International Business Machines Corporation

Ling Zhuo

Yuan Feng

Yue Yang

Pei Pei Liang

Jing James Xu

Jun Wang

Yi Dai

Yan Yan Han

Ke Wei Wei

Yin Xia

Xue Ying Zhang

Ying Cao

Guang Ming Zhang

Tao Wen

Shuang Ys Yu

Li Yi Zhou

Yun Bo Zhang

Lin Yan Wu

Su Li Hou

Tian Jiao Zhang

Qi Min

Yan Hao Zhang

Enzhong Wang

Computer-implemented methods for a database schema relationship analysis system are disclosed herein. Aspects include receiving a table name associated with a modified table entity in a database of an application. Aspects further include generating an application documentation table association tree using data from application documentation of the application. Aspects also include generating a source code table association tree using a set of data from source code of the application. Aspects further include determining a similarity level by comparing the application documentation table association tree and the source code table association tree. Aspects include generating a message that includes a suggestion to modify a table entity associated with the modified table entity using data from the application documentation table association tree and the source code table association tree in response to determining that the similarity level is within a threshold value.

Database schema relationship analysis system

A computer-implemented method, system and computer program product for recommending join operations of relational data among different tables. Relationships between one or more pairs of columns of relational data for one or more pairs of tables are identified by determining the semantic similarity between each pair of columns of relational data. The data content join converge for each of the identified relationships in connection with joining the tables involved in such identified relationships is determined. Furthermore, a join strength (indication of the degree that the relational data in the paired columns match) is calculated for each of the identified relationships based on the semantic similarity and the data content join coverage for such identified relationships. Based on the calculated join strength as well as other factors, a combination optimization algorithm identifies the best join combinations of relational data of the tables among a set of tables.

Recommending join operations of relational data among tables based on optimization model

An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

Intelligent expansion of reviewer feedback on training data

An approach is disclosed that determines an amount of time before a webpage is ready to use by a user by performing various actions. The approach captures a recording of the webpage from an invocation of the webpage for a period of time sufficient to load completely load the webpage with the capturing resulting in sequenced image frames. An AI system provides a loading point in the sequenced image frames based on an analysis of the frames input to the trained AI system. Image diversity and saturation measurements are calculated on consecutive image frames from the sequenced image frames resulting in an image change analysis. Native webpage events and times are detected from webpage characteristics gathered from the captured digital recording. The amount of time is then calculated based on the loading point from the AI system, the image change analysis; and the webpage events and their corresponding times.

Measure GUI response time

An approach is provided in which the approach captures a first user activity log of a first user accessing multiple systems and captures a set of second user activity logs of a set of second users accessing the multiple systems. The approach determines a set of system monitoring preferences based the first user activity log and the set of second user activity logs, and scores the multiple systems based on the set of system monitoring preferences. The approach generates a recommended system monitoring list based on the scored multiple systems, and transmits the recommended system monitoring list to the first user.

Recommend target systems for operator to attention in monitor tool

Ranking system metrics for monitoring by sorting members of a set of system metrics into correlation groups according to correlations among historic time series data, determining a sensitivity of the members of the set of system metrics, determining an importance of the members of the set of system metrics according to the correlation groups and sensitivity, and ranking the members of the set of system metrics according to the importance.

Selecting metrics for system monitoring

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Beijing, China

Ling Zhuo