CN111143328A - Agile business intelligent data construction method, system, equipment and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, equipment and a storage medium for constructing agile business intelligent data, which belong to the technical field of business intelligence, and comprise the following steps: the database generates a cube file; the cube file establishes a data model according to the original data; and outputting data according to the access requirement of the user. When a user accesses the design report, the bitmap index of the field to be used is loaded to the memory in advance. When processing the grouping, processing the data through the bitmap index, and generating a required result through conversion; grouping is carried out through multiple threads, and a summary result is generated by the multiple threads and the memory mapping file. And summarizing the result to establish a data cube model. The processing list is row data according to the value of the calculated bitmap index. The technical scheme of the embodiment of the invention does not generate redundant data and greatly improves the performance of the correlation calculation of the large table.
Description
Technical Field
The invention relates to the technical field of business intelligence, in particular to index management and application for providing data analysis support for industrial data, and specifically relates to a method, a system, equipment and a storage medium for constructing agile business intelligent data.
Background
In the current society, "data" has penetrated into every industry and business function area, becoming an important production factor. People's mining and application of mass data indicate a new wave of productivity increase and the arrival of surplus wave of consumers. The big data exists in the fields of physics, biology, environmental ecology and the like and the industries of military, finance, communication and the like for a long time, but the big data attracts people's attention in recent years due to the development of the internet and information industries.
No matter any new concept or technology, if it has no application value, it is certainly not popularized, but if it has an "application value" that can be understood by individuals, it is rapidly spread and infinitely enlarged by the mobile internet today. Undoubtedly, "big data" is developed like this, and people also accept management ideas such as "data management, data operation, data decision" from their application values. This is why big data can be driving economic, social progress and development.
At the same time, the business of enterprises is facing increasingly intense competition, government transformation is also facing pressure on data services, and if the data analysis platform is deployed and goes through a long implementation process like 10 years ago, then the data operation becomes an empty talk. Thus, an "agile business intelligence" has emerged that meets market expectations, with both users and vendors desiring that the process of building an analytics system on a data platform become faster, simpler, and more efficient.
However, traditional heavy BI (business intelligence) requires manually writing sql (structured query language) to build a model, which is not only costly to learn, but also requires that all business requirements be collected well at the beginning of a project, or else, if new requirements are generated later, the sql needs to be rewritten and modeled again. Therefore, the time for communication and research of project requirements is long, the development of commercial intelligent projects is changed into a long Marathon, the requirements in the season cannot be responded in time, and even after the business personnel are on line, the business personnel cannot use the business personnel due to technical obstacles.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide an agile business intelligent data construction method, an agile business intelligent data construction system, equipment and a storage medium, wherein the agile business intelligent data construction method can perform data matching calculation according to the incidence relation, guarantees the flexibility and the expansibility of a bottom layer data model, does not generate redundant data, and greatly improves the performance of big table incidence calculation.
The technical scheme adopted by the invention for solving the technical problems is as follows:
in one aspect, a method for constructing agile business intelligence data is provided, which comprises the following steps:
the database generates a cube file;
the cube file establishes a data model according to original data, wherein the original data come from a basic service system and a general hardware protocol;
and outputting data according to the access requirement of the user.
Further, the establishing a data model according to the raw data includes:
the original data enters a message queue through primary processing;
and respectively modeling and warehousing data according to different timeliness of data requirements.
Further, the preliminary processing includes:
data cleaning: carrying out noise reduction processing on the original data;
the data is rich: and associating the event and the object in the original data.
Further, the modeling and warehousing of the data according to different timeliness of the data requirements respectively includes:
establishing a data model with high data timeliness requirement, presenting the data model through a unified analysis service, and asynchronously warehousing the data;
and establishing a data model with low data timeliness requirement, directly warehousing, and waiting for calling for display.
Further, the data model includes an object model, an event model, and a non-stateful model, wherein,
the object model comprises physical attributes and logical relationships of the objects;
the event model comprises an event unique representation, event attribute identification (what type of event), event occurrence time, time change value, event description and event change value threshold.
Further, the data output is carried out according to the access requirements of the user, and the data output comprises accessing a design report, processing groups and a processing list.
Further, when a user accesses the design report, preloading a bitmap index of a field to be used to the memory, and preloading the bitmap index of the field to be used to the memory, includes: using the bitmap of the semi-finished product to dynamically generate a bitmap index of a required field by indexing;
when processing the packet, the method comprises the following steps: processing the data through the bitmap index, and generating a required result through conversion; grouping through multiple threads, generating a summary result by the multiple threads and the memory mapping file, and establishing a data cube model by the summary result;
the processing list is used for making row data according to values of the bitmap indexes.
In another aspect, the present invention further provides an agile business intelligence data construction system, comprising:
the file generation unit comprises a data source module, a data access module and a data layer module and is configured for the database to generate a cube file;
the model establishing unit comprises a data calculating module and a unified analysis service module and is configured for establishing a data model for the cube file according to the original data;
and the data output unit comprises a service application module and is configured to output data according to the access requirement of the user.
In another aspect, the present invention also provides an apparatus, comprising:
one or more processors;
a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to perform any of the agile business intelligence data construction methods of examples of the invention.
In another aspect, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any one of the agile business intelligence data construction methods of the examples of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the agile business intelligent data construction method disclosed by the example of the invention, after the data model is automatically built according to the original data by the cube file, all dimensions, indexes and associations are built, the dimensions can be freely dragged during new building analysis, the dimensions can be switched randomly during checking analysis, the market variable requirements can be adapted, the response is more timely, the project development period is greatly shortened, the requirements of customers are met at the fastest speed, and the burden of an IT department is also reduced.
2. According to the agile business intelligent data construction method disclosed by the invention, when the design report is accessed, the bitmap index of the field to be used is loaded to the memory in advance, the bitmap index of the semi-finished product is used, and the bitmap index of the field to be used can be dynamically generated within tens of milliseconds after the index is used, so that the response speed is greatly improved.
3. The quick business intelligent data construction method disclosed by the invention uses multi-thread grouping and multi-thread and memory mapping files to generate a summary result when processing the grouping, and can adapt to data summary of orders of magnitude such as more than ten million levels.
4. According to the agile business intelligent data construction method disclosed by the invention, the summary result is established into the data cube model, and repeated calculation is avoided when the data and part of the data are fetched next time.
5. According to the agile business intelligent data construction method disclosed by the invention, the processing list is used for making row data according to the value of the calculated bitmap index, the performance of the list has no upper limit, the data size can be quickly acquired, and the data processing efficiency is improved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a system architecture diagram of an agile business intelligence data construction system in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention.
All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
One embodiment of the invention provides an agile business intelligence data construction method, which comprises the following steps:
s1: the database generates a cube file;
s2: the cube file establishes a data model according to original data, wherein the original data come from a basic service system and a general hardware protocol;
s3: and outputting data according to the access requirement of the user.
Specifically, the method comprises the following steps:
collecting original data from various basic service systems and general hardware protocols;
the original data enters a message queue through primary processing; the primary processing mainly comprises data cleaning and data enrichment. The data cleaning is to perform noise reduction processing on the original data to ensure the accuracy of the data, and the data enrichment is to associate events and objects in the original data to perfect the original data;
aiming at different timeliness of data requirements, data modeling and warehousing are divided into two scenes: 1. establishing a data model with high data timeliness requirement, directly presenting the data model to a user through a unified analysis service, and asynchronously warehousing the data; 2. and establishing a data model with low data timeliness, directly warehousing, and waiting for the user to call and display the data model to the user.
The data model mainly comprises an object model, an event model (an alarm model and a performance model) and a non-stateful model.
An object model: the emphasis is to model all objects in the business requirements. The model is very flexible and can be suitable for object modeling of different scenes. Taking an IT operation and maintenance scene as an example, a user only needs to pay attention to physical attributes and logical relations of collection objects, and can import the system through various means, such as excel import, functions in the system such as visual modeling, and the like.
An event model: all the changed data is called an event. The event model mainly comprises attributes such as event unique representation, event attribute identification (what type of event), event occurrence time, time change value, event description and event change value threshold. Through threshold control, the models in the final database are divided into alarm models and performance models.
Generally, data output is carried out according to the access requirements of users, and the data output comprises accessing a design report, processing groups and processing lists.
When a user accesses a design report, the bitmap index of the field to be used is loaded to the memory in advance, and the bitmap index of the field to be used is dynamically generated by using the bitmap of the semi-finished product in tens of milliseconds.
When processing the grouping, processing the data through the bitmap index, and generating a required result through conversion; grouping is carried out through multiple threads, and a summary result is generated by the multiple threads and the memory mapping file. And data summarization of more than ten million levels is easily handled.
And establishing the summary result into a data cube model. The duplicate calculations are avoided at the next fetch, and at the partial fetches (e.g., if 3 fields were used before and 2 out of 3 fields were used after, then duplicate calculations are not needed).
The processing list is used for making row data according to the value of the calculated bitmap index, the performance of the list has no upper limit, and the data size can be quickly taken.
The construction method of the agile business intelligent data of the embodiment comprises the following steps:
from the high-performance level, the agile BI users are large in size and have to guarantee the performance, and the agile business intelligence data construction method provides a data caching mechanism for the agile business intelligence data construction method. After business personnel establish models, themes and dimensions, if the actual database or data engine does not respond fast enough, the method extracts data to a cache layer embedded in the system through an extraction function, a uniform resource scheduling interface is provided, and extraction of both increment and full amount can be easily defined. The cache layer can also be deployed and replaced according to the user requirements and the actual situation.
In the aspect of authority management, the authority management is not considered by many agile BI products, and the amount of users of many agile BI products is small, and the authority can be distributed by one or two bits. However, in large enterprises, such as the power industry and bank systems, hundreds or thousands of people use the self-service analysis platform, and the self-service analysis platform cannot be authorized by one or two people. Thus, the agile BI product must have a policy for hierarchical authorization, again using the bank example, a master level administrator authorizes a secondary (branch) administrator, who then proceeds to do the authorization down. The functional layer also needs to be provided with an authority inheritance mechanism, which mainly solves the problems of personnel mobility and repeated authorization of departments.
In the aspect of usability, two main points are provided, one is to use a report (spreadsheet), the agile BI product is directly innovated on Excel at the moment, and no report making mode more convenient than the conventional report making mode exists in the current market; and secondly, a self-help instrument panel function is pushed out, and the data model is established, so that the data model can be directly dragged and pulled by a mouse to form a required analysis report. Whether the report form or the self-help instrument panel really helps business personnel (non-technical background) to realize breakthrough of data analysis work without assistance of IT department colleagues. In addition, the report making is very convenient, and the report outputting is also very convenient and fast. Such as: multi-screen analysis, namely, whether Android or IOS, whether a mobile phone or a computer, all equipment can support application through a streaming layout; meanwhile, the report or the instrument panel is integrated into the tools of Word/PPT.
In another aspect, the present invention further provides an agile business intelligence data construction system, comprising:
the file generation unit comprises a data source module, a data access module and a data layer module and is configured for the database to generate a cube file;
the model establishing unit comprises a data calculating module and a unified analysis service module and is configured for establishing a data model for the cube file according to the original data;
and the data output unit comprises a service application module and is configured to output data according to the access requirement of the user.
The system architecture of this embodiment is as shown in fig. 1, and the data source module, the data access module, the data layer module, the data calculation module, the unified analysis service module, and the service application module are sequentially arranged from bottom to top.
A data source: the data source mainly comprises a user basic service system and a standard hardware interface source.
Data access: and cleaning and enriching the metadata. In the embodiment, the layer is uniformly packaged to form a configurable interface platform, so that non-IT personnel can access data in a configuration mode. The interface platform supports data access of an Http protocol interface, a websocket protocol interface, a database sharing table, a snmp general protocol interface and the like.
And (3) a data layer: a core layer of system storage data is used for starting a mysql database aiming at high concurrency business requirements and storing data with less basic and data quantity; the ES database stores data with large data quantity, such as an event model; the memory database is high in storage timeliness and needs frequently calculated data.
And (3) data calculation: the data calculation is divided into off-line calculation and real-time calculation. The off-line calculation is mainly used for data calculation of which a user actively initiates a request; and the real-time calculation is directly communicated with the data access layer, and the data with higher timeliness is processed.
Unified analysis service: visual modeling is a core function for allowing a user to define a report in a visual form and realizing agile BI; the user can customize the desired chart in the visual modeling, including the type of chart (bar, pie, line, etc.), skin, location, size, dimensions, metrics, etc.
The units according to the present embodiment may be implemented by software, or may be implemented by hardware. The described units or modules may also be provided in a processor. The names of these units or modules do not in some cases constitute a limitation of the unit or module itself.
In another aspect, the present invention also provides an apparatus, comprising:
one or more processors;
a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to perform the agile business intelligence data construction method of this embodiment.
Specifically, the processor of the device may adopt a CPU of a computer, the memory of the device may adopt a computer-readable storage medium, such as an optical disc, a floppy disc, a removable hard disk, a U-disc, an SD card, and the like, and the method implemented when the processor executes has been described in detail in this embodiment, and is not described herein again.
In another aspect, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the agile business intelligence data construction method of the present embodiment. Specifically, the readable storage medium may be an optical disc, a floppy disc, a removable hard disc, a U-disc, an SD card, etc., and the method implemented when executed is described in detail in this embodiment, which is not described herein again. The computer-readable storage medium may be the computer-readable storage medium contained in the apparatus described in the above embodiments; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods for labeling and identifying fixed objects in overhead lookout camera images described herein.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Other technical features than those described in the specification are known to those skilled in the art, and are not described herein in detail in order to highlight the innovative features of the present invention.
Claims (10)
1. An agile business intelligence data construction method, comprising:
the database generates a cube file;
the cube file establishes a data model according to original data, wherein the original data come from a basic service system and a general hardware protocol;
and outputting data according to the access requirement of the user.
2. The agile business intelligence data construction method of claim 1 wherein the building a data model from raw data comprises:
the original data enters a message queue through primary processing;
and respectively modeling and warehousing data according to different timeliness of data requirements.
3. The agile business intelligence data construction method of claim 2 wherein the initial processing comprises:
data cleaning: carrying out noise reduction processing on the original data;
the data is rich: and associating the event and the object in the original data.
4. The agile business intelligence data construction method according to claim 2 or 3, wherein the modeling and warehousing of the data according to the different timeliness of the data requirements respectively comprises:
establishing a data model with high data timeliness requirement, presenting the data model through a unified analysis service, and asynchronously warehousing the data;
and establishing a data model with low data timeliness requirement, directly warehousing, and waiting for calling for display.
5. The agile business intelligence data construction method of claim 1 wherein the data model comprises an object model, an event model, and a non-stateful model, wherein,
the object model comprises physical attributes and logical relationships of the objects;
the event model comprises an event unique representation, event attribute identification (what type of event), event occurrence time, time change value, event description and event change value threshold.
6. The agile business intelligence data construction method of claim 1 wherein the data output based on user access requirements comprises accessing design reports, processing groups and processing lists.
7. The agile business intelligence data construction method of claim 6,
when a user accesses a design report, pre-loading bitmap indexes of fields to be used to a memory, wherein the pre-loading bitmap indexes of the fields to be used to the memory comprises the following steps: using the bitmap of the semi-finished product to dynamically generate a bitmap index of a required field by indexing;
when processing the packet, the method comprises the following steps: processing the data through the bitmap index, and generating a required result through conversion; grouping through multiple threads, generating a summary result by the multiple threads and the memory mapping file, and establishing a data cube model by the summary result;
the processing list is used for making row data according to values of the bitmap indexes.
8. An agile business intelligence data construction system comprising:
the file generation unit comprises a data source module, a data access module and a data layer module and is configured for the database to generate a cube file;
the model establishing unit comprises a data calculating module and a unified analysis service module and is configured for establishing a data model for the cube file according to the original data;
and the data output unit comprises a service application module and is configured to output data according to the access requirement of the user.
9. An apparatus, comprising:
one or more processors;
memory storing one or more programs that, when executed by the one or more processors, cause the one or more processors to perform the agile business intelligence data construction method of any one of claims 1-7.
10. A computer-readable storage medium storing a computer program, which when executed by a processor implements the agile business intelligence data construction method according to any one of claims 1-7.
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| CN113126849A (en) * | 2021-04-07 | 2021-07-16 | 帆软软件有限公司 | Spreadsheet interactive mapping virtual system based on database and interactive mapping method |
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