storing an output of the real-time linking in a state change store. The method includes the step of, in a high-throughput layer, implementing a high-throughput linking of entities from a batch data source stream. The method includes storing an output of the high-throughput linking of entities in a state store to generate a unified and consistent view of the entities across a different representation of the entities. The method includes implementing an on-demand linking using the state change store and the state store.

Abstract: This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.

Abstract: A method of operation of a navigation system includes: locating a target POI; generating a relationship for the target POI and a related POI; and generating a travel route based on the relationship to the target POI for displaying on a device.