A skyline query method for incomplete data based on classification index

Abstract

The present invention discloses a skyline query method for incomplete data based on classification indexing. First, given an incomplete data set, the incomplete data set is classified using an incomplete data classification indexing algorithm. The classification indexing algorithm standardizes the data set and improves query efficiency. A skyline query is then performed on the classified data set. During the skyline query, optimal pruning points are introduced to reduce the amount of data. The incomplete data skyline query algorithm is then used to complete the skyline query on the data set and locate the global skyline point. The incomplete data classification indexing algorithm and the incomplete data skyline query algorithm can effectively reduce redundant data in the data set, thereby improving skyline query efficiency.

Description

Technical Field

[0001] The present invention relates to the field of skyline query of incomplete data, and in particular to a skyline query method of incomplete data based on classification index. Background Art

[0002] Skyline querying with incomplete data is a significant research issue in the field of spatial databases. In the study of skyline queries, datasets often lack data in certain dimensions. Therefore, among the many branches of skyline querying, skyline querying with incomplete data is a crucial one. With the development of the mobile internet, skyline querying with incomplete data has gained widespread application. In real-world scenarios, incomplete data querying has broad applications in marketing decision-making, financial risk management, and product design and optimization. In marketing, companies often need to select optimal target customers from databases containing incomplete customer data. Using incomplete data skyline querying can help companies identify target customers that perform well across various marketing metrics, thereby optimizing advertising and resource allocation. In finance, assessing the risk and return of investment portfolios is crucial. Using incomplete data skyline querying, investors can identify portfolios that perform well across different risk metrics, enabling them to make more informed investment decisions. Skyline querying with incomplete data holds significant research value.

[0003] The existing skyline query methods for incomplete data have significant limitations. Data classification is not implemented in the existing incomplete data skyline query process, and there is a lot of redundant data in the query process, which leads to low efficiency of incomplete data skyline query. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a skyline query method for incomplete data based on classification indexing. Its purpose is to complete the classification of incomplete data through an incomplete data classification indexing algorithm, introduce optimal pruning points into the incomplete data skyline query algorithm, and filter out redundant data. The proposed algorithm can improve the efficiency of incomplete data skyline query.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a skyline query method for incomplete data based on classification index, the method comprising the following steps:

[0006] 1. Input the incomplete data set into the incomplete data classification index. Split the dimension data of each incomplete data item and store them in the intermediate nodes of the index. Then, determine if the data in each intermediate node is missing and label it according to the missingness. Finally, classify the incomplete data based on the different label values ​​of each dimension.

[0007] 2. Perform incomplete data skyline query based on classified incomplete data. Calculate the local skyline points in each category. Then, extract the optimal dimensional values ​​of the local skyline points in each category and form the optimal pruning points. The optimal pruning points are introduced into each category and compared with the local skyline points for dominance. Local skyline points not dominated by the optimal pruning points become candidate skyline points, while local skyline points dominated by the optimal pruning points become shadow points. Dominance comparison is then performed again between the local skyline points and the shadow points. Finally, the undominated local skyline points become the global skyline points.

[0008] Furthermore, in step 1 of the skyline query method for incomplete data based on classification index, the data is labeled according to different missing conditions. When the dimension data is missing, the label value is 0; when the dimension data is incorrect, the label value is 1. Data with the same label value for each dimension is classified into one category.

[0009] Furthermore, in step 2 of the skyline query method for incomplete data based on classified indexes, the dominance comparison is the optimal comparison between two data. When the dimension data of data a is better than the corresponding dimension data of data b, data a dominates data b.

[0010] Furthermore, in step 2 of the skyline query method for incomplete data based on classification index, the local skyline point is a data point obtained after dominance comparison between the data in each classification bucket, and is a local skyline point only if it is not dominated by any data.

[0011] Furthermore, in step 2 of the incomplete data skyline query method based on classified index, the dominance comparison between local skyline points and shadow points is performed because the dominance relationship of incomplete data is not transitive, and the shadow points may contain data that dominates the local skyline points. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention is a flowchart of a spatial keyword query method with exclusion keywords.

[0013] Figure 2 Schematic diagram of an incomplete data set of the present invention.

[0014] Figure 3 It is a schematic diagram of the incomplete data classification index of the present invention.

[0015] Figure 4 Schematic diagram of the incomplete data skyline query algorithm of the present invention. Implementation Method

[0016] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the examples described are only some implementation examples of the present invention, rather than all embodiments. In addition, it should be understood that after reading this invention, those skilled in the art may make various modifications or changes to the present invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0017] The present invention discloses a skyline query method based on incomplete data of classification index, which mainly includes two algorithms. The operation process is as follows: Figure 1 As shown, Figure 2 The incomplete data set shown is entered into Figure 3 The incomplete data classification index algorithm shown in the figure is used for classification. After the classification is completed, the data is input into Figure 4 The incomplete data skyline query algorithm shown in Figure 1 performs a skyline query and ultimately finds the global skyline point. The specific steps of the two algorithms are as follows.

[0018] Algorithm 1: Incomplete data classification indexing algorithm.

[0019] Input: Incomplete dataset.

[0020] Output: Incomplete dataset after classification.

[0021] 1. Store the incomplete data in the root node in sequence.

[0022] 2. Split each dimension of the incomplete data and store them in internal nodes in sequence.

[0023] 3. Label the nodes based on the missing data. If the data is missing, it is assigned to the left child node and assigned a label value of 0. If the data is not missing, it is assigned to the right child node and assigned a label value of 1.

[0024] 4. Complete the classification of incomplete data according to the different label values ​​of each dimension and export the data to each classification bucket through the horizontal index of the leaf node.

[0025] 5. After the incomplete data is classified using the incomplete data classification indexing algorithm, an incomplete data skyline query is performed based on the classified data.

[0026] Algorithm 2: Incomplete data skyline query algorithm.

[0027] Input: Incomplete dataset after classification.

[0028] Output: global skyline points.

[0029] 1. Perform dominance comparison in the classification bucket and calculate the local skyline points in the classification bucket.

[0030] 2. Take the optimal value of each dimension of the local skyline point in the classification bucket to form the best pruning point, and add the best pruning point to other classification buckets.

[0031] 3. Perform a dominance comparison between the best pruning point and the local skyline points. If a dominance relationship exists, perform a dominance comparison between the points that make up the best pruning point and the dominated points. If dominance still exists, the dominated local skyline point is changed to a shadow point.

[0032] 4. After dominance comparison, the undominated local skyline points become candidate points. The candidate points are compared with the shadow points for dominance, and the dominated candidate skyline points are deleted. The undominated candidate skyline points become the global skyline points.

[0033] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A skyline query method for incomplete data based on classification index, characterized by The following steps are involved: Step 1: After obtaining the data set, the incomplete data set is classified using the incomplete data classification index algorithm. First, the incomplete data is split into dimensions and stored in the intermediate nodes. The missing dimension data in the node is determined and labeled according to the missing data. The classification is completed based on the different labels of each dimension. Finally, the data is exported through the horizontal index of the leaf node. Step 2: After completing the incomplete data classification, a skyline query is performed using the incomplete data skyline query algorithm. In the algorithm, the best pruning points are added to different categories. The best pruning points and local skyline points are compared for dominance. The dominated local skyline points become shadow points, and the local skyline points not dominated by the best pruning points become candidate skyline points. The candidate skyline points are further compared for dominance to select the global skyline point.

2. The skyline query method for incomplete data based on classified index according to claim 1, characterized in that: The incomplete data skyline query algorithm is used to perform skyline query on the classified incomplete data. The optimal pruning point is introduced in the query process to filter out redundant data.

3. The skyline query method for incomplete data based on classified index according to claim 1, characterized in that: The optimal pruning point is composed of the optimal value of each dimension of the local skyline point.

Images