Abstract: This application relates to apparatus and methods for automatically associating customer data to a corresponding customer. A computing device may receive linking data identifying a plurality of links, where each like associates at least two nodes that each represent customer data. The computing device may partition the linking data into multiple partitions, and cause a union find algorithm to be executed for each partition in parallel to associate each node with a parent ID. The computing device may iteratively execute a global shuffle algorithm to place all same nodes in a same partition, and may assign a same parent ID to the same nodes. The computing device may iteratively execute a path compression algorithm across all partitions to generate a graph output that associates all child nodes of a same parent node with the same parent ID.

Abstract: An apparatus, a method, and a storage medium for database query. The apparatus, method, and storage medium are configured to: determine a syntax tree corresponding to an SQL query statement includes a preset subtree, wherein the preset subtree is used to indicate a query mode for querying vector data; determine a query mode according to the preset subtree and the SQL query statement; query data in a database based on the query mode to determine query results. By using an SQL query statement including a preset subtree to query a vector database, to realize an efficient approximate query, so as to avoid high access costs caused by accessing the database by using an API. Moreover, it can reduce user's understanding costs on unstructured query.

Abstract: Described herein are systems and methods for generating an embedding—a learned representation—for an image. The embedding for the image is derived to capture visual aspects, as well as textual aspects, of the image. An encoder-decoder is trained to generate the visual representation of the image. An optical character recognition (OCR) algorithm is used to identify text/words in the image. From these words, an embedding is derived by performing an average pooling operation on pre-trained embeddings that map to the identified words. Finally, the embedding representing the visual aspects of the image is combined with the embedding representing the textual aspects of the image to generate a final embedding for the image.

Abstract: This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.

Abstract: Described herein are systems and methods for generating an embedding—a learned representation—for an image. The embedding for the image is derived to capture visual aspects, as well as textual aspects, of the image. An encoder-decoder is trained to generate the visual representation of the image. An optical character recognition (OCR) algorithm is used to identify text/words in the image. From these words, an embedding is derived by performing an average pooling operation on pre-trained embeddings that map to the identified words. Finally, the embedding representing the visual aspects of the image is combined with the embedding representing the textual aspects of the image to generate a final embedding for the image.

Abstract: This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.

Abstract: This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.

Abstract: This application relates to apparatus and methods for automatically associating customer data to a corresponding customer. A computing device may receive linking data identifying a plurality of links, where each like associates at least two nodes that each represent customer data. The computing device may partition the linking data into multiple partitions, and cause a union find algorithm to be executed for each partition in parallel to associate each node with a parent ID. The computing device may iteratively execute a global shuffle algorithm to place all same nodes in a same partition, and may assign a same parent ID to the same nodes. The computing device may iteratively execute a path compression algorithm across all partitions to generate a graph output that associates all child nodes of a same parent node with the same parent ID.

Abstract: This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.