In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide data governance functionality such as, for example, provenance (where a particular data came from), lineage (how the data was acquired/processed), security (who was responsible for the data), classification (what is the data about), impact (how impactful is the data to a business), retention (how long should the data live), and validity (whether the data should be excluded/included for analysis/processing), for each slice of data pertinent to a particular snapshot in time; which can then be used in making lifecycle decisions and dataflow recommendations.

A method and hardware system to remove the overhead caused by having stream handling instructions in nested loops. Where code contains inner loops, nested in outer loops, a compiler pass identifies qualified nested streams and generates ISA specific instructions for transferring stream information linking an inner loop stream with an outer loop stream, to hardware components of a co-designed prefetcher. The hardware components include a frontend able to decode and execute instructions for a stream linking information transfer mechanism, a stream engine unit with a streams configuration table (SCT) having a field for allowing a subordinate stream to stay pending for values from its master stream, and a stream prefetch manager with buffers for storing values of current elements of a master stream, and with a nested streams control unit for reconfiguring and iterating the streams.

A data confidence fabric for generating data confidence scores for a build pipeline is disclosed. Confidence scores are generated for data or jobs in a build pipeline. The scores may be combined into a final confidence score that reflects a confidence in the artifact generated by the pipeline and in the pipeline. A user or infrastructure may or may not perform the artifact based on the associated confidence score.

Systems and methods for generating a platform-agnostic data pipeline via a low code transformation layer are disclosed. The system receives one or more user selections of (i) nodes and (ii) links linking the nodes, indicating a data pipeline architecture of transfer/management/flow of data via a GUI. In response to receiving a user selection to implement the data pipeline, the system automatically identifies/generates a set of code portions, based on one or more software objects (e.g., JSON objects) associated with the user selections indicating the data pipeline architecture. The system then identifies a platform identifier associated with a remote server and generates a set of executable instructions (e.g., a script, executable program, or other file) associated with the data pipeline architecture by using a transformation component. The system then provides the executable instructions to the remote server to host the data pipeline.

A system and method to automatically generate a software service to provide service layer functionalities to legacy computing systems that are inherently incompatible with a Service Oriented Architecture (SOA) consumer environment. A configuration specification defining the characteristics of the software service, including data mapping rules is received. Based on the specification, at least one pattern template for the software service is selected from a library of templates. The pattern templates provide source code patterns usable to build the software service. Source code of the software service is outputted using programming code provided in the at least one design pattern template. The outputted source code is packaged or assembled into a source code package for deployment.

Systems, methods, and non-transitory computer readable media are provided for managing pipelines of operations on data. A system may access data and provide a set of functions for the data. The system may receive a user's selection of one or more functions from the set of functions. The system may generate a pipeline of operations for the data based on the user's selection. The pipeline of operations may include the function(s) selected by the user.

A method for improving throughput in a reconfigurable computing system includes detecting, in an algebraic representation of a computing task for a reconfigurable dataflow processor, an outer meta-pipeline loop, detecting an inner meta-pipeline loop nested within the outer meta-pipeline loop, and determining that the inner meta-pipeline loop and the outer meta-pipeline loop each conduct a common operation. The method also includes fusing the common operation for the inner meta-pipeline loop and the outer meta-pipeline loop into a single operation within the inner meta-pipeline loop. The instances of the common operation may be fused if the output of a first instance of the common operation is the source for a second instance of the common operation. Examples of the common operation include an accumulator operation, a re-read operation, and a temporal (chip buffer synchronized) operation such as a temporal concatenation operation and a temporal slicing operation.

Systems, methods, and non-transitory computer readable media are provided for managing pipelines of operations on data. A system may access data and provide a set of functions for the data. The system may receive a user's selection of one or more functions from the set of functions. The system may generate a pipeline of operations for the data based on the user's selection. The pipeline of operations may include the function(s) selected by the user.

A computer-implemented method for integration of machine learning components within a pipelined search query to generate a visualization is described. Herein, an interface is provided for receipt of pipelined code into a web-based programming application. The pipelined code features a series of operators configured to perform one or more tasks based on collective operations by the series of operators, wherein a first operator of the series of operators is to receive input data from a selected data source and each remaining operator of the series of operators to receive input based on an output from a preceding operator of the remaining operators. The task(s) performed by the pipelined code generate results including visualizations. The visualization is rendered in a manner that allows the pipelined code to be scrolled to display the pipelined code or the visualization.

The invention comprises (i) a compilation method for automatically converting a single-threaded software program into an application-specific supercomputer, and (ii) the supercomputer system structure generated as a result of applying this method. The compilation method comprises: (a) Converting an arbitrary code fragment from the application into customized hardware whose execution is functionally equivalent to the software execution of the code fragment; and (b) Generating interfaces on the hardware and software parts of the application, which (i) Perform a software-to-hardware program state transfer at the entries of the code fragment; (ii) Perform a hardware-to-software program state transfer at the exits of the code fragment; and (iii) Maintain memory coherence between the software and hardware memories. If the resulting hardware design is large, it is divided into partitions such that each partition can fit into a single chip. Then, a single union chip is created which can realize any of the partitions.