Abstract: Multiple types of tokens can be generated and utilized in a highly structured document with freeform text. For example, a tokenization system may receive a request for tokenizing a document with a first portion having structured content and a second portion having unstructured or semi-structured content. In response, the tokenization system identifies sensitive information in the first portion of the document, generates format-preserving tokens for the sensitive information in the first portion of the document, identifies sensitive information in the second portion of the document, and generates self-describing tokens for the sensitive information in the second portion of the document. The self-describing tokens reference the sensitive information in the first portion of the document. The tokenization system may then communicate the format-preserving tokens and the self-describing tokens to the first client computing system or to a second client computing system.

Abstract: A tokenization system receives a request for data anonymization, the request referencing unstructured/semi-structured content containing values of interest. The tokenization system performs a tokenization operation on the unstructured/semi-structured content, generates self-describing tokens for the values of interest, each self-describing token having a preconfigured pattern, an indication of a protection strategy, and a token value, and stores the values of interest in a secure data vault. The tokenization system may receive a request to reveal the self-describing tokens in the unstructured/semi-structured content. In response, the tokenization system searches the anonymized version of the unstructured or semi-structured content for the preconfigured pattern, identifies self-describing tokens, uses the self-describing tokens to retrieve the values of interest from the secure data vault, and produces a detokenized version of the unstructured/semi-structured content containing the values of interest.

Abstract: Multiple types of tokens can be generated and utilized in a highly structured document with freeform text. For example, a tokenization system may receive a request for tokenizing a document with a first portion having structured content and a second portion having unstructured or semi-structured content. In response, the tokenization system identifies sensitive information in the first portion of the document, generates format-preserving tokens for the sensitive information in the first portion of the document, identifies sensitive information in the second portion of the document, and generates self-describing tokens for the sensitive information in the second portion of the document. The self-describing tokens reference the sensitive information in the first portion of the document. The tokenization system may then communicate the format-preserving tokens and the self-describing tokens to the first client computing system or to a second client computing system.

Abstract: An intelligent integration system runs a workflow implementation in a test mode with an avatar, a set of handlers, and a choreography. The system receives a message from the second entity to the first entity via their respective avatars. The arrival of data in the message triggers the first entity to invoke an integration activity which utilizes the set of handlers. The integration activity follows the choreography and moving the data through the choreography is defined and governed by a choreography key. The system may stop moving the data through the choreography, determine/generate a handler that can meet a requirement of the choreography key that is not met by the set of handlers, and update the workflow implementation to include the handler. The system may then continue or restart the workflow implementation with the handler that can meet the requirement of the choreography key.

Abstract: At design time, an intelligent integration system may create a new avatar to represent, in a computing environment where the intelligent integration system operates, a computing system of an entity, the new avatar having a set of states, transitions, and data items, wherein the entity is one of a pair of entities having a conversation, each of the pair of entities having a choreography. The intelligent integration system may access a library containing avatars, determine a current configuration of states, transitions, and validation rules for the avatars, and perform a gap analysis. The gap analysis can identify gaps between the states, transitions, and data items of the new avatar and the current configuration of states, transitions, and validation rules for the avatars. The intelligent integration system is operable to determine a set of handlers that can remediate the gaps and update a workflow implementation to include the set of handlers.

Abstract: A method for enabling transactions may include defining states of a transactional system that implements the method and transitions between the states. Each transition between the states can comprise a data item. The transactional system may determine the states and the transitions between the states used by the first entity, determine the states and the transitions between the states used by the second entity, determine a required state not used by the first entity but is used by the second entity, and determine a required transition between the states. The required transition between the states is not used by the first entity but is used by the second entity. The transactional system may determine a required data item of the required transition between the states and define a handler to enable transactions between the first entity and the second entity in the transactional system.

Abstract: A reusable converter framework provides a set of tools for (1) loading existing data formats and transformation instructions of a source technology and (2) enabling the result of step (1) to be queried through Java code and analyzed. The source technology may follow a particular data transformation strategy such as data-drive, script-driven, or query driven. The reusable converter framework has a Java representation, referred to herein as an interpreted form, that can hold the information of a target technology, which follows a different data transformation strategy. The source and target technologies operate on different computing platforms having different computing environments. The interpreted form is utilized in step (3) to streamline and optimize the information from step (2) and produce a working data map suitable for the target technology.

Abstract: A tokenization system receives a request for data anonymization, the request referencing structured data containing values of interest. Responsively, the tokenization system performs a tokenization operation on the structured data, generates a corresponding token, and replaces a value of interest with the corresponding token to produce an anonymized version of the structured data. The tokenization system stores the value of interest with the corresponding token in a secure data vault. Subsequently, the tokenization system may receive a request for revealing the anonymized version of the structured data containing the corresponding token. In response, the tokenization system can perform a reveal operation on the anonymized version of the structured data by querying the secure data vault for the corresponding token and retrieving the value of interest from the secure data vault using the corresponding token.

Abstract: Multiple types of tokens can be generated and utilized in a highly structured document with freeform text. For example, a tokenization system may receive a request for tokenizing a document with a first portion having structured content and a second portion having unstructured or semi-structured content. In response, the tokenization system identifies sensitive information in the first portion of the document, generates format-preserving tokens for the sensitive information in the first portion of the document, identifies sensitive information in the second portion of the document, and generates self-describing tokens for the sensitive information in the second portion of the document. The self-describing tokens reference the sensitive information in the first portion of the document. The tokenization system may then communicate the format-preserving tokens and the self-describing tokens to the first client computing system or to a second client computing system.

Abstract: A tokenization system receives a request for data anonymization, the request referencing unstructured/semi-structured content containing values of interest. The tokenization system performs a tokenization operation on the unstructured/semi-structured content, generates self-describing tokens for the values of interest, each self-describing token having a preconfigured pattern, an indication of a protection strategy, and a token value, and stores the values of interest in a secure data vault. The tokenization system may receive a request to reveal the self-describing tokens in the unstructured/semi-structured content. In response, the tokenization system searches the anonymized version of the unstructured or semi-structured content for the preconfigured pattern, identifies self-describing tokens, uses the self-describing tokens to retrieve the values of interest from the secure data vault, and produces a detokenized version of the unstructured/semi-structured content containing the values of interest.

Abstract: A tokenization system receives a request for redaction of sensitive textual content in a document, identifies a portion of the document as the sensitive textual content, and edits the document, including replacing the sensitive textual content thus identified with tokens, each token having a token value and a pattern that identifies a start and an end of the token value. The editing produces a transformed version of the document with the tokens and without the sensitive textual content. The tokenization system may then communicates the transformed version of the document with the tokens and without the sensitive textual content to the client computing system, an automated recognition service, or a redaction plug-in to a frontend application.

Abstract: A map intuition system and method that involves machine learning techniques to analyze data sets and identify mappings and transformation rules as well as machine-human interactions to leverage human intuition and intelligence to rapidly complete a map.

Abstract: A map intuition system and method that involves machine learning techniques to analyze data sets and identify mappings and transformation rules as well as machine-human interactions to leverage human intuition and intelligence to rapidly complete a map.

Abstract: A map intuition system and method that involves machine learning techniques to analyze data sets and identify mappings and transformation rules as well as machine-human interactions to leverage human intuition and intelligence to rapidly complete a map.