Abstract: Disclosed are a super-resolution imaging system (1, 41, 51), a super-resolution imaging method, a biological sample identification system (4, 61) and method, a nucleic acid sequencing imaging system (5) and method, and a nucleic acid identification system (6) and method. The super-resolution imaging system (1, 41, 51) includes an illumination system (A) and an imaging system (B). The illumination system (A) outputs excitation light to irradiate a biological sample to generate excited light, and the imaging system (B) collects and records the excited light to generate an excited light image. The illumination system (A) includes an excitation light source (10, 10a) and a structured light generation and modulation device (11, 11a). The excitation light source (10, 10a) outputs the excitation light, and the structured light generation and modulation device (11, 11a) modulates the excitation light into structured light to irradiate the biological sample to generate the excited light.

Abstract: A system and method of operating an integration application management system comprising a processor executing code instructions for modelling, via a graphical user interface (GUI), a business integration process including a data mapping type visual element and a universal data type mapping pipeline system to classify first application input data via a classification module having a machine learning classifier to determine data classifications in the first application input data and to select, via an inference module, among a plurality of neural network mapping algorithms corresponding to each of the one or more data classifications. The system and method to generate, with the selected neural network mapping algorithm, and present to a user a plurality of sample data mappings for the data mapping type visual element from a first application to a second application in the business integration process.

Abstract: A system and method of operating an integration application management system comprising a processor executing code instructions for modelling, via a graphical user interface (GUI), a business integration process including a data mapping type visual element and a universal data type mapping pipeline system to classify first application input data via a classification module having a machine learning classifier to determine data classifications in the first application input data and to select, via an inference module, among a plurality of neural network mapping algorithms corresponding to each of the one or more data classifications. The system and method to generate, with the selected neural network mapping algorithm, and present to a user a plurality of sample data mappings for the data mapping type visual element from a first application to a second application in the business integration process.

Abstract: A system and method of operating an integration application management system comprising a processor executing code instructions for modelling, via a graphical user interface (GUI), a business integration process including a data mapping type visual element and a universal data type mapping pipeline system to classify first application input data via a classification module having a machine learning classifier to determine data classifications in the first application input data and to select, via an inference module, among a plurality of neural network mapping algorithms corresponding to each of the one or more data classifications. The system and method to generate, with the selected neural network mapping algorithm, and present to a user a plurality of sample data mappings for the data mapping type visual element from a first application to a second application in the business integration process.

Abstract: A method and a system for mapping a flattened data structure into a relation preserving data structure is disclosed. The method includes receiving a flattened schema comprising a plurality of columns. The method includes receiving a plurality of user inputs from a user. The plurality of user inputs comprises a plurality of identifiers. The method includes defining a sample target hierarchical schema based on user inputs. The method includes preparing a nested hierarchical structure corresponding to the sample target hierarchical schema in a User Interface (UI) representation. The nested hierarchical structure comprises a plurality of nodes with corresponding identifiers and relationships of the plurality of nodes. The method includes performing an Artificial Intelligence (AI) based mapping of columns of the flattened schema to respective nodes of the plurality of nodes of the nested hierarchical structure. The method further includes generating a target hierarchical schema based on AI based mapping.

Abstract: A method and a system for mapping a flattened data structure into a relation preserving data structure is disclosed. The method includes receiving a flattened schema comprising a plurality of columns. The method includes receiving a plurality of user inputs from a user. The plurality of user inputs comprises a plurality of identifiers. The method includes defining a sample target hierarchical schema based on user inputs. The method includes preparing a nested hierarchical structure corresponding to the sample target hierarchical schema in a User Interface (UI) representation. The nested hierarchical structure comprises a plurality of nodes with corresponding identifiers and relationships of the plurality of nodes. The method includes performing an Artificial Intelligence (AI) based mapping of columns of the flattened schema to respective nodes of the plurality of nodes of the nested hierarchical structure. The method further includes generating a target hierarchical schema based on AI based mapping.