International Business Machines Corporation

Chu Yun Tong

Bo Chen Zhu

Peng Hui Jiang

Cheng Fang Wang

Miao Liu

Peng Fei Tian

Tian Ming Pan

Cheng Luo

Wen Guo

Yi Xin Song

Al Chakra

Xiao Liang Hu

Ke Wei Wei

Jing Yan Ma

Fan Zhang

Li Cao

Yun Bj Wang

Chi Liu

Guang Ming Zhang

Tian Tian

Hong Wei Jia

Wu Yan

Mo Chi Liu

Ai Ping Feng

Xiao Ming Hu

Xinke Wang

Xinzhe Wang

Jean-François Puget

Qi Wen

Dan Hui Fan

An approach for recommending an application of a software code update. The approach receives components of an executable program. The approach scans the components to divide the executable program into units. The approach creates a logical layer of entry lists, based on the units, for instantiations of the executable program. The approach loads the instantiations of the executable program based on the logical layers. The approach monitors the execution of the instantiations to increment counters based on executions of the units. The approach creates a heatmap of the executions based on the counters. The approach generates a recommendation to update the executable based on the heatmap.

Program code update recommendation based on a heatmap

A system may port a data model into a strict schema system, translate the data model into a transformation rule definition, fit the transformation rule definition to a transform action, receive strict schema data, perform the transform action on the strict schema data based on the transformation rule definition to form rough data, and execute filtering and enriching operations on the rough data to form loose schema data.

Transforming data of strict schema structure database

Synching multiple streams in a complex enterprise product by collecting and analyzing stream dependency data. Collection and analysis of data for large scale and complex enterprise results in a multi-dimensional relationship diagram that highlights the interconnected dependencies of the streams. This allows enterprise software users to more easily determine and select which stream (or streams) will help the user to perform a given task.

Syncing streams by intelligent collection and analysis

Aspects of the invention include a computer-implemented method by executing, via a processor, a bottleneck model training process for microservices in a microservice system, wherein for each of the microservices the bottleneck model training process filters out a subset of training data based at least in part on a current situation setting. Building, via the processor, a bottleneck indicator model for each of the microservices using information from the bottleneck model training process, convergence points for an expected response time. Executing, via the processor, a bottleneck identification process for providing system alerts when a bottleneck is identified, wherein the bottleneck identification process uses analysis to monitor a data stream according to the bottleneck indicator model which uses the convergence points for response times.

Enhanced bottleneck analysis at an early stage in a microservice system

A computer-implemented method includes analyzing, by a processing unit, a relational database to discover a plurality of static relationships between a plurality of data fields captured in two or more tables. The processing unit can discover a plurality of entity relationships based on observing application-generated queries and results of accessing the relational database in response to one or more test triggers. The processing unit can build one or more relation graphs based on the static relationships and the entity relationships to link a plurality of nodes with one or more edges that define at least one relationship between the nodes. One or more class graphs are formed having a reduced number of edges than the one or more relation graphs. The processing unit can generate one or more result data graphs using the one or more class graphs as a graph database model of the relational database.

Extraction of relationship graphs from relational databases

Aspects of the invention include a computer-implemented method that receives, by a processor, an ensemble decision tree and generates, by the processor, native code from the ensemble decision tree. The method compiles, by the processor, the native code into machine language and scores, by the processor, the execution time of the native code. The method dynamically reoptimizes, by the processor, portions of the native code corresponding to the most traversed portion of the ensemble decision tree.

Generating native code with dynamic reoptimization for ensemble tree model prediction

Systems and methods for computer-automated labeling of data are disclosed. In embodiments, a method includes: identifying technical data assets in lineage data and corresponding business items in User Interface (UI) data of a user, wherein the lineage data includes a data source for the UI data; mapping the technical data assets to the corresponding business items; determining relevant labels to assign to the technical data assets from a label repository based on a similarity analysis of names of the corresponding business items and labels in the label repository; determining that one or more of the relevant labels meet a confidence threshold based on the similarity analysis; and automatically assigning the one or more of the relevant labels to associated ones of the technical data assets based on the determining that the one or more of the relevant labels meet the confidence threshold.

Automated data labeling

Aspects of the invention include detecting and predicting application performance. A non-limiting example computer-implemented method includes receiving source code and generating a first model of the source code by labeling a word of the source code. The computer implemented method optimizes the first model of the source code by assembling the first model of the source code with a plurality of models generated by a model generation module into a second model of the source code and extracts at least two basic features from the second model of the source code. The computer-implemented method provides an estimated performance of the source code based on historical data of the basic features extracted from the second model of the source code.

Detecting and predicting application performance

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Beijing, China

Chu Yun Tong