Walmart Apollo, LLC

Albin Kuruvilla

Mridul Jain

Saigopal Thota

Antriksh Akshesh Shah

Pruthvi Raj Eranti

Gajendra Alias Nishad Kamat

Ashraful Arefeen

Neil Mohan Reddy Palleti

Rijul Magu

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. The acts can include executing multiple iterations until a stopping condition is satisfied, by, for each of the multiple iterations: (i) processing a set of input nodes at the multiple processors using a set of criteria to generate first data at the multiple processors, wherein the set of input nodes is different at each of the multiple iterations; (ii) determining a list of output nodes using adjacency rows of an adjacency list at different ones of the multiple processors, such that each output node of the list of output nodes is one hop from a respective input node of the set of the input nodes; and (iii) updating the set of the input nodes for a subsequent iteration of the multiple iterations based on the list of output nodes when the stopping condition is not satisfied. The acts also can include outputting second data based at least in part on the first data. Other embodiments are disclosed.

Traversing an adjacency list on distributed processors

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.

Automatically measuring quality scores for connected components using machine learning models

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. Each iteration can include processing a set of input nodes at the multiple processing nodes using the set of criteria to generate first data at the multiple processing nodes, determining a set of output nodes such that each output node of the set of output nodes is one hop from a respective input node of the set of input nodes, consolidating the first data from the multiple processing nodes to a first processing node of the multiple processing nodes, processing the first data at the first processing node; and assigning the set of input nodes for a subsequent iteration of the one or more iterations based on the set of output nodes when the stopping condition is not satisfied. The acts further can include outputting second data based on the first data received and processed at the first processing node during the one or more iterations. Other embodiments are disclosed.

Traversing a large connected component on a distributed file-based data structure

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Bengaluru, India

Albin Kuruvilla