FIELD OF TECHNOLOGY

The present disclosure relates generally to database systems and data processing, and more specifically to epsilon (ε)-closure for frequent pattern (FP) analysis.

BACKGROUND

A cloud platform (i.e., a computing platform for cloud computing) may be employed by many users to store, manage, and process data using a shared network of remote servers. Users may develop applications on the cloud platform to handle the storage, management, and processing of data. In some cases, the cloud platform may utilize a multi-tenant database system. Users may access the cloud platform using various user devices (e.g., desktop computers, laptops, smartphones, tablets, or other computing systems, etc.).

In one example, the cloud platform may support customer relationship management (CRM) solutions. This may include support for sales, service, marketing, community, analytics, applications, and the Internet of Things. A user may utilize the cloud platform to help manage contacts of the user. For example, managing contacts of the user may include analyzing data, storing and preparing communications, and tracking opportunities and sales.

In some cases, the cloud platform may support frequent pattern (FP) analysis for data sets. For example, a data processing machine may determine FPs based on data in a database or data indicated by a user device. However, performing FP analysis on a data set with many common or prevalent data attributes may result in a very large number of resulting FPs. For example, if the data set contains one or more data attributes that correspond to the majority of data objects in the set, these data attributes may be added to almost any pattern with sufficient frequency for the data set, and the resulting “new” pattern will maintain sufficient frequency to also be included in the FP set. However, this “new” pattern may not contain a significant amount of interesting information to differentiate it over the sub-pattern it is built off of (e.g., the same pattern minus the prevalent data attribute). Semi-redundant patterns like these may greatly increase the memory and processing overhead of the FP mining procedure (e.g., based on the increased number of patterns to manage), as well as increasing latency of the FP analysis, without significantly improving the value of the determined FPs.

