A system is provided for resolving data errors. The system is configured to obtain an erroneous data element received from a source system and generate a plurality of probable data values for the data element. For each probable data value of the plurality of probable data values, the system verifies whether the probable data value can be processed successfully by an Extract, Transform and Load (ETL) logic and by one or more target applications configured to process the data element. The system determines one of the probable data values as a suggested data value of the data element, based on the verification of the probable data values. The system outputs the suggested data value for processing by the ETL logic.

A method comprising receiving, via a first application and from a client device, a request for a data format conversion of transaction data; responsive to receiving the request, transmitting, via the first application, a verification request comprising an institution identifier and an account identifier to a second application, receiving, via the first application from the second application, verification that the first account identifier is associated with a valid user account; converting, via the first application, a portion of the transaction data from a first format to a second format; transmitting, via the first application, an update request to the second application, receipt of the update request causing the second application to (1) select an established connection with an institution, and (2) initiate an update transaction with the institution based on the converted transaction data; receiving, via the first application, an indication that the update transaction was successful.

Techniques for providing data preview before recalling large data files are disclosed. In one aspect, a data file is made accessible while being offline by converting the data file from a native format to a preview format, storing the data file in the preview format in a primary storage that is locally available and moving, after the conversion to the preview format, the data file in the native format to a secondary storage. When a viewing request is received for the data file, the data file in the preview format is displayed to fulfill the viewing request.

A system that dynamically provides media essence to a client device. An I/O interface receives a media access request that defines a range of media content at a defined format, and a VFS generates an essence containing file that includes a file name and extension that identifies byte ranges for the content range and the defined format; dynamically builds the essence containing file to comprise APIs that can access at the identified byte ranges for the defined range of media content; imposes a structure on a portion of bytes contained in the essence containing file to define a processing function to be executed before delivering the essence to the client device; and opens the essence containing file to access the identified byte ranges using the APIs and causing the processing function to be executed on the accessed and identified byte ranges before delivering the essence to the client device.

A method includes: repeatedly executing a first processing configured to generate, in response to input of conversion examples of values set, a conversion program for converting values of records, convert the values of the records by executing the conversion program, and display a conversion result; and executing a second processing configured to select one or more second records from the records, each of the one or more records being a record on an upper side of a first record, the first record being a record for which a conversion example is added in second or subsequent input, determine whether a value of each second record is changed in a latest conversion result from a previous conversion result, and in response that a value of the second record is changed, cause the first processing to highlight a value of the second record in display of the latest conversion result.

Systems and methods for de-serializing one or more data elements of a serialized structured data record are presented. In response to a request to de-serialize one or more data elements of a serialized structured data record of a first type, the location of the serialized structured data record is determined within a data file containing a plurality of serialized structured data records. Locations of the data of the one or more data elements are determined and the data of the one or more data elements is retrieved. The data is de-serialized and stored in corresponding data elements in an instantiated structured data record of the first type. The one or more data elements that are de-serialized are fewer than all of the data elements of the serialized structured data record.

A method of processing diagnostic data including receiving a template file and a diagnostic file. Processing the diagnostic data based upon the template file where array and non-array portions of the data are treated differently.

The present disclosure relates to platform configured for executing and rendering templates to enable automation of control and data exploration across systems in an interactive and non-interactive mode. Initially, the platform is configured to add template to an exploration either on user request or system driven request and execute the template by extracting execution model associated with the template. Further, the target system is configured to run the execution plan in interactive mode wherein the user may provide inputs or non-interactive mode wherein the target system is configured to run the execution plan without user inputs. The target system is further configured to receive data generated by the target systems in response to the operation executed by the execution engine. On receiving the data from target systems in the data store, the target system is configured to visualize the data on the template rendered on the GUI.

Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: storing, within one or more storage systems, a transformed dataset generated by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers; and transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.

Techniques for deserializing stream objects are disclosed. The system may receive data representing a stream object. The data can include an object descriptor, a class descriptor, and stream field values corresponding to the stream object. The system may select a particular deserialization process, from among a plurality of deserialization processes. The selection may be based at least in part on the object descriptor and the class descriptor. The system can deserialize the data representing the stream object using the selected deserialization process, yielding one or more deserialized objects.