International Business Machines Corporation

Chun H Sun

Wei Wang

Xu Bin Cai

Yi Wang

Xiaobo Wang

Sriram Kolinjivadi Padmanabhan

Brian K Caufield

Mi Wan Shum

Ron E Liu

DongJie Wei

Yi Yang Ren

Jian Ling Shi

Chun Leng

Pin Lv

A computer-implemented method may include generating a rule operator comprising a rule table; generating a rule operator group comprising rule operators containing similar rule tables; communicating the rule operator group to a shared compute node; retrieving table data associated with the rule table of a first rule operator within the rule operator group; reusing the table data associated with the first rule operator in a first runtime of additional rule operators corresponding to the rule operator group; determining at least one checker metric associated with a second runtime of a data rule; determining a workload state for the shared compute node based on the at least one checker metric; determining time taken and resource usage for a data rule during the second runtime; and balancing a workload of each compute node based on the workload state, time taken, and resource usage for the data rule during the second runtime.

Data rule workload distribution

Performing a mutual exclusion data class analysis is provided. A data class group of a plurality of data class groups that a matching data class is a member of is identified. The matching data class matches data in a plurality of rows of a column in a data asset. Data classes included in the data class group that the matching data class is a member of are identified. A mutual exclusion data class is filtered from the data class group to form a filtered data class group for the column. The filtered data class group is run against the column of the data asset decreasing processing time and resource utilization of a computer.

Mutual exclusion data class analysis in data governance

Systems and methods are provided for automated classification of data using fingerprints. In embodiments, a method includes: generating, by a computing device based on predetermined rules, a fingerprint of a data column in a data set to be classified, the fingerprint comprising dimensions, wherein each of the dimension is assigned an attribute representing a characteristic of data in the data column; determining, by the computing device, that the fingerprint matches one or more target fingerprints by comparing the fingerprint to the target fingerprints, wherein each target fingerprint is associated with a class and includes dimensions, and each dimension is assigned an attribute representing a characteristic of data in the class; and assigning, by the computing device, one or more classes to the data column based on the one or more target fingerprints, thereby generating classified data.

Fingerprint-based data classification

Provided are techniques for multiple stage workload management. A staging queue and a run queue are provided. A workload is received. In response to determining that application resources are not available and that the workload has not been previously semi-started, the workload is added to the staging queue. In response to determining that the application resources are not available and that the workload has been semi-started, and, in response to determining that run resources are available, the workload is started. In response to determining that the application resources are not available and that the workload has been semi-started, and, in response to determining that the run resources are not available, adding the workload to the run queue.

Multiple stage workload management system

Growing community of inventors

Beijing, China

Chun H Sun