CN114756602A - Real-time streaming process mining method and system and computer readable storage medium - Google Patents
Real-time streaming process mining method and system and computer readable storage medium Download PDFInfo
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Abstract
The invention provides a real-time streaming flow mining system and a computer readable storage medium, comprising: storing the real-time incremental data; cleaning and preprocessing the real-time incremental data; identifying and saving event log data in the cleaned and preprocessed real-time incremental data, and sending a data updating notice; and carrying out stream type calculation on the event log data pointed by the data updating notification to obtain flow information. The invention only analyzes the data which is increased in real time, concentrates on the process performance and the performance of the newly added data without being influenced by the historical data with huge scale, improves the running speed and the efficiency of process mining, reduces the consumption of computing resources and solves the requirement of enterprises on real-time process analysis in the actual production process.
Description
Technical Field
The present invention relates to computer process mining algorithms, and in particular, to a real-time streaming process mining method and system and a computer readable storage medium.
Background
The process mining is a technical means for extracting process related information from an event log, modeling and analyzing the process related information, thereby improving process transparency and providing information support for a decision maker. The event log is analyzed through the process mining system and the intelligent algorithm thereof, and enterprises can obtain information such as the execution result of the process, the resources and the cost consumed by the process, the number of reworking events in the process and the like. By analyzing the information, the enterprise can better realize the optimization of the process, thereby better realizing the business target of the enterprise.
The main functions of process mining include: 1. flow discovery, namely establishing a flow model through an event log and discovering a bottleneck in flow operation by analyzing the resource consumption of each flow node; 2. consistency check, namely comparing the process model with the event log to find the accuracy of the process model; 3. and (4) optimizing the process, namely, discovering the obtained model by analyzing the process, and optimizing and improving the existing process.
The existing flow mining technology comprises the following steps: 1. recording all events in a next business period by an enterprise, recording the events in an event log, and uploading the events to a corresponding database; 2. the process mining system reads all information in the event log from the database; 3. the flow mining algorithm organizes and models events occurring in the business cycle through certain rules and logics to obtain flow information in the business cycle.
However, in the actual operation process, the historical process information with too early occurrence time cannot reflect the current enterprise process situation, so the enterprise needs to analyze the process information occurring in real time, and the existing process mining process cannot meet the requirement. For example, after deciding to change a process, an enterprise needs to know the impact and changes caused by changing the process in time. However, if the existing flow mining algorithm is used, the complete event log needs to be read. On one hand, as the amount of data increases, reading the entire event log completely consumes more time and computing resources; on the other hand, since the amount of the history data is too large, the newly added data may be regarded as noise removed by the algorithm and not obtain complete information of the newly added data.
Patent document CN103778051A discloses a service flow incremental mining method based on an L-x algorithm, and belongs to the field of service flow mining. The method aims to realize the excavation of the incremental logs through an intelligent excavation technology and avoid the situation that the logs are required to be excavated again after being increased. Firstly, extracting log sequences with high frequency from a service activity track, and then preprocessing the log sequences; analyzing the sequence relation between two adjacent activities according to the query concept of the L-x algorithm, establishing a behavior profile, and establishing an initial model according to a direct dependency relation; then, the fitness and the fitness of the model are compared, the consistency of the sequence relation of adjacent activities of the incremental log sequence and the behavior outline of the initial model is verified, whether the initial model is adjusted or not is judged, and a better model is selected by utilizing the consistency of the behavior outline; and obtaining the optimal business process model until all the incremental log sequences are verified.
The algorithm in CN103778051A mentions incremental log mining, but it has two serious drawbacks, which makes it impossible to apply the algorithm in practice. First, the algorithm relies on the computation and comparison of behavior contours, essentially reducing the amount of single data extraction by a large number of computations. In practical application, because the number of flow variants in real business is large and the number of behavior profiles of the enterprise is large, the consumption of the algorithm for calculating the behavior profile query far exceeds the benefit of the algorithm on incremental data processing, so that the calculation speed of the algorithm is low and the delay is high. At present, the data extraction amount is not a limiting factor in large memory and large-scale data analysis capacity enhancement. Under such technical conditions, the algorithm may reduce the real-time performance of calculating and analyzing the flow data. Second, the algorithm does not incorporate more attributes of time, resources, number of occurrences, etc. of the case into the calculation and analysis. The two drawbacks described above make this algorithm impractical for use in real business scenarios.
Disclosure of Invention
In view of the defects in the prior art, the present invention aims to provide a real-time streaming flow mining method and system and a computer readable storage medium.
The invention provides a real-time streaming flow mining system, which comprises:
database module for real-time deltas M1: storing the real-time incremental data;
the real-time data preprocessing module M2: cleaning and preprocessing the real-time incremental data;
the streaming flow analysis engine module M3: identifying and saving event log data in the cleaned and preprocessed real-time incremental data, and sending a data updating notice;
streaming computation module M4: and carrying out stream type calculation on the event log data pointed by the data updating notification to obtain flow information.
Preferably, the real-time data preprocessing module M2 includes:
reading module M2.1: reading the real-time incremental data from a structured database;
cleaning and completing module M2.2: cleaning event logs in the real-time incremental data, and completing cases lacking partial data in the event logs through history similar cases;
formatting module M2.3: and formatting the data in the event log to obtain formatted data, wherein the formatted data comprises a timestamp.
Preferably, the streaming flow analysis engine module M3 includes:
identification module M3.1: identifying formatted data for storage through any one of a time stamp, a case number and multi-table association, and merging a process updating result in the formatted data into a process result before data increment;
the notification module M3.2: a data update notification is issued after the merging.
Preferably, the streaming calculation module M4 includes:
event sequence update module M4.1: updating event sequence information in the event log according to the case number, wherein the event sequence is a variant;
variant property update module M4.2: and updating the mapping relation between the variants and the cases according to the case numbers, and updating the attributes of the variants to serve as the flow information.
The invention provides a real-time streaming flow mining method, which comprises the following steps:
database step S1 of real-time increments: storing the real-time incremental data;
real-time data preprocessing step S2: cleaning and preprocessing the real-time incremental data;
flow analysis engine step S3: identifying and saving event log data in the cleaned and preprocessed real-time incremental data, and sending a data updating notice;
Streaming calculation step S4: and carrying out stream computing on the event log data pointed by the data updating notification to obtain flow information.
Preferably, the real-time data preprocessing step S2 includes:
reading step S2.1: reading the real-time incremental data from a structured database;
a cleaning and completing step S2.2: cleaning event logs in the real-time incremental data, and completing cases lacking partial data in the event logs through history similar cases;
formatting step S2.3: and formatting the data in the event log to obtain formatted data, wherein the formatted data comprises a timestamp.
Preferably, the streaming flow analysis engine step S3 includes:
identification step S3.1: identifying formatted data for storage through any one of a timestamp, a case number and multi-table association, and merging a process updating result in the formatted data into a process result before data increment;
notification step S3.2: a data update notification is issued after the merging.
Preferably, the streaming calculation step S4 includes:
event sequence update step S4.1: updating event sequence information in the event log according to the case number, wherein the event sequence is a variant;
Variant property updating step S4.2: and updating the mapping relation between the variants and the cases according to the case numbers, and updating the attributes of the variants to serve as the flow information.
Preferably, the attribute of the variant comprises any one or more of the number of variants, sequence order, mean throughput time of variants, resource, number of occurrences;
in the streaming calculation, the calculation result is directly pre-calculated and stored, and the behavior profile is not calculated.
According to the present invention, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, implements the steps of the real-time streaming process mining method.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention only analyzes the data increased in real time, concentrates on the flow performance and the performance of the newly added data without being influenced by the large-scale historical data, improves the satisfaction degree and the market competitiveness of the user, and solves the requirement of enterprises on real-time process analysis in the actual production process
2. The invention improves the running speed and efficiency of the process mining and reduces the consumption of computing resources by using the structured database of real-time increment, the real-time data preprocessing, the distributed database and the distributed computing.
3. The present invention directly pre-computes and stores the computed results without having to compute the behavior profile. Because the behavior contour is not required to be calculated, the method has higher response speed, so that the real-time performance of the method is higher, and the method is more suitable for being applied to actual business scenes by enterprises.
4. The application scenario of the process mining is very special, and the original data result is usually influenced by the data increment of the process mining. In practical application, the particularity causes that the existing mature increment technology cannot be directly used in the application scene, otherwise, the mining result is distorted. The invention deals with the particularity that: the incremental data are calculated and updated in real time, and associated results such as variants, throughput time, resources and the like are achieved, so that the method can be really applied to the scene.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic diagram of the module structure and the working principle of the present invention.
FIG. 2 is a schematic diagram of historical data and real-time incremental data as described in the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 1, the real-time streaming flow mining system provided by the present invention includes: the system comprises a real-time incremental database module M1, a real-time data preprocessing module M2, a streaming flow analysis engine module M3, a streaming calculation module M4 and a visualization module M5. The message is sent between the modules by means including but not limited to message queue, active push, etc.
Database module for real-time deltas M1: storing the newly added data; particularly, a structured database updated in real time is used for storing and providing real-time incremental data; the database adopted by the real-time incremental database module M1 includes but is not limited to a stand-alone database, a distributed database and a memory distributed database;
the real-time data preprocessing module M2: cleaning and preprocessing the real-time incremental data provided by the real-time incremental database module M1, and storing event log data in the cleaned real-time incremental data;
the streaming flow analysis engine module M3: identifying and saving event log data in the washed and preprocessed real-time incremental data stored in the real-time incremental database module M1, and sending a data updating notice; the streaming flow analysis engine module M3 may be embodied in a form including, but not limited to, a distributed memory database, a disk-based distributed database, and stack memory data virtualization.
Streaming calculation module M4: reading the event log data pointed by the data update notification to the streaming flow analysis engine module M3, and performing streaming calculation to obtain flow information; the streaming calculation includes, but is not limited to, specific calculations such as case finding, case updating, case flow related data updating calculation, and global correlation result updating for incremental data.
Visualization module M5: the flow information calculated by the distributed stream calculation module M4 is shown.
In a specific application scenario, the working mechanism of the invention is as follows:
inputting incremental data from a user into the real-time incremental database module M1, as shown in fig. 2, the lower box representing historical data and the upper box representing real-time incremental data; as can be seen, the data volume of the real-time incremental data is significantly less than the data volume of the historical data; in a specific workflow, the real-time incremental data may include, but is not limited to: case number, event name, timestamp.
The real-time incremental database module M1 sends a notification of data update to the real-time data preprocessing module M2;
the real-time data preprocessing module M2 reads the real-time incremental data from the real-time incremental database module M1, performs data preprocessing, and transmits the preprocessed formatted data to the streaming flow analysis engine module M3.
Specifically, the real-time data preprocessing module M2 includes:
reading module M2.1: the real-time incremental data is read from the structured database of real-time incremental database module M1 through various network communications such as FTP or HTTP.
Cleaning and completing module M2.2: and removing noise and dirty data from the event log in the real-time incremental data, and completing the case lacking partial data in the event log through history similar cases.
Formatting module M2.3: and formatting the data in the event log to obtain formatted data, wherein the formatted data comprises a case number, an event name, a date and the like, and the formatted data comprises a timestamp. And by formatting the data, the real-time incremental data meets the requirements of a subsequent storage and calculation module.
The streaming flow analysis engine module M3 includes:
identification module M3.1: identifying formatted data for storage through any one of a timestamp, a case number and multi-table association, and merging a process updating result in the formatted data into a process result before data increment;
notification module M3.2: a data update notification is issued to the streaming module M4 after the merging.
The streaming computation module M4 includes:
event sequence update module M4.1: updating event sequence information in the event log according to the case number, wherein the event sequence is a variant; that is, a time series is a variant;
variant property update module M4.2: updating the mapping relation between the variants and the cases according to the case numbers, and updating the attributes of the variants as the calculation results of the incremental process; wherein the attributes of the variants include, but are not limited to, the number of related variants, sequence order, and mean throughput time of the variants; in actual operation, the streaming computation may adopt real-time update, may set timer update, may update according to a request of a client, or may adopt a combination of any of the three ways, including but not limited to;
the calculation result sending module M4.3: the incremental flow calculation results are sent to the streaming flow analysis engine module M3.
The streaming flow analysis engine module M3 sends the incremental flow calculation result to the visualization module M5, and the visualization module M5 displays the incremental flow calculation result as the flow information on a web page in a flow chart manner.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The invention provides a real-time streaming flow mining method, which comprises the following steps:
database of real-time deltas 1: storing the real-time incremental data; real-time data preprocessing step S2: cleaning and preprocessing the real-time incremental data; streaming flow analysis engine step S3: identifying and saving event log data in the cleaned and preprocessed real-time incremental data, and sending a data updating notice; streaming calculation step S4: and carrying out stream type calculation on the event log data pointed by the data updating notification to obtain flow information.
The real-time data preprocessing step S2 includes: reading step S2.1: reading the real-time incremental data from a structured database; cleaning and completing step S2.2: cleaning event logs in the real-time incremental data, and completing cases lacking partial data in the event logs through history similar cases; formatting step S2.3: and formatting the data in the event log to obtain formatted data, wherein the formatted data comprises a timestamp.
The streaming process analysis engine step S3 includes: identification step S3.1: identifying formatted data for storage through any one of a timestamp, a case number and multi-table association, and merging a process updating result in the formatted data into a process result before data increment; notification step S3.2: a data update notification is issued after the merging.
The streaming calculation step S4 includes: event sequence updating step S4.1: updating event sequence information in the event log according to the case number, wherein the event sequence is a variant; variant property updating step S4.2: and updating the mapping relation between the variants and the cases according to the case numbers, and updating the attributes of the variants to serve as the flow information. The attribute of the variant comprises any one or more of the number of variants, sequence order, mean throughput time of variants, resource, occurrence number; in the streaming calculation, the calculation result is directly pre-calculated and stored, and the behavior profile is not calculated.
According to the present invention, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, implements the steps of the real-time streaming process mining method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A real-time streaming process mining system, comprising:
database module for real-time deltas M1: storing the real-time incremental data;
the real-time data preprocessing module M2: cleaning and preprocessing the real-time incremental data;
the streaming flow analysis engine module M3: identifying and storing the event log data in the real-time incremental data which passes the cleaning and preprocessing, and sending a data updating notice;
streaming calculation module M4: and carrying out stream type calculation on the event log data pointed by the data updating notification to obtain flow information.
2. The real-time streaming process mining system according to claim 1, wherein the real-time data preprocessing module M2 comprises:
reading module M2.1: reading the real-time incremental data from a structured database;
cleaning and completing module M2.2: cleaning event logs in the real-time incremental data, and completing cases lacking partial data in the event logs through history similar cases;
formatting module M2.3: and formatting the data in the event log to obtain formatted data, wherein the formatted data comprises a timestamp.
3. The real-time streaming process mining system of claim 2, wherein the streaming process analysis engine module M3 comprises:
identification module M3.1: identifying formatted data for storage through any one of a timestamp, a case number and multi-table association, and merging a process updating result in the formatted data into a process result before data increment;
the notification module M3.2: a data update notification is issued after the merging.
4. The real-time streaming process mining system according to claim 3, wherein the streaming computation module M4 comprises:
event sequence update module M4.1: updating event sequence information in the event log according to the case number, wherein the event sequence is a variant;
variant property update module M4.2: and updating the mapping relation between the variants and the cases according to the case numbers, and updating the attributes of the variants to serve as the flow information.
5. A real-time streaming flow mining method is characterized by comprising the following steps:
database step S1 of real-time increments: storing the real-time incremental data;
real-time data preprocessing step S2: cleaning and preprocessing the real-time incremental data;
flow analysis engine step S3: identifying and saving event log data in the cleaned and preprocessed real-time incremental data, and sending a data updating notice;
Streaming calculation step S4: and carrying out stream computing on the event log data pointed by the data updating notification to obtain flow information.
6. The real-time streaming process mining method according to claim 5, wherein the real-time data preprocessing step S2 includes:
reading step S2.1: reading the real-time incremental data from a structured database;
cleaning and completing step S2.2: cleaning event logs in the real-time incremental data, and completing cases lacking partial data in the event logs through history similar cases;
formatting step S2.3: and formatting the data in the event log to obtain formatted data, wherein the formatted data comprises a timestamp.
7. The real-time streaming flow mining method according to claim 6, wherein the streaming flow analysis engine step S3 includes:
identification step S3.1: identifying formatted data for storage through any one of a timestamp, a case number and multi-table association, and merging a process updating result in the formatted data into a process result before data increment;
notification step S3.2: a data update notification is issued after the merging.
8. The real-time streaming process mining method according to claim 7, wherein the streaming calculation step S4 includes:
event sequence updating step S4.1: updating event sequence information in the event log according to the case number, wherein the event sequence is a variant;
variant property updating step S4.2: and updating the mapping relation between the variants and the cases according to the case numbers, and updating the attributes of the variants to serve as process information.
9. The real-time streaming process mining system according to claim 4 or the real-time streaming process mining method according to claim 8, wherein the attribute of the variant includes any one or more of the number of variants, sequence order, mean throughput time of variants, resource, number of occurrences;
in the streaming calculation, the calculation result is directly pre-calculated and stored, and the behavior profile is not calculated.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the real-time streaming flow mining method of any of claims 5 to 9.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115525693A (en) * | 2022-09-20 | 2022-12-27 | 山东理工大学 | Incremental event log-oriented process model mining method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103425661A (en) * | 2012-05-15 | 2013-12-04 | 阿里巴巴集团控股有限公司 | Method and system for analyzing website data |
| CN107038162A (en) * | 2016-02-03 | 2017-08-11 | 滴滴（中国）科技有限公司 | Real time data querying method and system based on database journal |
| CN109492012A (en) * | 2018-10-31 | 2019-03-19 | 厦门安胜网络科技有限公司 | A kind of method, apparatus and storage medium of data real-time statistics and retrieval |
| CN111143097A (en) * | 2018-11-03 | 2020-05-12 | 千寻位置网络有限公司 | GNSS positioning service-oriented fault management system and method |
| CN113946557A (en) * | 2021-10-20 | 2022-01-18 | 上海望繁信科技有限公司 | Process mining method and system based on multi-view model |
-
2022
- 2022-05-19 CN CN202210547199.4A patent/CN114756602A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103425661A (en) * | 2012-05-15 | 2013-12-04 | 阿里巴巴集团控股有限公司 | Method and system for analyzing website data |
| CN107038162A (en) * | 2016-02-03 | 2017-08-11 | 滴滴（中国）科技有限公司 | Real time data querying method and system based on database journal |
| CN109492012A (en) * | 2018-10-31 | 2019-03-19 | 厦门安胜网络科技有限公司 | A kind of method, apparatus and storage medium of data real-time statistics and retrieval |
| CN111143097A (en) * | 2018-11-03 | 2020-05-12 | 千寻位置网络有限公司 | GNSS positioning service-oriented fault management system and method |
| CN113946557A (en) * | 2021-10-20 | 2022-01-18 | 上海望繁信科技有限公司 | Process mining method and system based on multi-view model |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115525693A (en) * | 2022-09-20 | 2022-12-27 | 山东理工大学 | Incremental event log-oriented process model mining method and system |
| CN115525693B (en) * | 2022-09-20 | 2024-02-06 | 山东理工大学 | Incremental event log-oriented process model mining method and system |
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