Abstract: In some examples, a system receives delimiter separated value (DSV) data, and categorizes a character in the DSV data into a selected layer of a plurality of layers, where characters in a first layer of the plurality of layers comprise data characters, characters in a second layer of the plurality of layers comprise delimiters, and characters in a third layer of the plurality of layers comprise grouping symbols to group a string of characters into a semantic unit. The system parses the DSV data according to the categorizing.

Abstract: In some examples, a system performs a delimiter identification process that includes identifying candidate record delimiters and candidate field delimiters in the input data, and providing different pairs of candidate record delimiters and candidate field delimiters. For each respective pair of the different pairs, the system identifies records using the corresponding candidate record delimiter of the respective pair, and computes a collection of measures including a measure indicating a quantity of unique fields observed in the records identified using the corresponding field delimiter of the respective pair. The system selects, based on values of the collection of measures computed for corresponding pairs of the different pairs, a record delimiter and a field delimiter in a pair of the different pairs.

Abstract: In some examples, a system performs a delimiter identification process that includes identifying candidate record delimiters and candidate field delimiters in the input data, and providing different pairs of candidate record delimiters and candidate field delimiters. For each respective pair of the different pairs, the system identifies records using the corresponding candidate record delimiter of the respective pair, and computes a collection of measures including a measure indicating a quantity of unique fields observed in the records identified using the corresponding field delimiter of the respective pair. The system selects, based on values of the collection of measures computed for corresponding pairs of the different pairs, a record delimiter and a field delimiter in a pair of the different pairs.

Abstract: A multi-parameter data type framework can, among other things, provide a more comprehensive, systematic, and/or formal mechanisms for determining an appropriate data type for a data set. For example, the multi-parameter data type framework can be used to allow analytic tools to virtually automatically figure out an appropriate data type for a set of data values.

Abstract: A database query to be run against a database is received by a processor. The query includes a query predicate. The query predicate includes a condition. The condition applies to a single database table. The condition is parsed to create an input vector. The input vector is submitted to a neural network. The neural network is trained to calculate the selectivity, a number of unique values (NUV) of results of applying predicates to the single database table, and a high mode frequency (HMF) of results of applying predicates to the single database table. The neural network determines the selectivity of the query predicate, an NUV for each column in the result of applying the query predicate to the single database table, and an HMF for each column in the result of applying the query predicate to the single database table.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.