Techniques for approximate neighbor selection in tree models for computing co-occurrence

A method for building a contextual recommendation engine for use in creating an integration, the method comprising:

• receiving at least one flow representation indicating how plural computer-mediated services are to interact with one another in connection with the integration, each said computer-mediated service representing a discrete program logic segment;
• parsing each said flow representation to identify invocation and conditional processing operations therein;
• arranging the identified invocation and conditional operations in accordance with a predefined format, the predefined format including a plurality of sequences, each said sequence corresponding to a respective flow representation, each said sequence including any identified operations in an order matching their occurrences in the respective flow representation,
• wherein for each said identified conditional processing operation, each identified invocation operation associated with a branch of the respective identified conditional processing operation is merged into a single path for inclusion in the respective sequence in place of the respective identified conditional processing operation;
• converting each said sequence into a listing of atomic units;
• developing a co-occurrence model based on the listing of atomic units using a window of a predefined size;
• storing the developed co-occurrence model to a model store;
• and
• enabling the model store to be queried as integrations are being built.

A non-transitory computer readable storage medium including instructions that, when executed by a hardware processor of a computing system facilitate creation of a contextual recommendation engine for use with integration design, by at least:

• receiving at least one flow representation indicating how plural computer-mediated services are to interact with one another in connection with the integration, each said computer-mediated service representing a discrete program logic segment;
• parsing each said flow representation to identify invocation and conditional processing operations therein;
• arranging the identified invocation and conditional operations in accordance with a predefined format, the predefined format including a plurality of sequences, each said sequence corresponding to a respective flow representation, each said sequence including any identified operations in an order matching their occurrences in the respective flow representation,
• wherein for each said identified conditional processing operation, each identified invocation operation associated with a branch of the respective identified conditional processing operation is merged into a single path for inclusion in the respective sequence in place of the respective identified conditional processing operation;
• converting each said sequence into a listing of atomic units;
• developing a co-occurrence model based on the listing of atomic units using a window of a predefined size;
• storing the developed co-occurrence model to a model store;
• and
• enabling the model store to be queried as integrations are being built.