Tool and method applied to massive labeled entity data storage

Abstract

The invention discloses a tool and a method applied to massive labeled entity data storage, and belongs to the fields of massive data storage and label data storage. The tool includes a label metadatamodule, an entity label data module and a unified-access API module. A user inputs a username, a password and a request; the unified-access API module accesses the label metadata module according tothe username and the password, and reads metadata of a label; the metadata are packaged according to the user request, are converted into a data format of a data layer, and are transmitted to a corresponding interface of the data layer to execute an operation on label data; the entity label data module executes a corresponding operation according to a request issued by the unified-access API module, and carries out persistence processing on the data; and at the same time, the entity label data module returns a processing result to the unified-access API module, the unified-access API module packages the data, and returns the same to a tool interface according to a specified format. The tool has high pertinency, scalability and persistence, and supports higher-level service needs.

Description

technical field [0001] The invention relates to a tool and a method applied to massive tagged entity data storage, belonging to the fields of massive data storage and tagged data storage. Background technique [0002] In recent years, the domestic Internet business has continued to develop, and mobile Internet technology has continued to mature. However, with the development of business, a large amount of data has accumulated, and the problem of data dispersion has become more and more serious, resulting in a serious weakening of the value of data. Similar to target management, Systems or applications such as automatic intelligent recommendation require complete, highly integrated, accurate, and time-sensitive data as the basis, which makes the problem of how to extract and store high-value entity data more urgent. In this context, applications such as labeling systems and portrait systems have received more and more attention and research. [0003] In actual business scena...

AI Technical Summary

Problems solved by technology

[0003] In actual business scenarios, entity tags do not simply store a tag value, and there are many storage issues related to whether they can support upper-level business, such as: how to flexibly expand the tag system according to the needs of business development? How can I save the attached property of the tag value at the same time? How to set the lifetime of tag value? How to make the tag value have the attribute of confirmation state, so that the unconfirmed tag value will not be published? How to support custom expansion of the tag value dimension? How to store historical versions to support version backtracking? And how to realize the rapid import of massive offline data? Obviously, these problems cannot be solved by traditional storage models, or by simple relational databases.

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