Data analysis method, computer device and machine readable storage medium

Abstract

Embodiments of the present application provide a data analysis method, a computer device and a machine readable storage medium, and belong to the technical field of computers. The method comprises: determining a target data type and a model execution rule by analyzing a preset analysis model; determining standard data and to-be-analyzed data corresponding to the target data type based on the model execution rule; determining target standard data in the standard data that matches the to-be-analyzed data based on data elements of the to-be-analyzed data and weights of the data elements; and determining a data analysis result based on differences between each to-be-analyzed data and the target standard data. The data is analyzed by using an analysis model that can be adaptively adjusted based on actual requirements, thereby improving data analysis efficiency. The target standard data is determined by using data element weights, thereby ensuring the effectiveness of data analysis, automatically performing checking between the to-be-analyzed data and the target standard data, avoiding manual errors, and improving the accuracy of data analysis.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to a data analysis method, a computer device, and a machine-readable storage medium. Background Technology

[0002] Calculating the cost of metalworking parts requires a large amount of data, which needs to be carefully verified to ensure accuracy. Currently, the main technical method is manual verification, relying on the experience of technicians and comparing data with similar processed parts. Manual verification depends heavily on subjective factors such as the technician's experience, cognitive level, and attention to detail. It is also dependent on specific technicians, making it difficult to assess the accuracy of data across various dimensions, including materials, processing techniques, management, and transportation. Furthermore, the basis for judgment is not transparent. Verified data cannot be traced or used as a basis for subsequent verification. The standards for data judgment are inconsistent, and different departments have different data maintenance rules, making them unusable. Moreover, the standards for data judgment cannot be dynamically adjusted or expanded. Therefore, existing data analysis methods are inefficient and have high labor and time costs. Summary of the Invention

[0003] To address the aforementioned shortcomings in the prior art, the purpose of this invention is to provide a data analysis method, a computer device, and a machine-readable storage medium.

[0004] To achieve the above objectives, a first aspect of the present invention provides a data analysis method, comprising:

[0005] The pre-defined analysis model is analyzed to determine the type of target data and the model execution rules.

[0006] Based on the model execution rules, determine the data to be analyzed and the standard data corresponding to the target data type;

[0007] Based on the data elements and weights of the data to be analyzed, target standard data that matches the data to be analyzed is determined from the standard data.

[0008] The data analysis results are determined based on the differences between each data point to be analyzed and the target standard data.

[0009] In this embodiment of the invention, determining target standard data that matches the data to be analyzed from the standard data based on the data elements and weights of the data elements to be analyzed includes:

[0010] Identify the first data element in the data to be analyzed and the second data element in the standard data;

[0011] Determine the number of matches between the first data element and the second data element;

[0012] The target standard data is determined based on the number of matches and the weight of the first data element.

[0013] In this embodiment of the invention, before parsing the preset analysis model to determine the target data type and model execution rules, the method further includes:

[0014] The data type and the corresponding model execution rules are determined based on the input selection information.

[0015] Pre-defined analytical models are generated based on data types and model execution rules.

[0016] In this embodiment of the invention, the data analysis method further includes:

[0017] Retrieve all stored historical data;

[0018] Based on preset dimensional information, historical data is classified and the initial standard data for each data type is determined.

[0019] Based on the primary analytical requirements, the initial standard data for each data type were revised accordingly;

[0020] The revised initial standard data will be used as the standard data for the data type.

[0021] In this embodiment of the invention, the data analysis method further includes:

[0022] In the absence of standard data corresponding to the target data type, standard data for the target data type is determined and stored based on the second analysis requirement.

[0023] In this embodiment of the invention, determining the data analysis results based on the differences between each piece of data to be analyzed and the target standard data includes:

[0024] Determine the preset difference range based on the model execution rules;

[0025] If all differences are within the preset difference range, the data analysis result is determined to be normal.

[0026] If at least one difference is not within the preset difference range, the data analysis result is determined to be a data anomaly.

[0027] In this embodiment of the invention, after the step of determining that the data analysis result is data anomaly, the method further includes:

[0028] Determine the causes of data anomalies based on data analysis results;

[0029] All data to be analyzed that have data anomalies are marked according to the reasons for the data anomalies, resulting in anomaly status marking.

[0030] In this embodiment of the invention, the data analysis method further includes:

[0031] The data to be analyzed that contains anomalies is classified into anomalies according to the anomaly status label;

[0032] Obtain the data source corresponding to each anomaly type, and mark the source of each anomaly type based on the data source;

[0033] Output the data analysis results for all data to be analyzed, and output anomaly status markers and source markers for data to be analyzed that have anomalies.

[0034] A second aspect of the present invention provides a computer device comprising: a memory, a processor, and a program stored in the memory and executable on the processor, the program being configured to implement the steps of the data analysis method as described in the above embodiments.

[0035] A third aspect of the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the data analysis method as described in the above embodiments.

[0036] The above technical solution analyzes a pre-defined analytical model to determine the target data type and model execution rules. Based on these rules, it identifies the data to be analyzed and the standard data corresponding to the target data type. Based on the data elements and weights of the data to be analyzed, it determines the target standard data that matches the data to be analyzed. Finally, based on the differences between each data point to be analyzed and the target standard data, it determines the data analysis results. The analytical model, which can be adaptively adjusted according to actual needs, improves data analysis efficiency. Determining the target standard data through data element weights ensures the effectiveness of the data analysis, and the automated verification between the data to be analyzed and the target standard data avoids human error and improves the accuracy of the data analysis.

[0037] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0038] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0039] Figure 1 This is a flowchart illustrating a data analysis method according to an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram illustrating the selection of data types in an analysis model according to an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram illustrating the configuration of model execution rules in an analysis model according to an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram illustrating the initial standard data correction according to an embodiment of the present invention. Detailed Implementation

[0043] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0044] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0045] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0046] Figure 1 This is a flowchart illustrating a data analysis method according to an embodiment of the present invention. Figure 1 As shown, in this embodiment of the invention, a data analysis method is provided. Taking the application of this method to a processor as an example, the method may include the following steps:

[0047] Step S100: Analyze the preset analysis model to determine the target data type and model execution rules;

[0048] In this embodiment, it should be noted that the analysis model is the basic principle for analyzing data. This includes the data types corresponding to the data to be analyzed, and the execution rules that must be followed when analyzing that data type. This analysis model is pre-set based on actual needs. The target data types are the data types included in the analysis model, such as material data, management data, transportation data, and processing data. The model execution rules are the execution rules included in the analysis model, such as the execution time and frequency during data analysis, the allowable error range for result judgment, and whether a data analysis report needs to be generated. By parsing the analysis model, the types of data to be analyzed and the model execution rules for the analysis can be determined.

[0049] Step S200: Based on the model execution rules, determine the data to be analyzed and the standard data corresponding to the target data type;

[0050] It's important to note that the data to be analyzed is the data to be analyzed. The most crucial aspect of data analysis is verifying this data; the verification standard is the standard data. Standard data is pre-stored in separate databases for different data types. Model execution rules define the time and frequency for initiating data analysis. Based on these rules, the system queries the data to be analyzed corresponding to the target data type within the current business system, and then queries the corresponding standard data in the pre-stored databases.

[0051] Step S300: Based on the data elements and weights of the data to be analyzed, determine the target standard data that matches the data to be analyzed in the standard data.

[0052] It should be noted that each piece of data to be analyzed or standard data includes one or more data elements. For example, elements such as material, shape, weight, and length. Since different data elements have varying degrees of influence on the analysis results, different weights are pre-assigned to different data elements. Data elements with a greater impact on the analysis results have higher weights, and those with a smaller impact have lower weights. The data elements of the data to be analyzed are matched one-to-one with the data elements in the standard data. Based on the weights of each data element in the data to be analyzed, the target standard data with the highest matching degree is finally selected from all the standard data. It can be understood that there is at least one piece of data to be analyzed. If there are multiple pieces of data to be analyzed, the target standard data matching each piece of data is determined based on the data elements and their weights.

[0053] Step S400: Determine the data analysis results based on the differences between each data to be analyzed and the target standard data.

[0054] It should be noted that the target standard data can represent the expected value of the data under normal conditions. By comparing the data to be analyzed with the target standard data, the difference between the data to be analyzed and the target standard data is determined, thereby judging whether there is anomaly in the data to be analyzed, and finally obtaining the data analysis results of the data to be analyzed.

[0055] In the above scheme, a pre-set analysis model is analyzed to determine the target data type and model execution rules. Based on the model execution rules, the data to be analyzed and standard data corresponding to the target data type are determined. Based on the data elements and weights of the data elements to be analyzed, target standard data matching the data to be analyzed is determined from the standard data. Based on the differences between each data element to be analyzed and the target standard data, the data analysis results are determined. The analysis model, which can be adaptively adjusted according to actual needs, is used to analyze the data, improving data analysis efficiency. The target standard data is determined through data element weights to ensure the effectiveness of the data analysis, and the automatic verification between the data to be analyzed and the target standard data avoids human error and improves the accuracy of the data analysis.

[0056] In one embodiment, determining target standard data that matches the data to be analyzed from the standard data based on the data elements and weights of the data elements to be analyzed includes:

[0057] Identify the first data element in the data to be analyzed and the second data element in the standard data;

[0058] Determine the number of matches between the first data element and the second data element;

[0059] The target standard data is determined based on the number of matches and the weight of the first data element.

[0060] It should be noted that the data elements in the data to be analyzed are the first data elements; the data elements in the standard data are the second data elements. In this embodiment, the number of matches between data elements is further considered. The more matches between the data elements in the data to be analyzed and the standard data, the higher the correlation between the data to be analyzed and the standard data. There is no strict priority restriction between the two methods of determining the target standard data based on the number of matches and based on the weight of the first element; adjustments can be made adaptively based on the actual situation. For example, the number of matches can be given higher priority, the weight of the first element can be given higher priority, or the two methods can have equal priority. It is understood that when the number of matches between the first and second data elements is higher and the weight of the first data element is higher, the correlation between the standard data corresponding to the second data element and the data to be analyzed is considered higher, and the standard data with the highest correlation is taken as the target standard data.

[0061] In this embodiment, both the number of matches and the weight of the first data element are considered to ensure the accuracy of the target standard data used as the verification standard, reduce data verification errors, and improve the effectiveness of data analysis.

[0062] In one embodiment, before parsing a pre-defined analysis model to determine the target data type and model execution rules, the method further includes:

[0063] The data type and the corresponding model execution rules are determined based on the input selection information.

[0064] Pre-defined analytical models are generated based on data types and model execution rules.

[0065] In this embodiment, it should be noted that the analysis model is a flexibly configurable model. By inputting selection information, the type of data to be analyzed and the model execution rules to be followed during the analysis of that data type can be determined. (Reference) Figure 2 , Figure 2 This includes options for data types. First, select the data type, then choose the subcategories within that type, such as material data, transportation data, management data, and processing data. In one embodiment, the subcategories can be further subdivided, such as material type and shape under material data. After selecting the data type, you then set the model execution rules for that type of data, referring to [reference needed]. Figure 3 , Figure 3 This includes the settings for model execution rules. For example, execution time and frequency, allowable error range, and whether a data analysis report needs to be generated. Different data types can be mapped to different model execution rules, allowing for adaptive adjustments based on actual operational needs.

[0066] In this embodiment, the analysis model supports configurability, customization, and graphical processing. It supports user-customized model execution rules, which are abstracted into analysis models through code development. This allows for data analysis needs across different BOM (Bill of Material) structures, materials, and dimensions to be met with a single code development effort.

[0067] In one embodiment, the data analysis method further includes:

[0068] Retrieve all stored historical data;

[0069] Based on preset dimensional information, historical data is classified and the initial standard data for each data type is determined.

[0070] Based on the primary analytical requirements, the initial standard data for each data type were revised accordingly;

[0071] The revised initial standard data will be used as the standard data for the data type.

[0072] In this embodiment, it should be noted that historical data refers to standard data prior to the current data analysis, data to be analyzed where the analysis result is normal, and data to be analyzed where the analysis result is abnormal but has been corrected. In other words, historical data is the expected value of data under normal conditions. Preset dimension information may include dimensions such as region, time, department, and version. Historical data can be categorized by time based on different preset dimension information to provide data standards for data to be analyzed under different dimensions. After determining the initial standard data corresponding to each data type, the initial standard data for each data type can be corrected based on the first analysis requirement. The first analysis requirement is determined according to the actual application, for example, a data standard specifically set for a certain project, or a standard that needs to be changed over time or due to market changes. The corrected initial standard data is then used as the standard data for that data type. (See reference...) Figure 4 , Figure 4 The middle section is a visual interface for correcting initial standard data. For example, the grade, specification, surface treatment method, and processing method in the standard data can be corrected by correcting the grade, specification, surface treatment method, and processing method, respectively.

[0073] In this embodiment, the standard data is flexibly configured through the first analysis requirement, so that the data analysis requirements of the data to be analyzed can meet the actual application needs to the greatest extent and improve the practicality of data analysis.

[0074] In one embodiment, the data analysis method further includes:

[0075] In the absence of standard data corresponding to the target data type, standard data for the target data type is determined and stored based on the second analysis requirement.

[0076] It should be noted that when querying standard data based on the target data type, if the standard data cannot be found, it indicates that the standard data does not exist in the preset database, and the standard data corresponding to the target data type needs to be imported. In this embodiment, when no standard data corresponding to the target data type exists, standard data for the target data type based on the second analysis requirement is used, and this standard data is associated with and stored with the target data type. The second analysis requirement is the basis for importing standard data, and may include the standard data itself to be imported, or it may include the data source address of the standard data to be imported.

[0077] In this embodiment, if standard data corresponding to the target data type cannot be found, the corresponding standard data can be flexibly imported based on the second analysis requirement, thereby improving the flexibility and adaptability of data analysis.

[0078] In one embodiment, the data analysis results are determined based on the differences between each piece of data to be analyzed and the target standard data, including:

[0079] Determine the preset difference range based on the model execution rules;

[0080] If all differences are within the preset difference range, the data analysis result is determined to be normal.

[0081] If at least one difference is not within the preset difference range, the data analysis result is determined to be a data anomaly.

[0082] In this embodiment, it should be noted that when comparing the data to be analyzed with the target standard data, a certain degree of data deviation is permissible. This deviation has a negligible impact on the product's production and application. This embodiment limits this deviation by setting a preset difference range, which is further restricted by the model execution rules within the analysis model. After determining the differences between each piece of data to be analyzed and the target standard data, if all differences are within the preset difference range, the data analysis result is considered normal; if one or more differences are outside the preset difference range, the data analysis result is considered abnormal. Differences outside the preset difference range can include: unreasonable data (e.g., length value recorded as weight); missing data; and data deviation (e.g., exceeding the deviation range).

[0083] In this embodiment, the allowable error range during data analysis is limited by a preset difference range, thereby strictly controlling the accuracy of data analysis.

[0084] In one embodiment, after the step of determining that the data analysis result is a data anomaly, the method further includes:

[0085] Determine the causes of data anomalies based on data analysis results;

[0086] All data to be analyzed that have data anomalies are marked according to the reasons for the data anomalies, resulting in anomaly status marking.

[0087] In this embodiment, it should be noted that when the data analysis result is determined to be data anomaly, the cause of the data anomaly is determined based on the data analysis result. The cause of the data anomaly can be determined based on the difference between the data to be analyzed and the difference that is not within a preset difference range. Different causes of data anomalies are defined for different data differences. Thus, all the data to be analyzed that have data anomalies are marked according to the cause of the data anomaly, resulting in anomaly status markings. For example, different anomaly status markings such as deviation, data anomaly, and data missing.

[0088] In this embodiment, different abnormal status labels are obtained by marking the reasons for data anomalies, which can provide an effective reference for subsequent anomaly repair or anomaly tracing.

[0089] In one embodiment, the data analysis method further includes:

[0090] The data to be analyzed that contains anomalies is classified into anomalies according to the anomaly status label;

[0091] Obtain the data source corresponding to each anomaly type, and mark the source of each anomaly type based on the data source;

[0092] Output the data analysis results for all data to be analyzed, and output anomaly status markers and source markers for data to be analyzed that have anomalies.

[0093] In this embodiment, it should be noted that there are multiple types of data anomalies. Anomalies can be categorized based on anomaly status markers to identify common problems. For each anomaly type, a source marker is assigned to each anomaly type. For example, if the data source is a system push, the source marker is "System Push Data Anomaly"; if the data source is manual entry, the source marker is "Manual Entry Anomaly." This determines whether data with common problems originate from the same source, quickly pinpointing the root cause of the anomaly and enabling comprehensive error analysis. In one embodiment, when a data analysis report needs to be generated, the underlying causes behind the data can be explored based on the anomaly status markers and source markers to infer and avoid potential future problems, which can then be used as suggestions in the data analysis report.

[0094] In this embodiment, by classifying anomalies and marking the source of different anomaly types, an effective reference basis is provided for data tracing, thereby improving the comprehensiveness of data analysis.

[0095] This invention provides a computer device, which includes a memory, a processor, and a program stored in the memory and executable on the processor. The program is configured to implement the steps of the data analysis method as described in the above embodiments.

[0096] This invention provides a machine-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the data analysis method described in the above embodiments.

[0097] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0102] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0103] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0104] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0105] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A data analysis method, characterized in that, include: The pre-defined analysis model is analyzed to determine the type of target data and the model execution rules. Based on the model execution rules, determine the data to be analyzed and the standard data corresponding to the target data type; Based on the data elements and weights of the data elements to be analyzed, target standard data that matches the data to be analyzed in the standard data are determined; The data analysis results are determined based on the differences between each of the data to be analyzed and the target standard data; The step of determining the target standard data that matches the data to be analyzed in the standard data based on the data elements and weights of the data elements includes: Determine the first data element in the data to be analyzed and the second data element in the standard data; Determine the number of matches between the first data element and the second data element; The target standard data is determined based on the number of matches and the weight of the first data element.

2. The data analysis method according to claim 1, characterized in that, Before parsing the preset analysis model to determine the target data type and model execution rules, the process also includes: The data type and the corresponding model execution rule are determined based on the input selection information. The preset analysis model is generated based on the data type and the model execution rules.

3. The data analysis method according to claim 1, characterized in that, The data analysis method also includes: Retrieve all stored historical data; The historical data is classified based on preset dimensional information, and the initial standard data for each data type is determined. Based on the first analysis requirement, the initial standard data for each of the aforementioned data types are corrected respectively; The revised initial standard data will be used as the standard data for the data type.

4. The data analysis method according to claim 1, characterized in that, The data analysis method also includes: In the absence of standard data corresponding to the target data type, standard data for the target data type is determined and stored based on the second analysis requirement.

5. The data analysis method according to claim 1, characterized in that, The determination of data analysis results based on the differences between each of the data to be analyzed and the target standard data includes: The preset difference range is determined based on the model execution rules; If all the differences are within the preset difference range, the data analysis result is determined to be normal. If at least one of the differences is not within the preset difference range, the data analysis result is determined to be a data anomaly.

6. The data analysis method according to claim 5, characterized in that, Following the step of determining that the data analysis result is an anomaly, the method further includes: The cause of the data anomaly was determined based on the data analysis results. All data to be analyzed that exhibits data anomalies are marked according to the stated reasons for the data anomalies, resulting in anomaly status markings.

7. The data analysis method according to claim 6, characterized in that, The data analysis method also includes: The data to be analyzed that contains anomalies is classified into anomalies according to the aforementioned anomaly status markers; Obtain the data source corresponding to each anomaly type, and mark the source of each anomaly type based on the data source; Output the data analysis results for all data to be analyzed, and output the anomaly status marker and the source marker based on the data to be analyzed that has data anomalies.

8. A computer device, characterized in that, The computer device includes: a memory, a processor, and a program stored in the memory and executable on the processor, the program being configured to implement the steps of the data analysis method as described in any one of claims 1 to 7.

9. A machine-readable storage medium having instructions stored thereon for causing a machine to perform the steps of the data analysis method as described in any one of claims 1 to 7.

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