CN113515500A - Visual data processing system and processing method - Google Patents
Visual data processing system and processing method Download PDFInfo
- Publication number
- CN113515500A CN113515500A CN202110563262.9A CN202110563262A CN113515500A CN 113515500 A CN113515500 A CN 113515500A CN 202110563262 A CN202110563262 A CN 202110563262A CN 113515500 A CN113515500 A CN 113515500A
- Authority
- CN
- China
- Prior art keywords
- task
- task execution
- data
- cleaning
- training
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a visual data processing system and a processing method, wherein the system consists of a foreground visual operation part and a background data processing part, and the method comprises the following steps: s1, defining task execution units, defining the execution sequence of the specific task execution units according to specific task requirements, and forming task execution rules; and S2, calling the corresponding task execution unit according to the task execution rule to obtain and store the task execution result. The invention realizes effective control of data processing flow in a visual and self-defined mode, the whole operation process is simple and visual, the development period is greatly shortened, precious technical personnel resources in enterprises are saved, and the production efficiency and the actual output of the enterprises are improved.
Description
Technical Field
The invention relates to a data processing system and a data processing method, in particular to a comprehensive and visual data processing system and a data processing method applying the same, and belongs to the technical field of big data processing.
Background
Big data is a concept which has been widely noticed, discussed and researched more and more recently, and mainly refers to a data collection whose content cannot be captured, managed and processed by a conventional software tool within a certain period of time. The big data technology is a technology for rapidly obtaining valuable information from various types of big data. Technologies suitable for big data include a Massively Parallel Processing (MPP) database, a data mining grid, a distributed file system, a distributed database, a cloud computing platform, the internet, an extensible storage system, and the like.
It can be considered that effective utilization of big data in the industry is still a technical pain point at present, and how to effectively integrate and utilize data in a database according to the needs of different enterprises or different projects of the same enterprise to obtain the expected effect is a difficult problem which puzzles researchers in the industry.
In the actual application process at the present stage, a business person in an enterprise generally needs to provide a specific project requirement for processing big data, and then a developer in the enterprise evaluates and develops a system according to the project requirement, wherein the whole development period is as short as several hours, and the whole development period is as long as several days or weeks. Once communication between the business personnel and the developers is not smooth, and the developers have deviation in understanding, the system needs to be reversed and redeveloped. Obviously, for enterprises, the above operation flow undoubtedly causes great waste of resources, and severely restricts the production efficiency and actual output of the enterprises.
In summary, how to provide a comprehensive and visual data processing system and a data processing method using the same based on the prior art to overcome many defects in the prior art is a problem to be solved by researchers in the industry.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, an object of the present invention is to provide a comprehensive and visualized data processing system and a data processing method using the same, which are described in detail below.
A visualized data processing system for implementing large data processing, comprising:
the foreground visual operation part is used for defining a task execution unit, defining the execution sequence of the task execution unit according to specific task requirements, forming a task execution rule and sending the task execution rule;
the background data processing part is in signal connection with the foreground visual operation part and is used for receiving the task execution rule, calling the task execution unit according to the task execution rule, and obtaining and storing a task execution result;
the foreground visual operation part specifically comprises a display part,
the cleaning task execution units are used for defining specific data cleaning task operations and storing the operations in a modularized form;
the modeling task execution units are used for defining specific data cleaning task operations and storing the operations in a modularized form, and are mutually independent;
the task input unit is used for defining the execution sequence of the cleaning task execution unit and the modeling task execution unit according to specific task requirements, forming the task execution rule and sending the task execution rule;
the background data processing part specifically comprises a background data processing part,
the task receiving unit is in signal connection with the task input unit and is used for receiving the task execution rule;
the task analysis and judgment unit is in signal connection with the task receiving unit and is used for analyzing the task execution rule, judging whether the task execution rule is effective or not and executing subsequent operation according to a judgment result;
the task chain forming and executing unit is in signal connection with the task analyzing and judging unit and is also in signal connection with the cleaning task executing units and the modeling task executing units respectively, and when the task analyzing and judging unit judges that the task executing rules are valid, the cleaning task executing units and the modeling task executing units are called in sequence according to the task executing rules to obtain and send the task executing results;
and the task result storage unit is in signal connection with the task chain forming and executing unit and is used for storing and recording the task executing rule and the task executing result.
Preferably, a plurality of the cleaning task execution units are independent of each other;
each of the cleaning task performing units includes,
the cleaning object input module is used for defining a data set object needing data cleaning;
the cleaning process definition module is in signal connection with the cleaning object input module and is used for defining a specific data cleaning process;
and the cleaning result export module is in signal connection with the cleaning process definition module and is used for carrying out data cleaning on the data set object according to the data cleaning process to obtain and output a data cleaning result.
Preferably, a plurality of the modeling task execution units are independent of each other, and each modeling task execution unit comprises a model training subunit and a model application subunit;
the model training subunit includes a model training sub-unit,
the training set selection module is used for selecting a training data set;
the training set preprocessing module is in signal connection with the training set selection module and is used for carrying out data preprocessing operation on the training data set;
the training model building module is in signal connection with the training set preprocessing module and is used for forming a data processing model according to the preprocessed training data set by combining an algorithm and parameters;
the model application subunit includes a model application sub-unit,
the data set selection module is used for selecting a task training set;
the data set preprocessing module is in signal connection with the data set selection module and is used for preprocessing data of the task training set;
and the modeling result derivation unit is in signal connection with the data set preprocessing module and is used for obtaining and outputting a data modeling processing result by combining the data processing model according to the preprocessed task training set.
A visualized data processing method based on the visualized data processing system as above, comprising the following steps:
s1, defining task execution units, defining the execution sequence of the specific task execution units according to specific task requirements, and forming task execution rules;
s2, calling the corresponding task execution unit according to the task execution rule to obtain and store a task execution result;
s1 specifically includes the following steps,
s11, defining specific data cleaning task operation, storing the operation into a cleaning task execution unit in a modularized mode, and ensuring that a plurality of cleaning task execution units are mutually independent;
s12, defining specific data modeling task operation, storing the operation into a modeling task execution unit in a modularized form, and ensuring that a plurality of modeling task execution units are mutually independent;
s13, defining the execution sequence of the cleaning task execution unit and the modeling task execution unit according to specific task requirements, forming and sending the task execution rules;
s2 specifically includes the following steps,
s21, receiving the task execution rule;
s22, analyzing the task execution rule, judging whether the task execution rule is valid, if the judgment result is that the task execution rule is valid, executing S23 as required, and if the judgment result is that the task execution rule is invalid, reporting an error and ending the subsequent flow;
s23, calling the cleaning task execution unit and the modeling task execution unit in sequence according to the task execution rule, and after the operation process is executed in sequence, obtaining and sending the task execution result;
and S24, storing and recording the task execution rule and the task execution result, and storing the data processing models together if the data processing models are involved in the task execution process.
Preferably, S11 specifically includes the following steps:
s111, defining a data set object needing data cleaning, wherein the source of the data set object can be a file type database or a relational database or a message queue;
s112, defining a specific data cleaning process, wherein the cleaning process comprises the steps of removing weight, filling a mean value, filling a null value and deleting data;
and S113, carrying out data cleaning on the data set object in the data cleaning process, and optionally carrying out aggregation or space-time collision on the cleaned result to obtain and output a data cleaning result.
Preferably, S12 includes a model training sub-step and a model application sub-step performed in sequence;
the model training sub-step specifically comprises,
s121, selecting a training data set, wherein the training data set can be a file or a database table, and the training data set must contain a characteristic column required by training;
s122, performing data preprocessing operation on the training data set;
s123, selecting an algorithm and setting parameters according to the preprocessed training data set, forming a data processing model and storing the data processing model, wherein the parameters comprise training and testing data set proportion, iteration times, tree depth, classification quantity and regularization parameters;
the model application sub-step specifically comprises,
s124, selecting a task training set;
s125, performing data preprocessing operation on the task training set;
and S126, according to the preprocessed task training set, combining the data processing model and selecting a characteristic column (which is required to be consistent with the characteristic column in the process of training the model) or a column required to be processed according to the model to obtain a data modeling processing result and outputting the data modeling processing result to a file or a relational database.
Compared with the prior art, the invention has the advantages that:
the visual data processing system provided by the invention realizes effective control of a data processing flow in a visual and self-defined mode, the whole operation process is simple and visual, and service personnel who are not familiar with the technology can also finish the system construction in a targeted manner according to specific project requirements, thereby greatly shortening the development period, saving precious technical personnel resources in enterprises and improving the production efficiency and actual output of the enterprises.
Correspondingly, the visual data processing method provided by the invention efficiently realizes the cleaning and modeling work of the big data, and the automation degree and the integration degree in the whole method flow are high. The method can also fully meet the requirements of different enterprises or different projects of the same enterprise, and has wide application range and strong adaptability.
In addition, the invention also provides reference for other related problems in the same field, can be expanded and extended on the basis of the reference, is applied to other technical schemes related to big data processing, and has very wide application prospect.
The following detailed description of the embodiments of the present invention is provided in connection with the accompanying drawings for the purpose of facilitating understanding and understanding of the technical solutions of the present invention.
Drawings
FIG. 1 is a schematic diagram of a system architecture according to the present invention.
Detailed Description
The invention provides a comprehensive and visual data processing system and a data processing method using the same.
As shown in FIG. 1, the present invention discloses a visualized data processing system, for implementing the processing of big data, comprising:
the foreground visual operation part is used for defining a task execution unit, defining the execution sequence of the task execution unit according to specific task requirements, forming a task execution rule and sending the task execution rule;
and the background data processing part is in signal connection with the foreground visual operation part and is used for receiving the task execution rule, calling the task execution unit according to the task execution rule, and obtaining and storing a task execution result.
The foreground visualization operation part specifically comprises:
the cleaning task execution units are used for defining specific data cleaning task operations and storing the operations in a modularized form;
the modeling task execution units are used for defining specific data cleaning task operations and storing the operations in a modularized form, and are mutually independent;
and the task input unit is used for defining the execution sequence of the cleaning task execution unit and the modeling task execution unit according to specific task requirements, forming the task execution rule and sending the task execution rule.
The background data processing part specifically comprises:
the task receiving unit is in signal connection with the task input unit and is used for receiving the task execution rule;
the task analysis and judgment unit is in signal connection with the task receiving unit and is used for analyzing the task execution rule, judging whether the task execution rule is effective or not and executing subsequent operation according to a judgment result;
the task chain forming and executing unit is in signal connection with the task analyzing and judging unit and is also in signal connection with the cleaning task executing units and the modeling task executing units respectively, and when the task analyzing and judging unit judges that the task executing rules are valid, the cleaning task executing units and the modeling task executing units are called in sequence according to the task executing rules to obtain and send the task executing results;
and the task result storage unit is in signal connection with the task chain forming and executing unit and is used for storing and recording the task executing rule and the task executing result.
It is emphasized that a plurality of the cleaning task execution units are independent of each other; and each cleaning task execution unit comprises:
the cleaning object input module is used for defining a data set object needing data cleaning;
the cleaning process definition module is in signal connection with the cleaning object input module and is used for defining a specific data cleaning process;
and the cleaning result export module is in signal connection with the cleaning process definition module and is used for carrying out data cleaning on the data set object according to the data cleaning process to obtain and output a data cleaning result.
Similarly, the modeling task execution units are mutually independent, and each modeling task execution unit comprises a model training subunit and a model application subunit.
The model training subunit includes:
the training set selection module is used for selecting a training data set;
the training set preprocessing module is in signal connection with the training set selection module and is used for carrying out data preprocessing operation on the training data set;
and the training model construction module is in signal connection with the training set preprocessing module and is used for forming a data processing model according to the preprocessed training data set by combining an algorithm and parameters.
The model application subunit includes:
the data set selection module is used for selecting a task training set;
the data set preprocessing module is in signal connection with the data set selection module and is used for preprocessing data of the task training set;
and the modeling result derivation unit is in signal connection with the data set preprocessing module and is used for obtaining and outputting a data modeling processing result by combining the data processing model according to the preprocessed task training set.
In summary, the visualized data processing system provided by the invention realizes effective control of the data processing flow in a visualized and self-defined manner, the whole operation process is simple and intuitive, and business personnel who are not familiar with the technology can also complete the system construction in a targeted manner according to specific project requirements, thereby greatly shortening the development period, saving precious technical personnel resources in enterprises, and improving the production efficiency and actual output of the enterprises.
The invention also discloses a visualized data processing method, which is based on the visualized data processing system and comprises the following steps:
s1, defining task execution units, defining the execution sequence of the specific task execution units in convenient operation modes such as dragging and the like according to specific task requirements, and forming task execution rules;
and S2, calling the corresponding task execution unit according to the task execution rule to obtain and store a task execution result.
S1 specifically includes the following steps:
s11, defining specific data cleaning task operation, storing the operation into a cleaning task execution unit in a modularized mode, and ensuring that a plurality of cleaning task execution units are mutually independent;
s12, defining specific data modeling task operation, storing the operation into a modeling task execution unit in a modularized form, and ensuring that a plurality of modeling task execution units are mutually independent;
s13, defining the execution sequence of the cleaning task execution unit and the modeling task execution unit according to specific task requirements, forming and sending the task execution rules; data cleaning or data modeling tasks may be added here, and information such as task names, classifications, task descriptions, etc. may be specified.
S2 specifically includes the following steps:
s21, receiving the task execution rule;
s22, analyzing the task execution rule, judging whether the task execution rule is valid, if the judgment result is that the task execution rule is valid, executing S23 as required, and if the judgment result is that the task execution rule is invalid, reporting an error and ending the subsequent flow;
s23, calling the cleaning task execution unit and the modeling task execution unit in sequence according to the task execution rule, and after the operation process is executed in sequence, obtaining and sending the task execution result;
and S24, storing and recording the task execution rule and the task execution result, and storing the data processing models together if the data processing models are involved in the task execution process.
Further, S11 specifically includes the following steps:
s111, defining a data set object needing data cleaning, wherein the source of the data set object can be a file type database or a relational database or a message queue; each operator in the data set object is provided with type and specific parameter information;
s112, defining a specific data cleaning process, wherein the cleaning process can comprise specific cleaning operations such as de-weighting according to a plurality of columns, filling a field mean value, filling null values, deleting columns and the like, and the dependency relationship and the execution sequence of each cleaning operator;
s113, the data cleaning process carries out data cleaning on the data set object, and the cleaned result can be selectively aggregated or subjected to space-time collision according to actual requirements to obtain a data cleaning result, or the data cleaning result can be directly obtained and output; the data cleansing results described herein may also be files, relational databases, or message queues.
Further, S12 includes a model training sub-step and a model application sub-step that are performed sequentially.
The model training substep specifically comprises:
s121, selecting a training data set, wherein the training data set can be a file or a database table; it is emphasized that the training data set must include a feature column required for training, and optionally a labeled tag column;
s122, performing data preprocessing operation on the training data set, wherein the operation is optional, selecting a feature column and a label column to be trained (part of algorithms do not need the label column) when the operation is performed, and extracting feature values according to the feature column by a modeling algorithm;
s123, selecting an algorithm and setting parameters according to the preprocessed training data set, forming a data processing model and storing the data processing model, wherein the parameters comprise training and testing data set proportion, iteration times, tree depth, classification quantity and regularization parameters.
The model application substep specifically comprises:
s124, selecting a task training set;
s125, performing data preprocessing operation on the task training set;
and S126, obtaining and outputting a data modeling processing result by combining the data processing model according to the preprocessed task training set.
Corresponding to the system scheme, the visualized data processing method provided by the invention efficiently realizes the cleaning and modeling work of the big data, and the automation degree and the integration degree in the whole method flow are high. The method can also fully meet the requirements of different enterprises or different projects of the same enterprise, and has wide application range and strong adaptability.
In addition, the invention also provides reference for other related problems in the same field, can be expanded and extended on the basis of the reference, is applied to other technical schemes related to big data processing, and has very wide application prospect.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference thereto is therefore intended to be embraced therein.
Finally, it should be understood that although the present description refers to embodiments, not every embodiment contains only a single technical solution, and such description is for clarity only, and those skilled in the art should integrate the description, and the technical solutions in the embodiments can be appropriately combined to form other embodiments understood by those skilled in the art.
Claims (6)
1. A visualized data processing system for implementing large data processing, comprising:
the foreground visual operation part is used for defining a task execution unit, defining the execution sequence of the task execution unit according to specific task requirements, forming a task execution rule and sending the task execution rule;
the background data processing part is in signal connection with the foreground visual operation part and is used for receiving the task execution rule, calling the task execution unit according to the task execution rule, and obtaining and storing a task execution result;
the foreground visual operation part specifically comprises a display part,
the cleaning task execution units are used for defining specific data cleaning task operations and storing the operations in a modularized form;
the modeling task execution units are used for defining specific data cleaning task operations and storing the operations in a modularized form, and are mutually independent;
the task input unit is used for defining the execution sequence of the cleaning task execution unit and the modeling task execution unit according to specific task requirements, forming the task execution rule and sending the task execution rule;
the background data processing part specifically comprises a background data processing part,
the task receiving unit is in signal connection with the task input unit and is used for receiving the task execution rule;
the task analysis and judgment unit is in signal connection with the task receiving unit and is used for analyzing the task execution rule, judging whether the task execution rule is effective or not and executing subsequent operation according to a judgment result;
the task chain forming and executing unit is in signal connection with the task analyzing and judging unit and is also in signal connection with the cleaning task executing units and the modeling task executing units respectively, and when the task analyzing and judging unit judges that the task executing rules are valid, the cleaning task executing units and the modeling task executing units are called in sequence according to the task executing rules to obtain and send the task executing results;
and the task result storage unit is in signal connection with the task chain forming and executing unit and is used for storing and recording the task executing rule and the task executing result.
2. A visualized data processing system according to claim 1 wherein: the plurality of cleaning task execution units are mutually independent;
each of the cleaning task performing units includes,
the cleaning object input module is used for defining a data set object needing data cleaning;
the cleaning process definition module is in signal connection with the cleaning object input module and is used for defining a specific data cleaning process;
and the cleaning result export module is in signal connection with the cleaning process definition module and is used for carrying out data cleaning on the data set object according to the data cleaning process to obtain and output a data cleaning result.
3. A visualized data processing system according to claim 1 wherein: the modeling task execution units are mutually independent, and each modeling task execution unit comprises a model training subunit and a model application subunit;
the model training subunit includes a model training sub-unit,
the training set selection module is used for selecting a training data set;
the training set preprocessing module is in signal connection with the training set selection module and is used for carrying out data preprocessing operation on the training data set;
the training model building module is in signal connection with the training set preprocessing module and is used for forming a data processing model according to the preprocessed training data set by combining an algorithm and parameters;
the model application subunit includes a model application sub-unit,
the data set selection module is used for selecting a task training set;
the data set preprocessing module is in signal connection with the data set selection module and is used for preprocessing data of the task training set;
and the modeling result derivation unit is in signal connection with the data set preprocessing module and is used for obtaining and outputting a data modeling processing result by combining the data processing model according to the preprocessed task training set.
4. A visualized data processing method based on the visualized data processing system according to any one of claims 1 to 3, comprising the steps of:
s1, defining task execution units, defining the execution sequence of the specific task execution units according to specific task requirements, and forming task execution rules;
s2, calling the corresponding task execution unit according to the task execution rule to obtain and store a task execution result;
s1 specifically includes the following steps,
s11, defining specific data cleaning task operation, storing the operation into a cleaning task execution unit in a modularized mode, and ensuring that a plurality of cleaning task execution units are mutually independent;
s12, defining specific data modeling task operation, storing the operation into a modeling task execution unit in a modularized form, and ensuring that a plurality of modeling task execution units are mutually independent;
s13, defining the execution sequence of the cleaning task execution unit and the modeling task execution unit according to specific task requirements, forming and sending the task execution rules;
s2 specifically includes the following steps,
s21, receiving the task execution rule;
s22, analyzing the task execution rule, judging whether the task execution rule is valid, if the judgment result is that the task execution rule is valid, executing S23 as required, and if the judgment result is that the task execution rule is invalid, reporting an error and ending the subsequent flow;
s23, calling the cleaning task execution unit and the modeling task execution unit in sequence according to the task execution rule, and after the operation process is executed in sequence, obtaining and sending the task execution result;
and S24, storing and recording the task execution rule and the task execution result, and storing the data processing models together if the data processing models are involved in the task execution process.
5. The visualized data processing method according to claim 4, wherein S11 specifically comprises the following steps:
s111, defining a data set object needing data cleaning, wherein the source of the data set object can be a file type database or a relational database or a message queue;
s112, defining a specific data cleaning process, wherein the cleaning process comprises the steps of removing weight, filling a mean value, filling a null value and deleting data;
and S113, carrying out data cleaning on the data set object in the data cleaning process, and optionally carrying out aggregation or space-time collision on the cleaned result to obtain and output a data cleaning result.
6. A visualized data processing method according to claim 4, wherein S12 comprises a model training sub-step and a model application sub-step performed in sequence;
the model training sub-step specifically comprises,
s121, selecting a training data set, wherein the training data set can be a file or a database table, and the training data set must contain a characteristic column required by training;
s122, performing data preprocessing operation on the training data set;
s123, selecting an algorithm and setting parameters according to the preprocessed training data set, forming a data processing model and storing the data processing model, wherein the parameters comprise training and testing data set proportion, iteration times, tree depth, classification quantity and regularization parameters;
the model application sub-step specifically comprises,
s124, selecting a task training set;
s125, performing data preprocessing operation on the task training set;
and S126, obtaining and outputting a data modeling processing result by combining the data processing model according to the preprocessed task training set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110563262.9A CN113515500B (en) | 2021-05-24 | 2021-05-24 | Visual data processing system and processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110563262.9A CN113515500B (en) | 2021-05-24 | 2021-05-24 | Visual data processing system and processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113515500A true CN113515500A (en) | 2021-10-19 |
| CN113515500B CN113515500B (en) | 2023-06-30 |
Family
ID=78064827
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110563262.9A Active CN113515500B (en) | 2021-05-24 | 2021-05-24 | Visual data processing system and processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113515500B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7451403B1 (en) * | 2002-12-20 | 2008-11-11 | Rage Frameworks, Inc. | System and method for developing user interfaces purely by modeling as meta data in software application |
| EP2634733A1 (en) * | 2012-03-01 | 2013-09-04 | Fujitsu Limited | Operations task management system and method |
| CN110245158A (en) * | 2019-06-10 | 2019-09-17 | 上海理想信息产业（集团）有限公司 | A kind of multi-source heterogeneous generating date system and method based on Flink stream calculation technology |
| CN110909039A (en) * | 2019-10-25 | 2020-03-24 | 北京华如科技股份有限公司 | Big data mining tool and method based on drag type process |
| CN111339375A (en) * | 2020-03-19 | 2020-06-26 | 中国海洋石油集团有限公司 | Universal big data model configuration and analysis method |
| CN111694888A (en) * | 2020-06-12 | 2020-09-22 | 谷云科技(广州)有限责任公司 | Distributed ETL data exchange system and method based on micro-service architecture |
| CN112732687A (en) * | 2020-12-29 | 2021-04-30 | 中国民用航空飞行学院 | Aviation flight data visualization processing system and analysis method based on data cleaning |
-
2021
- 2021-05-24 CN CN202110563262.9A patent/CN113515500B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7451403B1 (en) * | 2002-12-20 | 2008-11-11 | Rage Frameworks, Inc. | System and method for developing user interfaces purely by modeling as meta data in software application |
| EP2634733A1 (en) * | 2012-03-01 | 2013-09-04 | Fujitsu Limited | Operations task management system and method |
| CN110245158A (en) * | 2019-06-10 | 2019-09-17 | 上海理想信息产业（集团）有限公司 | A kind of multi-source heterogeneous generating date system and method based on Flink stream calculation technology |
| CN110909039A (en) * | 2019-10-25 | 2020-03-24 | 北京华如科技股份有限公司 | Big data mining tool and method based on drag type process |
| CN111339375A (en) * | 2020-03-19 | 2020-06-26 | 中国海洋石油集团有限公司 | Universal big data model configuration and analysis method |
| CN111694888A (en) * | 2020-06-12 | 2020-09-22 | 谷云科技(广州)有限责任公司 | Distributed ETL data exchange system and method based on micro-service architecture |
| CN112732687A (en) * | 2020-12-29 | 2021-04-30 | 中国民用航空飞行学院 | Aviation flight data visualization processing system and analysis method based on data cleaning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113515500B (en) | 2023-06-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110516325B (en) | CAE automatic simulation analysis method and system | |
| CN110909039A (en) | Big data mining tool and method based on drag type process | |
| CN113741883B (en) | RPA lightweight data middling station system | |
| CN107679549A (en) | Generate the method and system of the assemblage characteristic of machine learning sample | |
| CN112347071A (en) | Power distribution network cloud platform data fusion method and power distribution network cloud platform | |
| CN105912588A (en) | Visualization processing method and system for big data based on memory calculations | |
| CN112115196A (en) | Industrial big data visual modeling method and system | |
| CN115981902A (en) | Fine-grained distributed micro-service system abnormal root cause positioning method and device | |
| CN111489135A (en) | System and method for analyzing and managing audit data | |
| CN113535831A (en) | Report form analysis method, device, equipment and medium based on big data | |
| CN112633850A (en) | Method for realizing service flow automation by managing, calling and monitoring RPA robot | |
| CN113515500B (en) | Visual data processing system and processing method | |
| CN110891083B (en) | Agent method for supporting multi-job parallel execution in Gaia | |
| CN116822926A (en) | Delay statistics and analysis method and device, electronic equipment and storage medium | |
| Basnet et al. | Experiences in developing an object-oriented modeling environment for manufacturing systems | |
| CN111311352A (en) | Goods source matching method, system, equipment and storage medium | |
| CN107122849B (en) | Spark R-based product detection total completion time minimization method | |
| Bohács et al. | Production logistics simulation supported by process description languages | |
| CN114661571A (en) | Model evaluation method, model evaluation device, electronic equipment and storage medium | |
| CN103942235A (en) | Distributed computation system and method for large-scale data set cross comparison | |
| CN111290855A (en) | GPU card management method, system and storage medium for multiple GPU servers in distributed environment | |
| CN110908789B (en) | Visual data configuration method and system for multi-source data processing | |
| CN116012019B (en) | Financial wind control management system based on big data analysis | |
| US20220101238A1 (en) | Computer implemented method of capturing and processing data from user activities in business processes and a computer program to implement this method | |
| CN115455069A (en) | Intelligent data statistical method based on RPA machine electric power |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |