A data analysis requirement definition aid apparatus includes a processor configured to execute a program; and a storage device configured to store a plurality of nodes that each include a character string, and an edge indicating a relationship between two nodes among the plurality of nodes, and wherein the processor configures to receive input of a to-be-analyzed node; retrieve, from among the plurality of nodes, a similar node including a character string similar to the character string of the to-be-analyzed node; acquire a directed graph structure constituted of a group of nodes including the similar node, and an edge between two nodes among the group of nodes; search for a path including the similar node from the directed graph structure; and output, in a displayable manner, the directed graph structure so as to display a path in a different format.

Methods and systems of similarity searching encrypted data strings are disclosed. An exemplary method can include receiving data strings, obtaining a set of reference strings, determining edit distances between each data string and the reference strings, converting each set of edit distances into a document of tokens. A method may further include encrypting the data strings, associating each of the documents with a corresponding data string, and storing the data strings and the associated documents in a memory. A method may continue by receiving a search request, determining a search set of edit distances between the search request and the reference strings, converting the search set of edit distances into a document, comparing the search document with the documents stored in memory to determine which documents are above a similarity threshold compared to the search document, and returning the data strings associated with documents above the similarity threshold.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for responding to a query. In some implementations, a computer obtains a query. The computer determines a meaning for each term in the query. The computer determines user data for the user that submitted the query. The computer identifies one or more ontologies based on the meanings for at least some of the terms. The computer identifies a knowledge graph based on the identified ontologies and the user data. The computer generates a response to the query by traversing a path of the identified knowledge graph to identify items in the knowledge graph based on the determined meaning for each of the terms. The computer generates path data that represents the path taken by the computer through the identified knowledge graph. The computer provides the generated response and the path data to the client device.

The present disclosure relates to generating a filtered data set. Data from a plurality of systems of record of a plurality of data source providers may be accessed. A master data set generated using the data accessed from the plurality of systems of record may be maintained. Restriction policies including one or more rules for restricting sharing of data may be maintained. A filtered data set may be generated for a data source provider responsive to an application of restriction policies of other data source providers to the master data set. The filtered data set may be provisioned.

The present disclosure relates to generating performance profiles of member nodes. A plurality of electronic activities can be accessed. A subset of electronic activities from the plurality of electronic activities can be identified. The subset of electronic activities can be parsed to identify participants of the electronic activities. A second node profile can be accessed for each participant. Participant types can be identified from each second node profiles. A distribution of the subset of electronic activities can be determined. A performance profile can be generated.

A method includes encoding an input data stream to generate an encoded input data pattern, transmitting the encoded input data pattern to a programmed automata processor, and searching the encoded input data pattern via the programmed automata processor to identify an identifiable data pattern within the encoded input data pattern as a data pattern search.

During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.

Machine learning to predict a layout type that each of a plurality of portions of a document appears in. This is done even though the computer-readable representation of the document does not contain information at the granularity of the prediction to be made that identifies which layout type that each of the plurality of document portions belongs in. For each of a plurality of the portions, the machine-learning system predicts the layout type that the respective portion appears in, and indexes the document using the predictions so as to result in a computer-readable index. The index represents a predicted layout type associated with each of the plurality of portions of the document. Thus, the index can be used to search based on position of a searched term within the document.

An apparatus and method displays an error between a first sequence of numbers and a second sequence of numbers. A plurality of respectively different algorithms is provided for comparing the first sequence of numbers and the second sequence of numbers. At least one of the algorithms is selected to compare the first sequence of numbers and the second sequence of numbers. The selected algorithm(s) are applied to the first sequence of numbers and the second sequence of numbers in order to identify the error, wherein the error is a discrepancy between one of the numbers in the first sequence and another of the numbers in the second sequence. The error is displayed by simultaneously displaying and indicating the numbers from each sequence that have the discrepancy. For at least one of the algorithms the numbers from each of the sequences have a matching associated label. At least one of the algorithms applies fuzzy matching to the numbers from each of the sequences. At least one of the algorithms adds the numbers from one of the sequences to obtain a partial addition sum and displays the partial addition sum with one of the numbers from the other sequence. For at least one of the algorithms the discrepancy is between non-identical positions within the two sequences.

Method and system to extract domain concepts to create domain dictionaries and ontologies comprises collecting a plurality of reference papers and further classifying the collected plurality of reference papers as relevant and irrelevant. Each of the ‘relevant’ reference papers is further processed by the system, during which the system identifies relevant sections from each document and further processes data in the relevant sections to extract required information and also to identify a relationship between different extracted information, which is further used to create domain dictionaries and ontologies.