CN111291107B - Progressive immersion type visual data analysis method based on virtual reality technology - Google Patents
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Abstract
The invention discloses a progressive immersion type visual data analysis method based on a virtual reality technology, which comprises the following steps of: when a client sends a message to a server through a WebSocket, a related database is selected to run a GSP progressive sequence mode mining algorithm, the mined data information is transmitted to a Unity client through the WebSocket, and visual presentation of the mined data is realized through a virtual reality technology; during running GSP progressive sequence pattern mining algorithm, related interactive operation is carried out on data, and steering operation is carried out on the algorithm, so that needed visual data is obtained. According to the characteristics of the virtual reality technology, the progressive immersion type visual analysis system can realize visual analysis and interaction of data, can give better data analysis environment and more excellent interactive immersion type experience to data analysis staff, and is beneficial to the rapid data analysis and mining of the data analysis staff.
Description
Technical Field
The invention relates to the field of visual data mining analysis, in particular to a progressive immersive visual data analysis method based on a virtual reality technology.
Background
In recent years, as the capability of computers to continuously process data has increased, the processing time of many mass data has been greatly reduced, but when processing many classical data analysis systems, delays have still been introduced to the data analyst due to the amount of mass data required and the complexity of the algorithms involved. Based on incremental visualization, there are many emerging progressive vision analysis systems whose purpose is to reduce latency by taking advantage of some of the intermediate results provided by the execution of progressive analysis algorithms. The basic idea of progressive visual analysis is that the analysis algorithm can be designed to produce meaningful partial results during execution, and then the progressive results can be combined with interactive visualizations, allowing the user to browse the partial results immediately, check them immediately after the new results are calculated, and perform a new exploratory analysis without waiting for the previous analysis to complete, suggesting avoiding mandatory waiting times. This approach is in sharp contrast to traditional pre-calculation and acceleration methods, which all assume that the analysis must be completed before the results can be used for visual interaction. The progressive analysis system allows a user to interact with the data mining algorithm by presenting the algorithm results immediately after they are found, or by providing the possibility to direct them onto the desired data or results. In summary, progressive visual analysis is expected to speed up the user's analysis by eliminating the time interval between user interaction and computational analysis.
Immersive analysis is also an emerging field of research, with the aim of exploring the applicability and development of emerging user interface technologies to create more engaging immersive experiences and seamless workflows for data analysis applications, i.e., research augmented reality usage (AR) and Virtual Reality (VR) devices to visualize and analyze data in an immersive manner. We can also define the use of attractive physicochemical analysis tools to support data understanding and decision making. The goal is to eliminate obstructions between personnel, data and tools for analysis. At the same time, it is intended to support data understanding and decision making across and everyone, whether working alone or in concert, the immersive analysis system is typically built on top of an existing virtual/augmented reality environment. In the field of progressive immersive visual analytics, virtual/augmented reality environments offer rich multi-modal interaction opportunities by multiple input devices controlling computing and tracking multiple human analytics behaviors.
The GSP algorithm is a Sequence pattern mining algorithm, and Sequence pattern mining is to find out all sequences meeting the minimum support specified by the user from a Data Sequence (Data Sequence) set S. Each such sequence is referred to as a frequent sequence, or sequence pattern. The GSP algorithm core idea is: at each scan (pass) of the database, candidate sequences are generated using the large sequences generated at the previous scan, and their support (support) is calculated while scanning, and the candidate sequences satisfying the support are used as the large sequences for the next scan. On scan 1, a frequent sequence pattern of length 1 is used as the initial seed set sequence. In addition, the GSP algorithm uses the Hash tree to store candidate sequences, reduces the number of sequences to be scanned, and converts the representation method of the data sequences, so that whether one candidate is a subsequence of the data sequences can be effectively found.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings of the prior art and provides a progressive immersion type visual data analysis method based on a virtual reality technology.
The aim of the invention is achieved by the following technical scheme:
a progressive immersive visual data analysis method based on a virtual reality technology comprises the following steps:
s1, carrying out message communication between a client and a server by utilizing a WebSocket, and selecting a corresponding database and operation by utilizing the transmitted message;
s2, running a GSP progressive sequence pattern mining algorithm to obtain data of customized content, and transmitting the data to a client through a WebSocket;
s3, using HTC VIVE equipment to realize a virtual reality immersive environment, and constructing a coordinate system according to the attribute of the mined data;
s4, carrying out data instantiation by using a Unity prefab, wherein the data are presented in a coordinate system, and carrying out data operation and interaction by using a handle;
in step S1, the WebSocket implements full duplex communication between the client and the server: after the socket is created, the socket can be responded through four events of onopen, onmessage, onclose and onerror; and (3) performing open connection operation through the address with good protocol, sending data to the server through a send () method, receiving the data returned by the server through an onmessage event, and after the data transmission is finished, performing close event by a WebSocket to close the connection, and triggering an onerror event if connection, processing, receiving and sending data fail.
In step S2, the GSP progressive sequence pattern mining algorithm can design an analysis algorithm to generate a meaningful partial result in the process of performing data mining, and can give the user a visual result of immediately browsing partial contents with the partial progressive result, and simultaneously perform the rest of data mining analysis.
In step S2, the GSP progressive sequence pattern mining algorithm generates candidate sequences by using the large sequences generated during the previous scan when the database is scanned each time, calculates their support (support) while scanning, and uses the candidate sequences satisfying the support as the large sequences for the next scan.
In step S3, the attribute of the mining data includes a size (size), a support, and a number (number) based on the pattern data.
In step S4, the visualization of the data using the prefab of Unity is specifically: firstly, creating objects and prefbs, then importing mining data into Unity and carrying out prefab instantiation, visualizing the data, converting world coordinates and Camera coordinates and converting scale, and then displaying the data in a coordinate system of a VR environment through VR equipment for interaction.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention provides an effective solution for visual analysis of the mined data from two aspects of progressive algorithm and immersive interaction based on virtual reality technology.
2. The invention provides an immersive interaction environment, which gives better interaction experience of a data analysis environment and a more stick to data analysis staff compared with the traditional interaction mode, and is beneficial to the data analysis staff to carry out rapid data analysis and mining.
3. The invention is very suitable for cross-platform development by using the Unity tool, provides a large number of plug-ins for development, wherein the VRTK is an efficient interactive plug-in, and can help to quickly and easily construct VR solutions in Unity, and the purpose is to help users to improve productivity by accelerating the creation process from prototype conception to complete solution construction.
Drawings
Fig. 1 is a comparison of a progressive visual analysis algorithm and a conventional non-progressive visual analysis algorithm.
FIG. 2 is an example of a GSP sequence pattern mining algorithm and an implementation pseudocode diagram of the GSP algorithm.
Fig. 3 is a flow chart of a comparison of a progressive immersive visual data analysis system based on virtual reality technology and a conventional progressive visual analysis system according to the present invention.
Fig. 4 is a comparison diagram of the manner in which the progressive immersive visual data analysis system and the conventional progressive visual analysis system perform interactive data display based on the virtual reality technology.
Fig. 5 is a flowchart of a progressive immersion type visual data analysis method based on the virtual reality technology according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
As shown in fig. 5, a progressive immersion type visual data analysis method based on a virtual reality technology includes the following steps: when the client sends a message to the server through the WebSocket, a related database is selected to run a GSP progressive sequence mode mining algorithm, the mined data information is transmitted to the Unity client through the WebSocket, and visual presentation of the mined data is realized through a virtual reality technology. During the operation of the GSP progressive sequence pattern mining algorithm, related interactive operation and steering operation can be carried out on the data to obtain the visual data required by the user.
As shown in fig. 1, the progressive visual analysis algorithm is compared with the conventional non-progressive visual analysis algorithm:
first, the data analyst selects the databases to be analyzed, selects parameters of data attributes to mine, and for systems of progressive visual analysis algorithms whose purpose is to reduce latency by taking advantage of partial intermediate results provided by execution of the progressive analysis algorithm, the system may design the analysis algorithm to produce meaningful partial results during execution, and then may combine the progressive results with interactive visualizations, allowing the data analyst to browse the partial results immediately, examine them immediately after calculation of new results, and perform new exploratory analysis without waiting for the previous analysis to complete, suggesting avoidance of mandatory latency. This approach is in sharp contrast to traditional pre-calculation and acceleration methods, which all assume that the analysis must be completed before the results can be obtained.
As shown in fig. 2, the steps for GSP sequence pattern mining algorithm example and pseudocode refinement in step S2 are as follows:
2.1 A sequence is a sequence s of objects obtained by arranging all events related to the objects in increasing order of time stamps. The sequence database contains a dataset of one or more sequence data, as shown in S1 and S2 in fig. 2. Sequence support refers to the proportion of all data sequences (ordered list of events associated with a single data object (a/B/C in fig. 2)) that contain s, and if the sequence s support is greater than or equal to minsup, s is referred to as a sequence pattern (frequent sequence). The sequence pattern mining refers to finding out all sequences with the support degree greater than or equal to the minimum support degree sup given a sequence data set Dataset and a user-specified minimum support degree sup.
2.2 The idea of GSP sequence pattern mining algorithm is: 1. a sequence pattern C1 with the length of 1 is used as an initial seed set candidate k-frequent sequence pattern; 2. according to a seed set Ck with the length of k, a candidate sequence pattern Ck+1 with the length of k+1 is generated through a connecting operation and a cutting operation, then a database is scanned, the support degree of each candidate sequence pattern is calculated, and a sequence pattern Fk+1 with the length of k+1 is generated and used as a new seed set. 3. The second step is repeated until no new sequence patterns or new candidate sequence patterns are generated.
Where Ck represents a candidate k-frequent sequence pattern, fk represents a k-frequent sequence pattern, and UkFk represents a union of all k-frequent sequence patterns.
Fig. 3 is a flow chart showing a comparison of a progressive immersion type visual data analysis system based on virtual reality technology and a conventional progressive type visual analysis system according to the present invention. The first few steps are the same, and the client sends and utilizes WebSocket technology to realize full duplex communication between the client and the server. The database to be mined and analyzed is selected through the message event of the WebSocket, then a GSP sequence pattern mining algorithm is operated to obtain pattern data which we want, and in the mining and analyzing process, user analysts can carry out algorithm steering and change attribute requirements of mining pattern data to obtain different data results. Table 1 is a specific explanation of four message events in a WebSocket.
TABLE 1
In a later step, the conventional progressive vision analysis system presents the mined pattern data on a conventional web page, and a data analyst performs interactive operations using a mouse. The progressive immersive visual data analysis system based on the virtual reality technology is provided, which visualizes mode data transmitted by WebSocket, constructs a virtual reality environment by HMD and VR equipment, visualizes the data in a world coordinate system defined by us, and the refinement steps are as follows:
3.1 To visualize data we need points to represent the data. There are many ways to do this, but one of the more straightforward ways is to use Sphere on built-in 3D assets of Unity and convert it to a so-called "prefab", which is essentially a template object that can be cloned and replicated. Modified as needed.
3.2 A prefab) we need to convert the custom Sphere in Unity to a prefab so that we can create their copy for the visualization as needed. The prefab object is created by right clicking on "events" in the "Project" window and selecting "prefab" in the "create" window in the open menu.
3.3 The mined data is passed through WebSocket and then the presefames are instantiated, we need to associate the prefabricated presefames we make with the script and then instruct the script to instantiate (clone). First we need to let the script know the preform to be placed, for which we need to declare a common GameObject variable in the script, dragging our created prefab preform from the "Project" window to this field.
3.4 Then using an instant method to carry out instantiation and conversion of position coordinates, continuously instantiating the transmitted mining mode data, reading attribute values of the mode data by a ToSingle method, endowing the attribute values with coordinate values, and then converting a world coordinate system into coordinate system values constructed by a Transform method to finally obtain visualized coordinate data.
As shown in fig. 4, interactive tasks such as changing the attribute size of the mining mode data, steering the mining algorithm, and viewing and analyzing the mined visual data are performed through the Handler handle. The method mainly uses VRTK to realize, and the refinement steps are as follows:
4.1 Introduction of stepvr and VRTK packages. A scene is newly built, the Camera with the Camera is deleted, and a Plane is newly built. An empty object is newly created, renamed as VRTK_SDK Manager, and a component VRTK_SDK Manager is added. Creating an empty object as a child object of the VRTK_SDK Manager, renaming the empty object to the VRTK_SDK Setup, and adding a component VRTK_SDK Setup.
4.2 Add the preform camera_rig as a child of vrtk_sdk Setup. Selecting the VRTK_SDK Manager, clicking "+" in the Setup, dragging the VRTK_SDK Setup to the position of "None (VRTK_SDK Setup)". The newly built empty object is renamed as VRTK_scripts. Two empty objects were created under vrtk_scripts, renamed LeftController (used to configure left handle), right handle, respectively.
4.3 A LeftController and a RightController are selected, and a left pointer and a right pointer are set, respectively. And then adding a self-contained partial script of the VRTK on the handle, so that interaction between the handle and an object can be controlled, when a user-defined function is needed, the user needs to write the script by himself or inherit the existing VRTK self-contained script to perform function expansion, and the used partial script functions are shown in a table 2.
TABLE 2
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (5)
1. The progressive immersive visual data analysis method based on the virtual reality technology is characterized by comprising the following steps of:
s1, carrying out message communication between a client and a server by utilizing a WebSocket, and selecting a corresponding database and operation by utilizing the transmitted message;
s2, running a GSP progressive sequence pattern mining algorithm to obtain data of customized content, and transmitting the data to a client through a WebSocket;
s3, using HTC VIVE equipment to realize a virtual reality immersive environment, and constructing a coordinate system according to the attribute of the mined data;
s4, carrying out data instantiation by using a Unity prefab, wherein the data are presented in a coordinate system, and carrying out data operation and interaction by using a handle;
wherein, the GSP progressive sequence pattern mining algorithm comprises:
step 1, taking a sequence pattern C1 with the length of 1 as an initial seed set candidate k-frequent sequence pattern;
step 2, generating a candidate sequence pattern Ck+1 with the length of k+1 through connection operation and cutting operation according to a seed set Ck with the length of k, then scanning a database, calculating the support degree of each candidate sequence pattern, and generating a sequence pattern Fk+1 with the length of k+1 as a new seed set;
repeating step 2 until no new sequence pattern or new candidate sequence pattern is generated;
wherein Ck represents a candidate k-frequent sequence pattern, fk represents a k-frequent sequence pattern;
in step S2, the GSP progressive sequence pattern mining algorithm generates candidate sequences by using the large sequences generated during the previous scanning when the database is scanned each time, calculates their support degree while scanning, and uses the candidate sequences satisfying the support degree as the large sequences for the next scanning.
2. The progressive immersion type visual data analysis method based on the virtual reality technology according to claim 1, wherein in step S1, the WebSocket realizes full duplex communication between a client and a server: after the socket is created, the socket can be responded through four events of onopen, onmessage, onclose and onerror; and (3) performing open connection operation through the address with good protocol, sending data to the server through a send () method, receiving the data returned by the server through an onmessage event, and after the data transmission is finished, performing close event by a WebSocket to close the connection, and triggering an onerror event if connection, processing, receiving and sending data fail.
3. The virtual reality technology based progressive immersive visual data analysis method according to claim 1, wherein in step S2, the GSP progressive sequence pattern mining algorithm is capable of designing the analysis algorithm to produce meaningful partial results in performing data mining, and is capable of giving the partial progressive results to the user for immediate viewing of the visual results of the partial content, while performing the remaining data mining analysis.
4. The method of claim 1, wherein in step S3, the attribute of the mined data includes a size, a support, and a number of the model data.
5. The method for analyzing progressive immersive visual data based on virtual reality technology according to claim 1, wherein in step S4, the visualization of data using a prefab of Unity is specifically: firstly, creating objects and prefbs, then importing mining data into Unity and carrying out prefab instantiation, visualizing the data, converting world coordinates and Camera coordinates and converting scale, and then displaying the data in a coordinate system of a VR environment through VR equipment for interaction.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104504159A (en) * | 2015-01-19 | 2015-04-08 | 齐鲁工业大学 | Application of multi-supporting-degree positive and negative sequence modes in clients' purchasing behavior analysis |
| CN105868314A (en) * | 2016-03-25 | 2016-08-17 | 齐鲁工业大学 | Multi-support-degree weighted negative sequence pattern data mining method |
| CN110262655A (en) * | 2019-05-21 | 2019-09-20 | 佛山科学技术学院 | A kind of collecting method and equipment of virtual reality fusion emulation experiment |
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| CN104504159A (en) * | 2015-01-19 | 2015-04-08 | 齐鲁工业大学 | Application of multi-supporting-degree positive and negative sequence modes in clients' purchasing behavior analysis |
| CN105868314A (en) * | 2016-03-25 | 2016-08-17 | 齐鲁工业大学 | Multi-support-degree weighted negative sequence pattern data mining method |
| CN110262655A (en) * | 2019-05-21 | 2019-09-20 | 佛山科学技术学院 | A kind of collecting method and equipment of virtual reality fusion emulation experiment |
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