Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: The present disclosure relates to an axial flux motor comprising a stator assembly and a rotor assembly. The axial flux motor rotor assembly also includes an air cooling arrangement to provide air cooling to the stator assembly. The axial flux motor includes stator cores having enlarged end plates. The axial flux motor also includes a cooling jacket including fins that extend between electromagnets of the stator assembly.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.