In general, embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for software process failure prevention are provided herein. Such embodiments may include steps of: generating error logs at one or more phases of a software process lifecycle; preprocessing each error log to standardize error log data; grouping the preprocessed error logs based on similar characteristics of the preprocessed error logs; associating each group of preprocessed error logs with one or more discrete events of the software process lifecycle; converting each preprocessed error log into a sequence of associated discrete events of the software process lifecycle; merging redundant sequences of associated discrete events of the software process lifecycle; and identifying one or more error causing patterns for software process failure prediction from the sequences of associated discrete events of the software process lifecycle.

A technology is provided for controlling input to a data digest model compiler in a data digest system, comprising: parsing a descriptor of a data structure, the data structure being operable to be emitted by a physical data source device; restructuring the parsed data structure descriptor according to a constrained data paradigm into a formal structure descriptor acceptable by the data digest model compiler as an input to generate a compiled executable operable to process the data content; augmenting the formal structure descriptor with processing directives operable to cause runtime transformation of at least one data content portion of the data structure into a predetermined input parameter form acceptable by a compiled executable generated by the data digest model compiler; and inputting the formal structure descriptor augmented with the processing directives as a data digest model to the data digest model compiler to generate the compiled executable.

A computer-implemented method for provision of data according to a ranking is provided. The method includes establishing an analytical representation of an arrangement of data entities, data sources and network relationships in a computer network; establishing a rule schema for ranking a data entity, a data source and a network relationship according to ranking criteria; storing the rule schema in association with the analytical representation; receiving a request for data, from a data consumer, specifying requested ranking metadata; receiving at least a segment of a data stream; executing a rules engine to apply the stored rule schema to the segment to classify the segment by linking associated ranking metadata with the segment; responsive to the associated ranking metadata matching the requested ranking metadata, populating an output data structure from data in the segment; and transmitting the populated output data structure to the data consumer.

Embodiments of the present disclosure provide a wireless debugger and a wireless debugging system. The wireless debugger includes: a processor, a wireless communication module, and a first peripheral interface; the processor is electrically connected to the wireless communication module and the first peripheral interface, respectively; the processor, is configured to receive debugging instructions through the wireless communication module, and the debugging instructions are used to instruct debugging/stop debugging a target board; the processor, is further configured to parse the debugging instructions and convert the parsed debugging instructions so that the debugging instructions are adapted to a protocol of the first peripheral interface; and the processor, is further configured to transmit the converted debugging instructions to the to-be-debugged target board through the first peripheral interface. Debugging control is convenient and reliable.

A system and a method for predicting errors and failure of an application and performing preventive maintenance measures is disclosed. The present invention provides for generating timestamped event logs and extracting data which includes application Id, service Id, user Id, host, error information, resource information, user request data and frequently used execution sequences of associated software components. Further, multiple user requests are modelled based on information extracted from the timestamped event logs and an execution sequence of associated software components is created based on data extracted from timestamped event logs. Further, an execution scheme is generated based on modelled user requests, execution sequence of associated applications and resource status. Yet further, propensity of failure of application is predicted based on the timestamped event logs, user requests, execution sequence of associated applications, resource status and execution scheme of the application by applying a holistic technique.

The invention relates to a computer-implemented method for processing events. The method provides a database comprising original event objects stored in association with canonical event objects. The method executes a learning algorithm on the associated original and canonical event objects for generating a trained ML program adapted to transform an original event object of any one of the one or more original data formats into a canonical event object having the canonical data format and uses the trained machine learning program for automatically transforming original event objects generated by an active IT-monitoring system into canonical event objects processable by an event handling system.

The method includes receiving event information for a plurality of events from a plurality of data sources; normalizing the event information for each event of the plurality of events into a desired format by at least one of changing a field name of the event information or transforming at least one value in the event information into a recognized value; enriching the event information for each event of the plurality of events by adding additional data points associated with the event information to the event information; and/or transmitting the event information to a data recipient.

An encoder receives an application log file including component values and encodes the component values into lists of preliminary encoded values. The lists of preliminary encoded values are combined into a combined list of preliminary encoded values. An encoder-decoder neural network is trained to encode the combined list of preliminary encoded values into a list of collectively encoded values, to decode the list of collectively encoded values into a list of decoded values, and to optimize a metric measuring the encoder-decoder neural network's functioning, in response to receiving the combined list of preliminary encoded values. The trained encoder-decoder neural network receives combined lists of preliminary encoded values for application log files and encodes the combined lists of preliminary encoded values into lists of collectively encoded values. The lists of collectively encoded values are sent to a detector, thereby enabling the detector to detect an application event associated with the application log files.

Various methods and apparatuses are described for performing high speed format translations of incoming data, where the incoming data is arranged in a delimited data format. As an example, the data in the delimited data format can be translated to a mapped variable field format using pipelined operations. A reconfigurable logic device can be used in exemplary embodiments as a platform for the format translation.

The present disclosure provides a method, a system, a device for processing operation and maintenance data. The method includes acquiring a plurality of types of the operation and maintenance data of a target server, converting the operation and maintenance data into quantized data of each dimension according to a preset rule, and determining quality evaluation information of the target server according to the quantized data of each dimension. The technical solution provided by the present application may improve evaluation precision of product performance.