Shanghai Icekredit, Inc.

Zhipan Guo

Lingyun Gu

Wei Wang

Haiquan Li

Chang Liu

Kai Wang

Xiaofeng Zhang

Xuan Wang

Jie Xie

Jianye Liu

Pengfei Xie

Anwei Jiang

Junhong Zheng

Kaiping He

A distributed transaction processing method and system based on a message queue and a database is provided. In the method, a component encapsulation server generates a target compressed package according to the obtained first configuration information of a business request server and the obtained second configuration information of a business execution server, and sends the target compressed package to the business request server and the business execution server, respectively, so that the business request server and the business execution server can decompress and configure the target compressed package to deploy a transaction processing component and a message transmission path. The system includes the component encapsulation server, the business request server and the business execution server. The business request server and the business execution server communicate with each other through the message queue. The component encapsulation server communicates with the business request server and the business execution server, respectively.

Distributed transaction processing method and system based on message queue and database

A big data regression verification method includes: adding first data source information, second data source information, and data feature information to a preset configuration file; when a script running instruction is detected, running a python automation script to establish a first data access channel to a database of a business system and a second data access channel to a database of a big data system based on the first data source information and the second data source information; processing and calculating data in the database of the business system and the database of the big data system according to the data feature information; and determining whether a calculated first result file corresponding to the database of the business system is consistent with a calculated second result file corresponding to the database of the big data system, to verify the data. A big data regression verification apparatus is further provided.

Big data regression verification method and big data regression verification apparatus

A data query method and a data query system are provided. A data query server loads, based on a preset configuration interface, configuration metadata sent by a central cluster server for a target application programming interface (API), to a target storage region being located in a database server and associated with the target API, and loads queryable data associated with the target API to the target storage region. Then, the central cluster server sends a query instruction to the data query server based on query metadata in a data query request sent by a user terminal for the target API. After that, the data query server queries corresponding target query data in the target storage region and sends the target query data to the user terminal through the central cluster server. In this way, a data query service can be provided in a form of an API interface.

Data query method and system

Disclosed are a method and a device for graph data quality verification, which can perform quality verification of the graph data to be processed before importing the graph data to be processed to the target graph database, thereby avoiding generating a target list based on the graph data to be processed with errors. By determining whether there is an outlier in the target list, the abnormal graph data in the graph data to be processed can be detected to ensure the correctness of the graph data to be processed imported into the target graph database. By generating a graph data quality report, it is possible to verify whether the graph data to be processed has errors during the import process.

Method and device for graph data quality verification

Disclosed are a method and a device for processing distributed data. The method includes: integrating and configuring data analysis services of multiple users with different data analysis requirements into a distributed computing engine program to obtain an analysis service data package; configuring a distributed scheduler in the cluster server according to the analysis service data package, and calling the distributed scheduler to monitor a message content transmitted by a message middleware including multiple data analysis services to be executed; and generating a distributed data execution plan according to the message content, and performing distributed scheduling calculation on the distributed data execution plan to obtain a distributed calculation result.

Method and device for processing distributed data solving problem of manual intervention by data analysts

Disclosed are a method and a device for processing multi-source heterogeneous data. The data source to be processed of multi-source heterogeneous data and the field data of the field to be converted under each data source to be processed are determined, then the target standard attribute field of the field to be converted under each data source to be processed in the target data dimension is determined from a pre-configured conversion field library. Then, the fields to be converted under each data source to be processed are converted into corresponding target standard attribute fields, to obtain the field data of the target standard attribute field under each data source to be processed, thereby synthesizing the multi-source heterogeneous standard data of the target data dimension.

Method and device for processing multi-source heterogeneous data

Disclosed are a method of indicator information determination and an apparatus of indicator information determination. Firstly, configuration information in json format is obtained and a query path list is generated based on the configuration information. Secondly, a script file is generated according to a constraint condition of the information nodes corresponding to the query path and stored in a first database server. Then, according to a third identification information of the query request input by a terminal device, a target script file corresponding to a fourth identification information is searched in the first database server, then the target script file is executed in the second database server to obtain a query result.

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Shanghai, China

Zhipan Guo