A data operations system receives compressed data and a search term. The data operations system completes a modified decoding of the compressed data, resulting in distinguishable data terms that are smaller than the corresponding data terms, and loads modified decoded terms into a data register. The data operations system generates a truncated search term and loads instances of the truncated search term into a query register. The data operations system performs a parallel data operation, such as a query operation, by comparing each of the modified decoded terms to an instance of the truncated search term. The data operations system returns the results of the operation.

A system creates a graph of nodes connected by edges, the nodes including: i) a first node associated with a first value and a count of the first value, and ii) a second node associated with a second value and a count of the second value, the edges including an edge that connects the first and second nodes and is associated with a count of instances of the first value being stored with the second value. The system includes each node and each associated with clique count less than clique threshold in keys sets and deletes each node and each edge associated with clique count less than clique threshold. The system identifies triplet nodes connected by triplet edges. If estimated clique count for triplet values represented by triplet nodes is less than clique threshold, the system includes triplet values in keys set and identify triplet of nodes as analyzed.

A data operations system receives compressed data and a search term. The data operations system completes a modified decoding of the compressed data, resulting in distinguishable data terms that are smaller than the corresponding data terms, and loads modified decoded terms into a data register. The data operations system generates a truncated search term and loads instances of the truncated search term into a query register. The data operations system performs a parallel data operation, such as query operation, by comparing each of the modified decoded terms to an instance of the truncated search term. The data operations system returns the results of the operation.

A computer server system and method are disclosed for personalization and customizable filtering of network search results and search result rankings, such as for Internet searching. A representative server system comprises: a network interface to receive a query from a respondent or co-respondent; at least one data storage device storing a plurality of return queries; and one or more processors adapted to access the data storage device and using the query, to select the return queries for transmission; to search the data storage device for corresponding pluralities of responses to the return queries from other co-respondents or respondents; to pair-wise score the responses and generate pair-wise alignment scores for respondent and co-respondent combinations; to sort and rank the combinations according to the alignment scores; to time duration filter the plurality of respondent and co-respondent combinations; and to output a listing of the sorted and ranked respondents or co-respondents to form the personalized network search results and search result rankings.

A dataset management system organizes datasets using a data relationship graph that serves as a representation of datasets that are related to one another. For example, the data relationship graph includes nodes that each represent a dataset as well as edges that each connect two nodes. Each edge represents a similarity in characteristics of the similar datasets such as a similarity in the datasets' origin, organizational schema, lineage, or data values. When a user is to be provided recommended datasets, the dataset management system identifies candidate datasets by traversing the nodes and edges of the data relationship graph. Amongst these candidate datasets, the dataset management system evaluates users that have accessed the candidate datasets as well as the context in which the candidate datasets were accessed to identify recommended datasets for presentation to the user.

A search engine is configured to return increased diversity results based on past user interactions with search results. For a given query, historical data is analyzed to generate an item score describing a past quantity of users that navigated to a given page of an item. The historical data can further be used to generate a category score describing a past quantity of users that navigated to a given category of items. The category of items can be analyzed to generate a diversity score describing their diversity with respect to each other. Results for the given query can be arranged using items scores, category scores, and diversity scores.

An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

Systems and methods are disclosed for optimizing responses to queries. Analyses of user interactions and other behaviors can lead to association of queries with signals, including ASINs and other product descriptions. The associations can be algorithmically graphed and analyzed on a disaggregated basis and individually weighted to improve search recall and reduce the risk of returning defective search results. Machine learning techniques can further optimize the associations and/or the search results.

Sorting an array consisting of large number of elements. The present invention provides an apparatus for executing a multiway merging process which generates one output sequence from N input sequences on an array consisting of a large number of elements. The apparatus includes: an execution unit configured to execute the multiway merging process on N input sequences without rearranging the elements based on a plurality of input sequences; and a generation unit configured to rearrange the elements constituting the input sequences according to an output sequence that has been generated by the multiway merging process in the execution unit so as to generate a sorted array of elements.

Determine first count of first records storing first value in first field, second count of second records storing second value in second field, third count of third records storing third value in third field. Determine count threshold using first, second and third counts, dispersion measure based on dispersion of values stored in second field by first records and other dispersion measure based on other dispersion of values stored in third field by first records. Train machine-learning model to determine dispersion measure threshold based on dispersion and other dispersion measures. If first count is greater than count threshold, and dispersion measure is greater than dispersion measure threshold, create match index based on first and second fields. Receive prospective record storing first value in first field, second value in second field. Use match index to identify record storing first value in first field, second value in second field as matching prospective record.