Abstract: A method of preparing a dataset may comprise calculating a pattern relevance for a first field in the dataset and modifying the first field based on the pattern relevance. The method may further comprise detecting a contextual cue in the first field. The method may further comprise retrieving contextual information for a value in the first field and adding that contextual information to the database. Finally, the method may further comprise identifying a numerical scheme for the first field and parsing the first field into a number according to that numerical scheme.

Abstract: A method and/or system includes: adding a new source of data to be stored in the data storage system; obtaining the schema for the new source of data to be stored in the data storage system; storing the data to be stored in the data storage system in a Not Only Structured Query Language (NOSQL) database in the data storage system; and storing schema for the data to be stored in the data storage system in a metadata store in the electronic data storage system wherein the metadata store is separate from the NOSQL database.

Abstract: A method and/or system of extracting a table having data in a plurality of rows from a Not Only Structured Query Language (NoSQL) database to a different type of database that includes: scanning all the rows in a desired table in the NoSQL database and producing a list of column families and associated column names; creating a schema for a new table having a table catalog of new column names using a Java Script Object Notation (JSON) structure to extract the columns names from the list of column families; reading and extracting at least a portion of the data from the desired table in the NoSQL database into the new table having the table catalog of new columns names; associating a creation timestamp with the new table; and saving the new table having the table catalog of new column names to the different database.

Abstract: An approach is provided that automatically computes a number of feature combinations based on a set of subject matter fields that are selected from a set of subject matter fields and further based on qualifiers that correspond to the selected set of subject matter fields. A model in an artificial intelligence (AI) system is then trained using the computed plurality of feature combinations.

Abstract: A method and/or system includes: adding a new source of data to be stored in the data storage system; obtaining the schema for the new source of data to be stored in the data storage system; storing the data to be stored in the data storage system in a Not Only Structured Query Language (NOSQL) database in the data storage system; and storing schema for the data to be stored in the data storage system in a metadata store in the electronic data storage system wherein the metadata store is separate from the NOSQL database.

Abstract: A semantic, machine readable language part that supports the specification of features is used by an engine that interprets that language to produce the features based on the raw data. In this way, the model developer can specify the measurement columns (for example, data fields), to obtain the dimensions from which the engine can compute the multiple combinations of features possible based on this set of input fields. This computation of features can be used to perform machine learning (ML) training and/or scoring algorithms (for example, ML algorithms for fraud detection).

Abstract: A method and/or system of partially updating data in a data collection including determining, in a first incoming row of an incoming update file, whether a first incoming column contains new data, and in response using the new data in a first updated column of a first updated row in an updated master file; and in response to the first incoming column of the incoming update file not containing new data, determining whether a first master column in a first master row of the master table contains old data; and in response using the old data in the first updated column of the first updated row in the updated master file.

Abstract: A method and/or system of extracting a table having data in a plurality of rows from a Not Only Structured Query Language (NoSQL) database to a different type of database that includes: scanning all the rows in a desired table in the NoSQL database and producing a list of column families and associated column names; creating a schema for a new table having a table catalog of new column names using a Java Script Object Notation (JSON) structure to extract the columns names from the list of column families; reading and extracting at least a portion of the data from the desired table in the NoSQL database into the new table having the table catalog of new columns names; associating a creation timestamp with the new table; and saving the new table having the table catalog of new column names to the different database.

Abstract: A method of preparing a dataset may comprise calculating a pattern relevance for a first field in the dataset and modifying the first field based on the pattern relevance. The method may further comprise detecting a contextual cue in the first field. The method may further comprise retrieving contextual information for a value in the first field and adding that contextual information to the database. Finally, the method may further comprise identifying a numerical scheme for the first field and parsing the first field into a number according to that numerical scheme.

Abstract: An approach is provided that automatically computes a number of feature combinations based on a set of subject matter fields that are selected from a set of subject matter fields and further based on qualifiers that correspond to the selected set of subject matter fields. A model in an artificial intelligence (AI) system is then trained using the computed plurality of feature combinations.