Abstract: The present disclosure pertains to parsing addresses using a deep learning model. The deep learning model may be trained using a plurality of reference address records and a plurality of modifications to the plurality of reference address records. In some implementations, the model can be a multi-layer bi-directional long short-term memory recurrent neural network model. The output of the model may include indications of an address component type for each character of the address. The output can be parsed based on the indications of the address component type to obtain a plurality of labeled address components for the address. The labeled address components can be compared to reference address records to determine a matched address record identifying a geographic location.

Abstract: An e-liquid filling structure, including a drip tip with an e-liquid cartridge, an air duct provided in the drip tip, a base mounted to the drip tip, an atomizing core on the base, and a connecting seal hermetically connecting the atomizing core to the air duct. Embodiments of the present invention further disclose an assembly and e-liquid injection method of an e-liquid filling structure, including installing an atomizing core, pre-installing a connecting seal, injecting e-liquid, and installing a base. An e-liquid injection hole in the embodiments of the present invention not only allows injection of e-liquid but also functions to fasten the atomizing core while another e-liquid filling hole is spared, so that the atomizing core is reliably sealed.

Abstract: The present disclosure pertains to parsing addresses using a deep learning model. The deep learning model may be trained using a plurality of reference address records and a plurality of modifications to the plurality of reference address records. In some implementations, the model can be a multi-layer bi-directional long short-term memory recurrent neural network model. The output of the model may include indications of an address component type for each character of the address. The output can be parsed based on the indications of the address component type to obtain a plurality of labeled address components for the address. The labeled address components can be compared to reference address records to determine a matched address record identifying a geographic location.

Abstract: Disclosed herein are system, method, and computer program product embodiments for data anonymization in an in-memory database. An embodiment operates by receiving an indication to perform data anonymization based on one or more quasi attributes of a data set. The data set is sorted based on the one or more quasi attributes. The sorted data set is grouped into a first plurality of groups. A particular group that does not include enough records to satisfy an anonymization threshold is identified from amongst the first plurality of groups. The particular group is combined with another group of the first plurality of groups.

Abstract: Disclosed herein are system, method, and computer program product embodiments for data anonymization in an in-memory database. An embodiment operates by receiving an indication to perform data anonymization based on quasi attributes of a data set. Partitioning is recursively performed on the data set based on one or more of the quasi attributes until both a first anonymization threshold corresponding to the quasi attributes is satisfied and a second anonymization threshold corresponding to the one or more sensitive attributes is satisfied for each of a plurality of sub-partitions produced as a result of the partitioning. A resultant data set including a plurality of records of the data set corresponding to the plurality of sub-partitions that satisfy both the first anonymization threshold and the second anonymization threshold is provided.

Abstract: Disclosed herein are system, method, and computer program product embodiments for data anonymization in an in-memory database. An embodiment operates by identifying a textual attribute corresponding to data of an input table. A value corresponding to the textual attribute is determined for each of the plurality of records. A plurality of groups is generated based on the determined values. At least portion of the data as sorted into the plurality of groups is provided.

Abstract: Disclosed herein are system, method, and computer program product embodiments for data anonymization in an in-memory database. An embodiment operates by identifying a textual attribute corresponding to data of an input table. A value corresponding to the textual attribute is determined for each of the plurality of records. A plurality of groups is generated based on the determined values. At least portion of the data as sorted into the plurality of groups is provided.

Abstract: Disclosed herein are system, method, and computer program product embodiments for data anonymization in an in-memory database. An embodiment operates by receiving an indication to perform data anonymization based on one or more quasi attributes of a data set. The data set is sorted based on the one or more quasi attributes. The sorted data set is grouped into a first plurality of groups. A particular group that does not include enough records to satisfy an anonymization threshold is identified from amongst the first plurality of groups. The particular group is combined with another group of the first plurality of groups.

Abstract: Disclosed herein are system, method, and computer program product embodiments for data anonymization in an in-memory database. An embodiment operates by receiving an indication to perform data anonymization based on quasi attributes of a data set. Partitioning is recursively performed on the data set based on one or more of the quasi attributes until both a first anonymization threshold corresponding to the quasi attributes is satisfied and a second anonymization threshold corresponding to the one or more sensitive attributes is satisfied for each of a plurality of sub-partitions produced as a result of the partitioning. A resultant data set including a plurality of records of the data set corresponding to the plurality of sub-partitions that satisfy both the first anonymization threshold and the second anonymization threshold is provided.

Abstract: Described herein is a technology for facilitating searching and matching of data. In accordance with one implementation, first and second feature sets are extracted. The first feature set is associated with an input data string including one or more first ideographic elements, while the second feature set is associated with a candidate string including one or more second ideographic elements. A match score of the candidate string is determined based on the first and second feature sets.

Abstract: Described herein is a technology for facilitating searching and matching of data. In accordance with one implementation, first and second feature sets are extracted. The first feature set is associated with an input data string including one or more first ideographic elements, while the second feature set is associated with a candidate string including one or more second ideographic elements. A match score of the candidate string is determined based on the first and second feature sets.

Abstract: Described herein is a technology for facilitating searching and matching of data. In some implementations, an input data string, including one or more ideographic elements, is received and converted to a Latin-based input data string. One or more input keys may be generated based on the Latin-based input data string. A reference database may be searched for one or more candidate records indexed by at least one of the one or more input keys. If the one or more candidate records are found, a match score is determined for the one or more candidate records.

Abstract: Described herein is a technology for facilitating searching and matching of data. In some implementations, an input data string, including one or more ideographic elements, is received and converted to a Latin-based input data string. One or more input keys may be generated based on the Latin-based input data string. A reference database may be searched for one or more candidate records indexed by at least one of the one or more input keys. If the one or more candidate records are found, a match score is determined for the one or more candidate records.

Abstract: Described herein is a technology for facilitating searching and matching of data. In some implementations, an input data string, including one or more ideographic elements, is received and converted to a Latin-based input data string. One or more input keys may be generated based on the Latin-based input data string. A reference database may be searched for one or more candidate records indexed by at least one of the one or more input keys. If the one or more candidate records are found, a match score is determined for the one or more candidate records.