CN109344201A - A kind of database performance load evaluation system and method based on machine learning - Google Patents
A kind of database performance load evaluation system and method based on machine learning Download PDFInfo
- Publication number
- CN109344201A CN109344201A CN201811207264.9A CN201811207264A CN109344201A CN 109344201 A CN109344201 A CN 109344201A CN 201811207264 A CN201811207264 A CN 201811207264A CN 109344201 A CN109344201 A CN 109344201A
- Authority
- CN
- China
- Prior art keywords
- data
- model
- training
- characteristic
- database
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides a kind of database performance load evaluation system and appraisal procedure based on machine learning, performance load learning model is generated using machine learning algorithm training data, by data mart modeling, it is trained using these mass datas as training set and with machine learning techniques, ultimately generate performance and load learning model, newly generated characteristic is predicted using this model, assesses the performance and loading condition of database.On the one hand, the conclusion of the assessment system is more reasonable than analysis of the expert model to feature, will not more omit important performance load index, and it is more accurate to position to the problem of database performance and load；On the other hand, the knowledge and Capability Requirement to database operation maintenance personnel are reduced, human cost can be greatly saved, working efficiency is provided.
Description
Technical field
The present invention is a kind of database performance load evaluation system and method based on machine learning, belongs to artificial intelligence machine
Device learning areas is related to database O&M.
Background technique
Database application requires lower response time and high-performance, so the property of will do it before disposing database application
It can load testing.But with the lasting operation of database application, user is more and more, and data volume is increasing, leads to data
The performance in library and load increase, if being intervened not in time and being handled, may result in the different degrees of event such as system failure
Barrier occurs.So the performance of real-time monitoring data and load become quite important now.
The performance load monitoring tools of database are very various at present, but these tools are generally only that simple displaying is some
Crucial operating index can not really reflect the operation conditions of database sometimes.When high response and low performance occurs in database
When situation, DBA analysis expert is usually still needed, these monitoring tools do not play the positioning and analysis of problem very big
Effect.
Although performance expert model commonly used in the art and load expert model are for common performance load monitoring tool
For, be greatly improved in terms of analytical database problem and positioning failure, but it there is also following some disadvantages:
1) expert model is the accumulation of advanced DBA knowledge and experience over more years, this working for database operation maintenance personnel
Experience and it is proficient in degree and has very high requirement, virtually improves human cost.
2) expert model is in addition to rule definition, and there are also complicated scripts and code, this needs stable developer and fortune
Troop is tieed up, has higher requirement to personnel's circulation.
3) expert model is the experience accumulation of advanced DBA, it defines whether really to be fitted for some indexs and regular
The performance of database and load have uncertainty, because each DBA can some deviations for the understanding of index and rule.Such as
There are the abnormal conditions of some small probabilities in fruit, and the index of expert model is not related to, then just needing advanced DBA from a large amount of
Historical data in discovery influence performance and load factor, this process Wang Wang is very time-consuming.
In summary illustrate, the technical solution for needing one kind new is to solve the above problems.
Summary of the invention
Goal of the invention: the present invention discloses a kind of database performance load evaluation system based on machine learning and assessment side
Method generates performance load learning model using machine learning algorithm training data, is subject to pre- assessment to new data using this model
Estimate.On the one hand, the conclusion of the assessment system is more reasonable than analysis of the expert model to feature, will not more omit important performance
Loading index, it is more accurate to position to the problem of database performance and load；On the other hand it reduces again to database operation maintenance personnel
Knowledge and Capability Requirement, human cost can be greatly saved, working efficiency is provided.
Technical solution: in order to achieve the above objectives, the present invention is based on the database performance load evaluation systems of machine learning can
It adopts the following technical scheme that
A kind of database performance load evaluation system based on machine learning, comprising:
Data acquisition module grabs characteristic to report to neutralize in database journal from database awr；
Data preprocessing module, to delete the single value tag in characteristic, missing feature is deleted, high correlation
Feature is deleted, while to fill missing data, make data normalization；
Training data model module generates model to training data；
Model evaluation module collects assessment models using verifying to the evaluation index according to different machine learning models；
Model tuning module, to adjust the hyper parameter of model to model automated tuning；
Model prediction module is divided into offline prediction and on-line prediction to prediction model；Offline prediction refers to utilizing instruction
Practice the test set that collection is separated；On-line prediction refers to the collected real time data of data collector, need by scaler into
It is being predicted after row data normalization.
Further, in data preprocessing module, single value tag is deleted are as follows: when a certain column characteristic value is all identical,
Directly delete；
It lacks feature to delete are as follows: then delete this feature column when characteristic series missing ratio reaches specific threshold；
High correlated characteristic is deleted are as follows: calculates the correlation between characteristic variable by Pearson correlation coefficient, building is related
Relational matrix deletes one of feature when finding that feature correlation is higher than some threshold value；
Missing Data Filling includes: to go to fill with the previous row data of missing values；It goes to fill with the average value of missing characteristic series；
Performance and load are A, B, C, D, E according to grade classification, fill missing values according to the average value of each grade.
Data normalization are as follows: scaling is normalized in initial characteristic data, using deviation standardization and standard difference standard
Change；Scaler during data normalization is stored in local, needs to utilize the contracting when predicting freshly harvested data
Put the data normalization that device carries out equal extent.
Further, in training data model module, data set is divided into training set, verifying collection and test set.Wherein instruct
Practice collection, it is respectively 70%, 15%, 15% that verifying collection and test set, which account for the ratio of data set,；
This system uses Integrated Algorithm training pattern, and gradient is promoted tree algorithm GBM and got a promotion based on gradient descent algorithm
Exponential model.Regression model uses training aids LGBMRegressor, and disaggregated model uses training aids LGBMClassifier.
Further, early stop technology and CheckPoint technology are used during assessment models；
Early stop: deconditioning in advance sees in discovery training process that the validation error for verifying collection will not become again
When change, training can be automatically stopped after k wheel, and k is preset value；
CheckPoint technology: the technology can automatically save training pattern optimal in training process.
Further, the hyper parameter in model tuning module includes:
Iteration wheel number epochs
Learning rate learning_rate
Setting tree depth max_depth
Learner leaf maximum number max_leaves
The quantity number n_estimators of weak learner
Loss function objective.
The utility model has the advantages that the present invention is based on the database performance load evaluation systems of machine learning from the database of multiple examples
Extract multiple characteristic indexs about performance and load in AWR report and database journal, by data mart modeling (data cleansing and
Conversion), it is trained using these mass datas as training set and with machine learning techniques, ultimately generates performance and load is learned
Model is practised, newly generated characteristic is predicted using this model, assesses the performance and loading condition of database.The assessment
4~6 or so, performance classification model and load are classified for the performance regression model of system and the root-mean-square error of load regression model
Model accuracy rate reaches 99.3 or so, has reached production application requirement.The system is artificial intelligence in database application
Primary innovation and application, reduce the human cost of enterprise, reach good economic benefit.
And the corresponding above-mentioned database performance load evaluation system based on machine learning, the present invention also provides be based on machine
The technical solution of the database performance load evaluation method of study:
A kind of database performance load evaluation method based on machine learning, comprising the following steps:
(1), data acquisition: report to neutralize in database journal grabbing characteristic from database awr；
(2), data prediction: the single value tag in characteristic is deleted, and missing feature is deleted, high correlation feature
It deletes, while to be filled missing data, make data normalization；
(3) training data generates model；
(4), assessment models: according to the evaluation index of different machine learning models, collect assessment models using verifying；
(5), the hyper parameter of model model tuning: is adjusted to model automated tuning；
(6), model prediction: it is divided into offline prediction and on-line prediction；Offline prediction refers to separating using training set
Test set；On-line prediction refers to the collected real time data of data collector, needs to carry out data normalization by scaler
It is being predicted afterwards.
In step (2), single value tag is deleted are as follows: when a certain column characteristic value is all identical, is directly deleted；
It lacks feature to delete are as follows: then delete this feature column when characteristic series missing ratio reaches specific threshold；
High correlated characteristic is deleted are as follows: calculates the correlation between characteristic variable by Pearson correlation coefficient, building is related
Relational matrix deletes one of feature when finding that feature correlation is higher than some threshold value；
Missing Data Filling includes: to go to fill with the previous row data of missing values；It goes to fill with the average value of missing characteristic series；
Performance and load are A, B, C, D, E according to grade classification, fill missing values according to the average value of each grade.
Data normalization are as follows: scaling is normalized in initial characteristic data, using deviation standardization and standard difference standard
Change；Scaler during data normalization is stored in local, needs to utilize the contracting when predicting freshly harvested data
Put the data normalization that device carries out equal extent.
In step (3), data set is divided into training set, verifying collection and test set.Wherein training set, verifying collection and test set
The ratio for accounting for data set is respectively 70%, 15%, 15%；
This system uses Integrated Algorithm training pattern, and gradient is promoted tree algorithm GBM and got a promotion based on gradient descent algorithm
Exponential model.Regression model uses training aids LGBMRegressor, and disaggregated model uses training aids LGBMClassifier.
In step (4), early stop technology and CheckPoint technology are used；
Early stop: deconditioning in advance sees in discovery training process that the validation error for verifying collection will not become again
When change, training can be automatically stopped after k wheel, and k is preset value；
CheckPoint technology: the technology can automatically save training pattern optimal in training process.
Hyper parameter in step (5) includes:
Iteration wheel number epochs
Learning rate learning_rate
Setting tree depth max_depth
Learner leaf maximum number max_leaves
The quantity number n_estimators of weak learner
Loss function objective.
The utility model has the advantages that the beneficial effect of the corresponding above-mentioned database performance load evaluation system based on machine learning, this is negative
It is more reasonable than analysis of the expert model to feature to carry the conclusion that appraisal procedure is made, more will not omit important performance load and refer to
Mark, it is more accurate to position to the problem of database performance and load；On the other hand the knowledge to database operation maintenance personnel is reduced again
And Capability Requirement, it can be greatly saved human cost, working efficiency is provided.
Detailed description of the invention
Fig. 1 is the flow chart of the database performance load evaluation method in this assessment system based on machine learning.
Specific embodiment
Incorporated by reference to shown in Fig. 1,
The present invention provides a kind of database performance load evaluation system based on machine learning, comprising:
Data acquisition module grabs characteristic to report to neutralize in database journal from database awr；
Data preprocessing module, to delete the single value tag in characteristic, missing feature is deleted, high correlation
Feature is deleted, while to fill missing data, make data normalization；
Training data model module generates model to training data；
Model evaluation module collects assessment models using verifying to the evaluation index according to different machine learning models；
Model tuning module, to adjust the hyper parameter of model to model automated tuning；
Model prediction module is divided into offline prediction and on-line prediction to prediction model；Offline prediction refers to utilizing instruction
Practice the test set that collection is separated；On-line prediction refers to the collected real time data of data collector, need by scaler into
It is being predicted after row data normalization.
In data preprocessing module, single value tag is deleted are as follows: when a certain column characteristic value is all identical, is directly deleted；
It lacks feature to delete are as follows: then delete this feature column when characteristic series missing ratio reaches specific threshold；
High correlated characteristic is deleted are as follows: calculates the correlation between characteristic variable by Pearson correlation coefficient, building is related
Relational matrix deletes one of feature when finding that feature correlation is higher than some threshold value；
Missing Data Filling includes: to go to fill with the previous row data of missing values；It goes to fill with the average value of missing characteristic series；
Performance and load are A, B, C, D, E according to grade classification, fill missing values according to the average value of each grade.
Data normalization are as follows: scaling is normalized in initial characteristic data, using deviation standardization and standard difference standard
Change；Scaler during data normalization is stored in local, needs to utilize the contracting when predicting freshly harvested data
Put the data normalization that device carries out equal extent.
In training data model module, data set is divided into training set, verifying collection and test set.Wherein training set, verifying
It is respectively 70%, 15%, 15% that collection and test set, which account for the ratio of data set,；
This system uses Integrated Algorithm training pattern, and gradient is promoted tree algorithm GBM and got a promotion based on gradient descent algorithm
Exponential model.Regression model uses training aids LGBMRegressor, and disaggregated model uses training aids LGBMClassifier.
Early stop technology and CheckPoint technology are used during assessment models；
Early stop: deconditioning in advance sees in discovery training process that the validation error for verifying collection will not become again
When change, training can be automatically stopped after k wheel, and k is preset value；
CheckPoint technology: the technology can automatically save training pattern optimal in training process.
Hyper parameter in model tuning module includes:
Iteration wheel number epochs
Learning rate learning_rate
Setting tree depth max_depth
Learner leaf maximum number max_leaves
The quantity number n_estimators of weak learner
Loss function objective
Please in conjunction with shown in Fig. 1, the database performance load evaluation method in this assessment system based on machine learning includes:
1, data set is obtained.
The performance load index with log log is reported using the awr of PostgreSQL database access oracle database,
Data are grabbed to grab according to average 3 minutes.Characteristic in awr tables of data has 62 dimensions, and the characteristic in log sheet has
Data in PostgreSQL tables of data are stored in this according to the sequencing of crawl time using python script by 200 dimensions
In ground csv file.
2, data prediction.
Single value tag is deleted: when a certain column characteristic value is all identical, any help no to the fitting of model also increases
Add calculation amount, can directly delete.
Missing feature is deleted: then being deleted this feature column when characteristic series missing ratio reaches specific threshold, is lacked in this system
It loses threshold value and is set as 60%.
High correlated characteristic is deleted: being calculated the correlation between characteristic variable by Pearson correlation coefficient, is constructed related close
It is matrix, deletes one of feature when finding that feature correlation is higher than some threshold value, the setting of this system relevance threshold
It is 0.9.
Missing Data Filling: there is missing data in obtaining data engineering and be inevitable, we only go as far as possible
It is fitted missing values.Three kinds of methods are used in this system:
It goes to fill with the previous row data of missing values.
It goes to fill with the average value of missing characteristic series.
Performance and load are A, B, C, D, E according to grade classification, fill missing values according to the average value of each grade.
Data normalization: scaling is normalized in initial characteristic data, this system uses two methods: deviation standardization
(Min-max normalization) and standard deviation standardize (zero-mean normalization).Data normalization process
In scaler be stored in local, need that the scaler is utilized to carry out equal extent when predicting freshly harvested data
Data normalization.
In disaggregated model, need to divide performance rate and load etc. by section according to performance scores and load score
Grade, specific as follows:
3, training data generates model
Data set is divided into training set, verifying collection and test set.Wherein training set, verifying collection and test set account for data set
Ratio is respectively 70%, 15%, 15%.
This system uses Integrated Algorithm training pattern, and gradient is promoted tree algorithm GBM and got a promotion based on gradient descent algorithm
Exponential model.Regression model uses training aids LGBMRegressor, and disaggregated model uses training aids LGBMClassifier.
4, assessment models
Early stop technology and CheckPoint technology are used during assessment models.
Early stop: deconditioning in advance sees in discovery training process that the validation error for verifying collection will not become again
When change, training can be automatically stopped after k wheel, and k is settable.Invalid training process, training for promotion effect will not thus occur
Rate.
CheckPoint technology: the technology can automatically save training pattern optimal in training process, even if being abnormal
Training is caused to stop that training result can also be saved in time.
5, model tuning
Model tuning function adjusts the hyper parameter of model using hyopt, and major parameter includes:
Iteration wheel number epochs
Learning rate learning_rate
Setting tree depth max_depth
Learner leaf maximum number max_leaves
The quantity number n_estimators of weak learner
Loss function objective
6, model prediction
Offline prediction: being predicted in model after tuning using the data in training set, and
Prediction in real time: grabbing data from database in real time and predicted, shows prediction result and is inserted into the new data
Into historical data, in case doing training set use when later period more new model.
In addition, there are many concrete methods of realizing and approach of the invention, the above is only a preferred embodiment of the present invention.
It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, can also do
Several improvements and modifications out, these modifications and embellishments should also be considered as the scope of protection of the present invention.What is be not known in the present embodiment is each
The available prior art of component part is realized.
Claims (10)
1. a kind of database performance load evaluation system based on machine learning, feature include:
Data acquisition module, to grab characteristic from database awr report and database journal；
Data preprocessing module, to delete the single value tag in characteristic, missing feature is deleted, high correlation feature
It deletes, while to be filled missing data, make data normalization；
Training data model module generates model to training data；
Model evaluation module collects assessment models using verifying to the evaluation index according to different machine learning models；
Model tuning module, to adjust the hyper parameter of model to model automated tuning；
Model prediction module is divided into offline prediction and on-line prediction to prediction model；Offline prediction refers to utilizing training set
The test set separated；On-line prediction refers to the collected real time data of data collector, needs to be counted by scaler
It is being predicted according to after standardization.
2. database performance load evaluation system according to claim 1, which is characterized in that in data preprocessing module,
Single value tag is deleted are as follows: when a certain column characteristic value is all identical, is directly deleted；
It lacks feature to delete are as follows: then delete this feature column when characteristic series missing ratio reaches specific threshold；
High correlated characteristic is deleted are as follows: is calculated the correlation between characteristic variable by Pearson correlation coefficient, is constructed correlativity
Matrix deletes one of feature when finding that feature correlation is higher than some threshold value；
Missing Data Filling includes: to go to fill with the previous row data of missing values；It goes to fill with the average value of missing characteristic series；Performance
It is A, B, C, D, E with loading according to grade classification, missing values is filled according to the average value of each grade.
Data normalization are as follows: scaling is normalized in initial characteristic data, is standardized using deviation standardization and standard difference；Number
Be stored in local according to the scaler in process of normalization, needed when predicting freshly harvested data using the scaler into
The data normalization of row equal extent.
3. database performance load evaluation system according to claim 2, it is characterised in that: training data model module
In, data set is divided into training set, verifying collection and test set.Wherein training set, verifying collection and test set account for the ratio of data set
Respectively 70%, 15%, 15%；
This system uses Integrated Algorithm training pattern, and gradient, which promotes tree algorithm GBM and is based on gradient descent algorithm, to get a promotion digital-to-analogue
Type.Regression model uses training aids LGBMRegressor, and disaggregated model uses training aids LGBMClassifier.
4. database performance load evaluation system according to claim 3, it is characterised in that: make during assessment models
With early stop technology and CheckPoint technology；
Early stop: deconditioning in advance, when seeing in discovery training process that the validation error for verifying collection will not change again,
Training can be automatically stopped after k wheel, and k is preset value；
CheckPoint technology: the technology can automatically save training pattern optimal in training process.
5. database performance load evaluation system according to claim 4, which is characterized in that super in model tuning module
Parameter includes:
Iteration wheel number epochs
Learning rate learning_rate
Setting tree depth max_depth
Learner leaf maximum number max_leaves
The quantity number n_estimators of weak learner
Loss function objective.
6. a kind of database performance load evaluation method based on machine learning, which comprises the following steps:
(1), data acquisition: report to neutralize in database journal grabbing characteristic from database awr；
(2), data prediction: the single value tag in characteristic is deleted, and missing feature is deleted, and high correlation feature is deleted
It removes, while to be filled missing data, make data normalization；
(3), training data generates model；
(4), assessment models: according to the evaluation index of different machine learning models, collect assessment models using verifying；
(5), the hyper parameter of model model tuning: is adjusted to model automated tuning；
(6), model prediction: it is divided into offline prediction and on-line prediction；Offline prediction refers to the survey separated using training set
Examination collection；On-line prediction refers to the collected real time data of data collector, need by scaler carry out data normalization after
It is predicted.
7. database performance load evaluation system according to claim 6, which is characterized in that in step (2), single value is special
Sign is deleted are as follows: when a certain column characteristic value is all identical, is directly deleted；
It lacks feature to delete are as follows: then delete this feature column when characteristic series missing ratio reaches specific threshold；
High correlated characteristic is deleted are as follows: is calculated the correlation between characteristic variable by Pearson correlation coefficient, is constructed correlativity
Matrix deletes one of feature when finding that feature correlation is higher than some threshold value；
Missing Data Filling includes: to go to fill with the previous row data of missing values；It goes to fill with the average value of missing characteristic series；Performance
It is A, B, C, D, E with loading according to grade classification, missing values is filled according to the average value of each grade.
Data normalization are as follows: scaling is normalized in initial characteristic data, is standardized using deviation standardization and standard difference；Number
Be stored in local according to the scaler in process of normalization, needed when predicting freshly harvested data using the scaler into
The data normalization of row equal extent.
8. database performance load evaluation system according to claim 2, it is characterised in that: in step (3), by data set
It is divided into training set, verifying collection and test set.It is respectively 70% that wherein training set, verifying collection and test set, which account for the ratio of data set,
15%, 15%；
This system uses Integrated Algorithm training pattern, and gradient, which promotes tree algorithm GBM and is based on gradient descent algorithm, to get a promotion digital-to-analogue
Type.Regression model uses training aids LGBMRegressor, and disaggregated model uses training aids LGBMClassifier.
9. database performance load evaluation system according to claim 3, it is characterised in that: in step (4), use
Early stop technology and CheckPoint technology；
Early stop: deconditioning in advance, when seeing in discovery training process that the validation error for verifying collection will not change again,
Training can be automatically stopped after k wheel, and k is preset value；
CheckPoint technology: the technology can automatically save training pattern optimal in training process.
10. database performance load evaluation system according to claim 4, which is characterized in that the hyper parameter in step (5)
Include:
Iteration wheel number epochs
Learning rate learning_rate
Setting tree depth max_depth
Learner leaf maximum number max_leaves
The quantity number n_estimators of weak learner
Loss function objective.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811207264.9A CN109344201A (en) | 2018-10-17 | 2018-10-17 | A kind of database performance load evaluation system and method based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811207264.9A CN109344201A (en) | 2018-10-17 | 2018-10-17 | A kind of database performance load evaluation system and method based on machine learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109344201A true CN109344201A (en) | 2019-02-15 |
Family
ID=65310448
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811207264.9A Pending CN109344201A (en) | 2018-10-17 | 2018-10-17 | A kind of database performance load evaluation system and method based on machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109344201A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110019151A (en) * | 2019-04-11 | 2019-07-16 | 深圳市腾讯计算机系统有限公司 | Database performance method of adjustment, device, equipment, system and storage medium |
| CN110134665A (en) * | 2019-04-17 | 2019-08-16 | 北京百度网讯科技有限公司 | Database self-learning optimization method and device based on traffic mirroring |
| CN110443304A (en) * | 2019-08-06 | 2019-11-12 | 民生科技有限责任公司 | A kind of business risk appraisal procedure based on machine learning model |
| CN110717535A (en) * | 2019-09-30 | 2020-01-21 | 北京九章云极科技有限公司 | Automatic modeling method and system based on data analysis processing system |
| CN110750512A (en) * | 2019-09-09 | 2020-02-04 | 北京新数科技有限公司 | Database performance evaluation management method and device |
| CN111079361A (en) * | 2019-12-07 | 2020-04-28 | 复旦大学 | Load modeling method of FPGA circuit |
| CN112153636A (en) * | 2020-10-29 | 2020-12-29 | 浙江鸿程计算机系统有限公司 | Method for predicting number portability and roll-out of telecommunication industry user based on machine learning |
| CN112628132A (en) * | 2020-12-24 | 2021-04-09 | 上海大学 | Water pump key index prediction method based on machine learning |
| CN113157814A (en) * | 2021-01-29 | 2021-07-23 | 东北大学 | Query-driven intelligent workload analysis method under relational database |
| CN113297169A (en) * | 2021-02-26 | 2021-08-24 | 阿里云计算有限公司 | Database instance processing method, system, device and storage medium |
| US11194785B2 (en) | 2019-08-14 | 2021-12-07 | International Business Machines Corporation | Universal self-learning database recovery |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060106851A1 (en) * | 2004-11-03 | 2006-05-18 | Dba Infopower, Inc. | Real-time database performance and availability monitoring method and system |
| US20080005317A1 (en) * | 2006-06-30 | 2008-01-03 | International Business Machines Corporation | Method and apparatus for cross-tier management in multi-tier computing system architecture |
| CN104361337A (en) * | 2014-09-10 | 2015-02-18 | 苏州工业职业技术学院 | Sparse kernel principal component analysis method based on constrained computation and storage space |
| CN108375808A (en) * | 2018-03-12 | 2018-08-07 | 南京恩瑞特实业有限公司 | Dense fog forecasting procedures of the NRIET based on machine learning |
| CN108388503A (en) * | 2018-02-13 | 2018-08-10 | 中体彩科技发展有限公司 | Data-base performance monitoring method, system, equipment and computer readable storage medium |
-
2018
- 2018-10-17 CN CN201811207264.9A patent/CN109344201A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060106851A1 (en) * | 2004-11-03 | 2006-05-18 | Dba Infopower, Inc. | Real-time database performance and availability monitoring method and system |
| US20080005317A1 (en) * | 2006-06-30 | 2008-01-03 | International Business Machines Corporation | Method and apparatus for cross-tier management in multi-tier computing system architecture |
| CN104361337A (en) * | 2014-09-10 | 2015-02-18 | 苏州工业职业技术学院 | Sparse kernel principal component analysis method based on constrained computation and storage space |
| CN108388503A (en) * | 2018-02-13 | 2018-08-10 | 中体彩科技发展有限公司 | Data-base performance monitoring method, system, equipment and computer readable storage medium |
| CN108375808A (en) * | 2018-03-12 | 2018-08-07 | 南京恩瑞特实业有限公司 | Dense fog forecasting procedures of the NRIET based on machine learning |
Non-Patent Citations (4)
| Title |
|---|
| 优达学城UDACITY: "有了这个神器，机器学习特征选择不用愁！", 《HTTPS://M.VLAMBDA.COM/MIP/WZ_X8I6VU5KT7.HTML》 * |
| 那伊抹微笑: "LightGBM中文文档", 《HTTPS://LIGHTGBM.APACHECN.ORG/#/DOCS/6》 * |
| 韩忠明: "《数据分析与R》", 31 August 2014, 北京邮电大学出版社 * |
| 马倩: "基于机器学习的电子商务平台重复购买客户预测", 《中国优秀硕士学位论文全文数据库经济与管理科学辑》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020207268A1 (en) * | 2019-04-11 | 2020-10-15 | 腾讯科技（深圳）有限公司 | Database performance adjustment method and apparatus, device, system, and storage medium |
| CN110019151A (en) * | 2019-04-11 | 2019-07-16 | 深圳市腾讯计算机系统有限公司 | Database performance method of adjustment, device, equipment, system and storage medium |
| CN110019151B (en) * | 2019-04-11 | 2024-03-15 | 深圳市腾讯计算机系统有限公司 | Database performance adjustment method, device, equipment, system and storage medium |
| CN110134665A (en) * | 2019-04-17 | 2019-08-16 | 北京百度网讯科技有限公司 | Database self-learning optimization method and device based on traffic mirroring |
| CN110443304A (en) * | 2019-08-06 | 2019-11-12 | 民生科技有限责任公司 | A kind of business risk appraisal procedure based on machine learning model |
| US11194785B2 (en) | 2019-08-14 | 2021-12-07 | International Business Machines Corporation | Universal self-learning database recovery |
| CN110750512A (en) * | 2019-09-09 | 2020-02-04 | 北京新数科技有限公司 | Database performance evaluation management method and device |
| CN110717535A (en) * | 2019-09-30 | 2020-01-21 | 北京九章云极科技有限公司 | Automatic modeling method and system based on data analysis processing system |
| CN111079361A (en) * | 2019-12-07 | 2020-04-28 | 复旦大学 | Load modeling method of FPGA circuit |
| CN111079361B (en) * | 2019-12-07 | 2023-05-02 | 复旦大学 | Load modeling method of FPGA circuit |
| CN112153636A (en) * | 2020-10-29 | 2020-12-29 | 浙江鸿程计算机系统有限公司 | Method for predicting number portability and roll-out of telecommunication industry user based on machine learning |
| CN112628132A (en) * | 2020-12-24 | 2021-04-09 | 上海大学 | Water pump key index prediction method based on machine learning |
| CN112628132B (en) * | 2020-12-24 | 2022-04-26 | 上海大学 | Water pump key index prediction method based on machine learning |
| CN113157814B (en) * | 2021-01-29 | 2023-07-18 | 东北大学 | Query-driven intelligent workload analysis method under relational database |
| CN113157814A (en) * | 2021-01-29 | 2021-07-23 | 东北大学 | Query-driven intelligent workload analysis method under relational database |
| CN113297169A (en) * | 2021-02-26 | 2021-08-24 | 阿里云计算有限公司 | Database instance processing method, system, device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109344201A (en) | A kind of database performance load evaluation system and method based on machine learning | |
| CN111767802B (en) | Method and device for detecting abnormal state of object | |
| CN105300692B (en) | A kind of bearing failure diagnosis and Forecasting Methodology based on expanded Kalman filtration algorithm | |
| CN108199795A (en) | The monitoring method and device of a kind of equipment state | |
| CN113095555A (en) | Crop disease and insect pest monitoring method and system based on Internet of things and storage medium | |
| Rosenzweig | Preston’s ergodic conjecture: the accumulation of species in space and time | |
| CN103440283A (en) | Vacancy filling system for measured point data and vacancy filling method | |
| US11060899B2 (en) | Method for determining a maximum allowable volume of water that can be removed over time from an underground water source | |
| CN110865924B (en) | Health degree diagnosis method and health diagnosis framework for internal server of power information system | |
| CN108090628A (en) | A kind of grain feelings security detection and analysis method based on PSO-LSSVM algorithms | |
| CN113610381B (en) | Water quality remote real-time monitoring system based on 5G network | |
| CN104735710A (en) | Mobile network performance early warning pre-judging method based on trend extrapolation clustering | |
| CN111027881B (en) | Transmission tower natural disaster loss evaluation method based on improved grey correlation degree | |
| CN117371727A (en) | Intelligent agricultural monitoring and planning method and system based on Internet of things | |
| CN116579615A (en) | Vegetation coverage monitoring system based on unmanned aerial vehicle remote sensing | |
| CN104408525A (en) | Quantitative evaluation and control method of job shop scheduling risks | |
| CN115719283A (en) | Intelligent accounting management system | |
| CN112861422B (en) | Deep learning coal bed gas screw pump well health index prediction method and system | |
| CN103197564B (en) | Digitizing man-machine interface monitor unit quantity optimization method and system | |
| Prates et al. | Intervention analysis of hurricane effects on snail abundance in a tropical forest using long-term spatiotemporal data | |
| CN107480703A (en) | Transaction fault detection method and device | |
| CN115775092B (en) | Construction process safety risk management and control system based on digital twin technology | |
| CN115361424A (en) | Medical equipment monitoring method and system based on data analysis of Internet of things | |
| CN115640329A (en) | Intelligent diagnosis method for oil well fault based on multi-source data analysis | |
| CN111144682A (en) | Method for mining main influence factors of operation efficiency of power distribution network |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190215 |