Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: generating linkage scores between nodes at least based on a machine learning model; creating links between the nodes to form connected components based on the linkage scores exceeding a predetermined threshold; generating an actual matching linkage set of the nodes linked in the connected components by using a relaxed blocking criteria; and generating a quality score for the connected components. Other embodiments are disclosed.

Abstract: In some examples, a system may, obtain constraint data and customer profile data of a plurality of customers associated with the system. Moreover, for each customer of the plurality of customers, the system may, based on the customer profile data of the customer and the constraint data, generate a score associated with one or more constraints of the plurality of constraints, based on the score of each of the one or more constraints, generate an overall score, and associate the overall score with a customer profile of the customer. Further, the system may, implement operations that generate a clean dataset based on the overall score associated with a customer profile of each of the plurality of customers.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: bundling multiple registered queries of a dataset using a scheduling technique, wherein the dataset is homogenous in schema; running a single table scan of the dataset to process the multiple registered queries of the dataset in parallel; and generating a respective output responsive to each of the multiple registered queries. Other embodiments are disclosed.

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: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: generating linkage scores between nodes at least based on a machine learning model; creating links between the nodes to form connected components based on the linkage scores exceeding a predetermined threshold; generating an actual matching linkage set of the nodes linked in the connected components by using a relaxed blocking criteria; and generating a quality score for the connected components. Other embodiments are disclosed.

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: A distributed system including multiple processors associated with non-transitory computer-readable media storing computing instructions. The computing instructions, when collectively executed on the multiple processors, cause the multiple processors collectively to perform certain acts.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform ingesting streaming events for processing by multiple models; mapping each of the streaming events to a model of the multiple models; storing each of the streaming events in a respective queue in a respective sequence store, such that a respective one of the multiple models retrieves (i) a respective one of the streaming events in the respective sequence store associated with the respective one of the multiple models and (ii) a respective key corresponding to the respective one of the streaming events from a leaf store, to asynchronously perform the respective machine-learning inferencing, wherein the multiple models run independently and in parallel on multi-tenant threads. Other embodiments are disclosed.

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: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: bundling multiple registered queries of a dataset using a scheduling technique, wherein the dataset is homogenous in schema; running a single table scan of the dataset to process the multiple registered queries of the dataset in parallel; and generating a respective output responsive to each of the multiple registered queries. Other embodiments are disclosed.

Abstract: A distributed system including multiple processing nodes. The distributed system can perform certain acts. The acts can include receiving a set of input nodes and a set of criteria. The acts can include obtaining an adjacency list representing a large connected component. The large connected component can include nodes, edges, and edge metadata. A quantity of the nodes of the large connected component can exceed 1 billion. The adjacency list can be distributed across the multiple processing nodes. The nodes of the large connected component can include the input nodes. The acts also can include performing one or more iterations of traversing the large connected component until a stopping condition is satisfied.

Abstract: A distributed system including multiple processing nodes. The distributed system can perform certain acts. The acts can include receiving a first conflation event identifying a first node and a second node. The first node can be part of a first set. The first set can include a sole parent node stored at a first processing node of the multiple processing nodes. The second node can be part of a second set. The second set can include a sole parent node stored at a second processing node of the multiple processing nodes. The first and second sets can be disjoint sets. The first conflation event can be received at an event-driven stream application at one of the multiple processing nodes. The acts also can include conflating the first set and the second set into a conflated set. The conflated set can include the first and second nodes. The conflated set can include a sole parent node.

Abstract: A distributed system including multiple processing nodes. The distributed system can perform certain acts. The acts can include receiving a first conflation event identifying a first node and a second node. The first node can be part of a first set. The first set can include a sole parent node stored at a first processing node of the multiple processing nodes. The second node can be part of a second set. The second set can include a sole parent node stored at a second processing node of the multiple processing nodes. The first and second sets can be disjoint sets. The first conflation event can be received at an event-driven stream application at one of the multiple processing nodes. The acts also can include conflating the first set and the second set into a conflated set. The conflated set can include the first and second nodes. The conflated set can include a sole parent node.

Abstract: A distributed system including multiple processing nodes. The distributed system can perform certain acts. The acts can include receiving a set of input nodes and a set of criteria. The acts can include obtaining an adjacency list representing a large connected component. The large connected component can include nodes, edges, and edge metadata. A quantity of the nodes of the large connected component can exceed 1 billion. The adjacency list can be distributed across the multiple processing nodes. The nodes of the large connected component can include the input nodes. The acts also can include performing one or more iterations of traversing the large connected component until a stopping condition is satisfied.

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.

Abstract: Resource processor circuitry parses responses corresponding to a resource of a request from other responses corresponding to other resources embedded in the resource of the request. This parsing reduces the shear amount of data needed to be processed by the resource processor circuitry, thereby improving performance of the resource processor circuitry. The resource processor circuitry parsing uses a pattern matching routine to parse the response corresponding to the resource of the request from the responses corresponding to other resources embedded in the resource of the request. Thereafter, the resource processor circuitry includes the response corresponding to the resource of the request to a catalog.

Abstract: Resource processor circuitry parses responses corresponding to a resource of a request from other responses corresponding to other resources embedded in the resource of the request. This parsing reduces the shear amount of data needed to be processed by the resource processor circuitry, thereby improving performance of the resource processor circuitry. The resource processor circuitry parsing uses a pattern matching routine to parse the response corresponding to the resource of the request from the responses corresponding to other resources embedded in the resource of the request. Thereafter, the resource processor circuitry includes the response corresponding to the resource of the request to a catalog.

Abstract: A gateway system determines whether load conditions are heavy or light according to a predetermined criterion. If load conditions are light, the gateway operates in reactive mode. If load conditions are heavy, the gateway operates in proactive mode.

Abstract: A gateway system determines whether load conditions are heavy or light according to a predetermined criterion. If load conditions are light, the gateway operates in reactive mode. If load conditions are heavy, the gateway operates in proactive mode.