International Business Machines Corporation

Chun Leng

Yi Yang Ren

Jian Ling Shi

Liang Lu

Sun Chun Hua

Yue Wang

Wei Wang

Chun H Sun

Xu Bin Cai

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

Provided is a method, computer program product, and system for classifying a set of data items based on format organizations. A processor may determine at least one format organization of a set of data items. The format organization of a data item indicates a symbol type of at least one continuous symbol in the data item and a number of the at least one continuous symbol. The processor may determine at least one candidate data class for the set of data items from a plurality of predetermined data classes based on the at least one format organization. The processor may classify the set of data items into at least one target data class selected from the at least one candidate data class. In this way, the set of data items can be efficiently classified.

Data classification

A computer-implemented method includes: reading a vector of a first table in a database, the vector including counts of a plurality of keywords in the first table, the plurality of keywords including a first keyword and a second keyword; determining a first custom attribute describing the first table, the first custom attribute having a vector including counts of at least a first portion of the plurality of keywords in the first table; determining a multiplier of the first custom attribute, the multiplier being a number of other tables that reference the first custom attribute; and revising the vector of the first table based on the first custom attribute.

Data governance with custom attribute based asset association

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Beijing, China

Chun Leng