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The cost and the speedness of data access on a query depend on the numbers of attributes. In fact the query function will search for data from various attributes in the database table, whereas not all the attributes are needed on the accessed tables. To avoid the not necessary attributes, Vertical Fragmentation methods can be used. The purpose of this research aims to compare the vertical fragmentation of database tables using Bond Energy algorithm and Graph-Based Vertical Partitioning algorithm to create a distributed database. The results of both algorithms will determine which attributes will be avoided. Then, the results of the two algorithms are compared and evaluated using Partition Evaluator to show the amount of the access data costs that are used, so to find which is the better algorithm to be applied. Based on some experimental results, GBVP algorithm showed a better performance with higher Partition Evaluator value respectively at 4,98; 10,41; 17,95; 4,64; and 14,07 as well as the lower value of execution time respectively at 0,003; 0,002; 0,002; 0,002; 0,003 and 0,003. [ABSTRACT FROM AUTHOR]

Distributed database systems are increasingly becoming the dominant tools for data management. However, in these systems, the sites are remote and exchange a huge amount of data, which leads to bottlenecks as well as large disk accesses in data transfers that may be irrelevant. Query processing optimization techniques are an important concern for system administrators to improve the performance of distributed database systems (DDBS). Vertical fragmentation is a suitable solution but its complication lies in the large number of alternatives to obtain an optimal solution. This paper provides a new approach better suited to the problem of vertical fragmentation by the Kmeans classification algorithm but with our new adequate distance. To validate our approach, we compared our solution first with a vertical fragmentation algorithm called VFAR and second with the same k-means algorithm with the hamming distance. [ABSTRACT FROM AUTHOR]

Distributed database systems are gaining importance due to the production in massive amount of data. The efficacy of such systems is highly dependent upon the design of the system. To increase the effectiveness and efficiency of distributed databases, two processes are mainly employed i.e. fragmentation and allocation. Fragmentations can be vertical or horizontal. This work focuses on vertical fragmentation design methods. In this paper, a novel differential bond energy (DBE) algorithm is proposed with objective to determine optimal partition point. The performance of proposed algorithm is compared with classical bond energy algorithm (BEA) on basis of global affinity measure (GAM) value. Results are depicted in form of line graphs. The mean difference in GAM values for both algorithms are also illustrated. The experimental results portrays that DBE is suitable for vertical fragmentation of high dimensional problems as it attain high GAM value as compared to BEA on various datasets.

Distributed Multimedia Database Systems have become an indispensable part of modern world organizations that increased demand for reliable, scalable, and expeditiously accessible information processing systems, data has evolved in multiple media forms having found many application areas across industries that calls for optimal storage, processing and retrieval methodologies in a distributed fashion. The solution mainly relies on the optimization of database design structure in which data fragmentation, allocation and replication play eminent roles. The presented scheme employs a method of vertical fragmentation using enhanced CRUD matrix and Fibonacci heap to efficiently fragment the database into clusters. The fragments are then allocated and replicated at different network nodes depending on the manipulates and reads operation at respective sites, taking into consideration the cost factor. With the use of Fibonacci heap, the amortized complexity of the proposed algorithm has come down to O(E + V log V) in contrast to the previous works of enhanced Prims algorithm in vertical fragmentation which offered a complexity of O(E log V) where E denotes the number of edges and V, the number of vertices. This approach generates all the fragments at once and without the use of any predetermined parameters and does not involve the use of a query log. The proposed approach also considers communication and site storage costs for optimal allocation and replication thus minimizing the overall system costs. [ABSTRACT FROM AUTHOR]

Distributed database systems are gaining importance due to the production in massive amount of data. The efficacy of such systems is highly dependent upon the design of the system. To increase the effectiveness and efficiency of distributed databases, two processes are mainly employed i.e. fragmentation and allocation. Fragmentations can be vertical or horizontal. This work focuses on vertical fragmentation design methods. In this paper, a novel differential bond energy (DBE) algorithm is proposed with objective to determine optimal partition point. The performance of proposed algorithm is compared with classical bond energy algorithm (BEA) on basis of global affinity measure (GAM) value. Results are depicted in form of line graphs. The mean difference in GAM values for both algorithms are also illustrated. The experimental results portrays that DBE is suitable for vertical fragmentation of high dimensional problems as it attain high GAM value as compared to BEA on various datasets. [ABSTRACT FROM AUTHOR]

Analyses of multinational enterprises have generally shifted from a capital‐market perspective to viewing them as real production units. Yet, we still have difficulties in answering a basic question: What do multinational enterprises do? Here, we seek some broad, general answers about the structure of multinational firms' international activities. By structure, we include (a) the geographic distribution of a firm's activities, (b) activities performed by various branches, (c) where do foreign affiliates sell their outputs and purchase their inputs and (d) interactions among establishments. Much of our analysis relates to which of two archetypes is dominant: (1) horizontal structures in which foreign affiliates replicate the firm's core activities and serve local and regional markets and (2) vertical structures in which foreign affiliates perform different activities and act as links in a global production chain. Examining this from a variety of perspectives, we find that horizontal replication dominates vertical fragmentation. The final section of the paper discusses intangible assets and other service flows between parents and affiliates. Smoking‐gun evidence suggests that these are large, particularly compared to intra‐firm trade in goods. Despite this, their importance is undervalued, likely due to severe measurement difficulties. [ABSTRACT FROM AUTHOR]

Emerging medical imaging applications in healthcare, the number and volume of medical images is growing dramatically. Information needs of users in such circumstances, either for clinical or research activities, make the role of powerful medical image search engines more significant. In this paper, a text-based multi-dimensional medical image indexing technique is proposed in which correlation of the features-usages (according to the user's queries) is considered to provide an off-the content indexing while taking users' interestingness into account. Assuming that each medical image has some extracted features (e.g., based on the DICOM standard), correlations of the features are discovered by performing data mining techniques (i.e., quantitative association pattern discovery), on the history of users' queries as a data set. Then, based on the pairwise correlation of the features of medical images (a.k.a. Affinity), set of the all features is fragmented into subsets (using method like the vertical fragmentation of the tables in distribution of relational DBs). After that, each of these subsets of the features turn into a hierarchy of the features (by applying a hierarchical clustering algorithm on that subset), subsequently all of these distinct hierarchies together make a multi-dimensional structure of the features of medical images, which is in fact the proposed text-based (feature-based) multi-dimensional index structure. Constructing and using such text-based multi-dimensional index structure via its specific required operations, medical image retrieval process would be improved in the underlying medical image search engine. Generally, an indexing technique is to provide a logical representation of documents in order to optimize the retrieval process. The proposed indexing technique is designed such that can improve retrieval of medical images in a medical image search engine in terms of its effectiveness and efficiency. Considering correlation of the features of the image would semantically improve precision (effectiveness) of the retrieval process, while traversing them through the hierarchy in one dimension would try to optimize (i.e., minimize) the resources to have a better efficiency. The proposed text-based multi-dimensional indexing technique is implemented using the open source search engine Lucene, and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier platform (available for the benchmarking of information retrieval systems) and other the most related indexing techniques. Evaluation results of memory usage and time complexity analysis, beside the experimental evaluations efficiency and effectiveness measures show that the proposed multi-dimensional indexing technique significantly improves both efficiency and effectiveness for a medical image search engine. [ABSTRACT FROM AUTHOR]

Over the last decade, a huge number of medical visual data are widely used for diagnose, treatment, and follow-up. Retrieving needed medical image(s) from a huge number of images is one of the most widely used features in medical information systems, especially in medical image search engines. Indexing as part of search engines (or information retrieval systems), increases the speed (efficiency) of search and the information retrieval process. In this paper, a multidimensional indexing technique for medical images is presented that can improve effectiveness and efficiency of medical image search engines. The structure of the proposed multi-dimensional indexing technique and its main operations (i.e., creation, insertion, deletion and search) is designed and evaluated. In order to create this multidimensional index, the "vertical fragmentation" approach (which is usually applied for distributed database design) is used to determine the each of dimensions; roughly speaking, dimensions are different aspects of the medical images for a/some information need (e.g., image type and format, type of disorder, etc.). Accordingly, data structure of the proposed multidimensional indexing technique (which is named "Diamond") is formed by using the vertical fragmentation of medical image attributes (to differentiate the dimensions), and then using agglomerative hierarchical clustering to build up the hierarchy in each dimension. The proposed multi-dimensional indexing technique is implemented using the open-source search engine Lucene and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier Platform (available for the benchmarking of information retrieval systems). In this evaluation, efficiency and effectiveness measures of the proposed multidimensional indexing technique (Diamond) are evaluated experimentally, beside the analysis of the designed data structure and its operations. For the experimental evaluation data set, images from Tabriz Behbood Hospital and a subset of TCIA images were used. Experimental evaluation results show that Diamond, the proposed multidimensional indexing technique improves both efficiency and effectiveness for a medical image search engine. [ABSTRACT FROM AUTHOR]

Vertical fragmentation technique is used to enhance the performance of database system and reduce the number of access to irrelevant instances by splitting a table or relation into different fragments vertically. The partitioning design can be derived using FPClose algorithm, which is a data mining algorithm used to extract the frequent closed itemsets in a dataset. A new design approach is implemented to perform fragmentation. A benchmark with different minimum support levels is tested. The obtained results from FPClose algorithm are compared with the Apriori algorithm.

In the literature of distributed database system (DDBS), several methods sought to meet the satisfactory reduction on transmission cost (TC) and were seen substantially effective. Data Fragmentation, site clustering, and data distribution have been considered the major leading TC-mitigating influencers. Sites clustering, on one hand, aims at grouping sites appropriately according to certain similarity metrics. On the other hand, data distribution seeks to allocate the fragmented data into clusters/sites properly. The combination of these methods, however, has been shown fruitful concerning TC reduction along with network overheads. In this work, hence, a heuristic clustering-based approach for vertical fragmentation and data allocation is meticulously designed. The focus is directed on proposing an influential solution for improving relational DDBS throughputs across an aggregated similarity-based fragmentation procedure, an effective site clustering and a greedy algorithm-driven data allocation model. Moreover, the data replication is also considered so TC is further minimized. Through the delineated-below evaluation, the findings of experimental implementation have been observed to be promising.

Classical databases represent the traditional DBMS's and the most widely used DBMS in the world of databases and information systems; they have been regarded as the best systems for managing data. Today with the growth of data for both applications and consumers, and its openness to the public, traditional databases are not able to meet the needs of a large number of applications, including Online Analytical Processing models: OLAP data processing and Business Intelligence analysis. As a result, many dedicated DBMS have emerged like: Column Store, In Memory and NoSQL databases. They meet users' expectations and fit well with current needs. Consequently, the scope of classical databases has become increasingly restricted to handle Online Transaction Processing OLTP models and small problems. However, those dedicated solutions hardly cope with rich features of DBMS like simplicity, flexibility or scalability. They remain limited to mainly process single database models, and users cannot deal with both OLTP and OLAP queries in a single environment. To deal with this problem, vertical fragmentation is the best way to effectively handle the OLAP model, but this technique fails to handle some analytical queries with low selectivity, presenting poor results in some cases. In this perspective, we propose a new vertical fragmentation design T-Plotter which makes it possible to deal effectively with the whole of analytical queries and improve the performance of DBMS's to process the OLAP data models. [ABSTRACT FROM AUTHOR]

The need to design an optimally distributed database is increasingly important with the growth of information technology and computer networks. However, designing a distributed database is an extremely complex process due to a large number of geographically distributed sites and database relations. Moreover, decreasing communication costs and query response time should be taken into consideration. There are three main techniques applied to design a distributed database, namely Fragmentation, Data allocation, and Replication. It is notable that these techniques are often treated separately and rarely processed together. Some available allocation methods are applied regardless of how the fragmentation technique is performed or replication process is adopted. In contrast, other fragmentation techniques do not consider the allocation or the replication techniques. Therefore, the first and foremost step for designing an optimal database is to develop a comprehensive understanding of the current fragmentation, replication, and allocation techniques and their disadvantages. This article presents an attempt to fulfill this step by proposing a comprehensive taxonomy of the available fragmentation and allocation techniques in distributed database design. The article also discusses some case studies of these techniques for a deeper understanding of its achievements and limitations. [ABSTRACT FROM AUTHOR]

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented.

Data fragmentation and allocation has for long proven to be an efficient technique for improving the performance of distributed database systems’ (DDBSs). A crucial feature of any successful DDBS design revolves around placing an intrinsic emphasis on minimizing transmission costs (TC). This work; therefore, focuses on improving distribution performance based on transmission cost minimization. To do so, data fragmentation and allocation techniques are utilized in this work along with investigating several data replication scenarios. Moreover, site clustering is leveraged with the aim of producing a minimum possible number of highly balanced clusters. By doing so, TC is proved to be immensely reduced, as depicted in performance evaluation. DDBS performance is measured using TC objective function. An inclusive evaluation has been made in a simulated environment, and the compared results have demonstrated the superiority and efficacy of the proposed approach on reducing TC.

Many scholars have attempted to use an encryption method to resolve the problem of data leakage in data outsourcing storage. However, encryption methods reduce data availability and are inefficient. Vertical fragmentation perfectly solves this problem. It was first used to improve the access performance of the relational database, and nowadays some researchers employ it for privacy protection. However, there are some problems that remain to be solved with the vertical fragmentation method for privacy protection in the relational database. First, current vertical fragmentation methods for privacy protection require the user to manually define privacy constraints, which is difficult to achieve in practice. Second, there are many vertical fragmentation solutions that can meet privacy constraints; however, there are currently no quantitative evaluation criteria evaluating how effectively solutions can protect privacy more effectively. In this article, we introduce the concept of information entropy to quantify privacy in vertical fragmentation, so we can automatically discover privacy constraints. Based on this, we propose a privacy protection model with a minimum entropy fragmentation algorithm to achieve minimal privacy disclosure of vertical fragmentation. Experimental results show that our method is suitable for privacy protection with a lower overhead.

Deforestation since the 1980s has led to substantial loss of ecological services in China. As a responsive strategy, China launched the most ambitious reforestation efforts in the developing world. However, like many other environmental policies, forestry policy has not been effectively implemented, mainly due to the fragmentation nature in China's environmental governance institution. This paper highlights the impact of central-local conflicts on forestry policy implementation. With insufficient motivation, local governments tend to minimize their efforts in planning, monitoring, and supporting reforestation activities, which poses great challenges on the sustainability of the reforestation benefits. With extensive field experiences, this paper also raises three innovative strategies to solve the financial dilemma that leads to the effort minimization phenomenon, with both the advantages and disadvantages for each strategy critically discussed. It finally recommends ways by which the central government could improve design of reforestation policies, or other large-scale ecological programs, which involve local governments as a key liaison. [ABSTRACT FROM AUTHOR]

The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification, and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show that some of the common properties of real data can be leveraged in order to greatly ameliorate the negative effects of the curse of dimensionality. In real data sets, many dimensions contain high levels of inter-attribute correlations. Such correlations enable the use of a process known as vertical fragmentation in order to decompose the data into vertical subsets of smaller dimensionality. An information-theoretic criterion of mutual information is used in the vertical decomposition process. This allows the use of an anonymization process, which is based on combining results from multiple independent fragments. We present a general approach, which can be applied to the k-anonymity, $$\ell $$ -diversity, and t-closeness models. In the presence of inter-attribute correlations, such an approach continues to be much more robust in higher dimensionality, without losing accuracy. We present experimental results illustrating the effectiveness of the approach. This approach is resilient enough to prevent identity, attribute, and membership disclosure attack. [ABSTRACT FROM AUTHOR]

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Many practical problems occur when we wish to manipulate the data in a way that requires information not included explicitly in this data, and where we have to deal with functions of such a nature. In a networked environment, the data may reside in components on a number of geographically distributed sites. These databases cannot be moved to other network sites due to security, size, and privacy consideration. In this paper, we present two self-decomposing algorithms for constructing a function from given discrete data, and finding the extrema of any function whose arguments are stored across a number of distributed databases. [ABSTRACT FROM AUTHOR]

Considering the existing massive volumes of data processed nowadays and the distributed nature of many organizations, there is no doubt how vital the need is for distributed database systems. In such systems, the response time to a transaction or a query is highly affected by the distribution design of the database system, particularly its methods for fragmentation, replication, and allocation data. According to the relevant literature, from the two approaches to fragmentation, namely horizontal and vertical fragmentation, the latter requires the use of heuristic methods due to it being NP-Hard. Currently, there are a number of different methods of providing vertical fragmentation, which normally introduce a relatively high computational complexity or do not yield optimal results, particularly for large-scale problems. In this paper, because of their distributed and scalable nature, we apply swarm intelligence algorithms to present an algorithm for finding a solution to vertical fragmentation problem, which is optimal in most cases. In our proposed algorithm, the relations are tried to be fragmented in such a way so as not only to make transaction processing at each site as much localized as possible, but also to reduce the costs of operations. Moreover, we report on the experimental results of comparing our algorithm with several other similar algorithms to show that ours outperforms the other algorithms and is able to generate a better solution in terms of the optimality of results and computational complexity. [ABSTRACT FROM AUTHOR]

The design of distributed databases involves making decisions on the fragmentation and placement of data and programs across the sites of a computer network. The first phase of the distribution design in a top-down approach is the fragmentation phase, which clusters in fragments the information accessed simultaneously by applications. Most distribution design algorithms propose a horizontal or vertical class fragmentation. However, the user has no assistance in the choice between these techniques. In this work we present a detailed methodology for the design of distributed object databases that includes: (i) an analysis phase, to indicate the most adequate fragmentation technique to be applied in each class of the database schema; (ii) a horizontal class fragmentation algorithm, and (iii) a vertical class fragmentation algorithm. Basically, the analysis phase is responsible for driving the choice between the horizontal and the vertical partitioning techniques, or even the combination of both, in order to assist distribution designers in the fragmentation phase of object databases. Experiments using our methodology have resulted in fragmentation schemas offering a high degree of parallelism together with an important reduction of irrelevant data. [ABSTRACT FROM AUTHOR]

Fragmentation of base relations in distributed database management systems increases the level of concurrency and therefore system throughput for query processing. Algorithms for horizontal and vertical fragmentation of relations in relational, object-oriented and deductive databases exist; however, hybrid fragmentation techniques based on variable bindings appearing in user queries and query-access-rule dependency are lacking for deductive database systems. In this paper, we propose a hybrid fragmentation approach for distributed deductive database systems. Our approach first considers the horizontal partition of base relations according to the bindings imposed on user queries, and then generates vertical fragments of the horizontally partitioned relations and clusters rules using affinity of attributes and access frequency of queries and rules. The proposed fragmentation technique facilitates the design of distributed deductive database systems. [ABSTRACT FROM AUTHOR]

In query-intensive database application areas, like decision support and data mining, systems that use vertical fragmentation have a significant performance advantage. In order to support relational or object oriented applications on top of such a fragmented data model, a flexible yet powerful intermediate language is needed. This problem has been successfully tackled in Monet, a modern extensible database kernel developed by our group. We focus on the design choices made in the Monet interpreter language (MIL), its algebraic query language, and outline how its concept of tactical optimization enhances and simplifies the optimization of complex queries. Finally, we summarize the experience gained in Monet by creating a highly efficient implementation of MIL. [ABSTRACT FROM AUTHOR]

Processing costs in distributed environments is most often dominated by the network communications required for interprocess communication. It is well known from distributed relational database design research that careful placement of data “near” the users or processors where it is used is mandatory or system performance will suffer greatly. Data placement in relational database systems is comparatively simple because the data is flat, structured, and passive. Objects are characterized by an inheritance hierarchy (other hierarchies could also be considered including, class composition and execution), unstructured (possibly dynamic data), and contain a behavioral component that defines how the “data” is accessed by encapsulating it within the object per se. Algorithms currently exist for fragmenting relations, but the fragmentation and allocation of objects is still a relatively untouched field of study. Similar to relations, objects can be fragmented both horizontally and vertically. Vertical fragmentation must minimize application execution time by splitting a class so that all class attributes and methods frequently accessed together are grouped together into a single fragment. This paper adopts a classification of classes into four main models, and contributes by proposing algorithms for vertically fragmenting the four realizable class models consisting of simple or complex attributes combined with simple or complex methods. Vertical fragmentation entails splitting classes into a set of “smaller” equivalent classes (actually fragments of the class' extent) that can later be placed precisely where they are used. Our approach consists of grouping into a fragment, all attributes and methods of the class frequently accessed together by applications running on either this class, its subclasses, its containing classes or its complex method classes. [ABSTRACT FROM AUTHOR]

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented. [ABSTRACT FROM AUTHOR]

In the literature of distributed database system (DDBS), several methods sought to meet the satisfactory reduction on transmission cost (TC) and were seen substantially effective. Data Fragmentation, site clustering, and data distribution have been considered the major leading TC-mitigating influencers. Sites clustering, on one hand, aims at grouping sites appropriately according to certain similarity metrics. On the other hand, data distribution seeks to allocate the fragmented data into clusters/sites properly. The combination of these methods, however, has been shown fruitful concerning TC reduction along with network overheads. In this work, hence, a heuristic clustering-based approach for vertical fragmentation and data allocation is meticulously designed. The focus is directed on proposing an influential solution for improving relational DDBS throughputs across an aggregated similarity-based fragmentation procedure, an effective site clustering and a greedy algorithm-driven data allocation model. Moreover, the data replication is also considered so TC is further minimized. Through the delineated-below evaluation, the findings of experimental implementation have been observed to be promising., (© 2020 The Author(s).)

Data fragmentation and allocation has for long proven to be an efficient technique for improving the performance of distributed database systems' (DDBSs). A crucial feature of any successful DDBS design revolves around placing an intrinsic emphasis on minimizing transmission costs (TC). This work; therefore, focuses on improving distribution performance based on transmission cost minimization. To do so, data fragmentation and allocation techniques are utilized in this work along with investigating several data replication scenarios. Moreover, site clustering is leveraged with the aim of producing a minimum possible number of highly balanced clusters. By doing so, TC is proved to be immensely reduced, as depicted in performance evaluation. DDBS performance is measured using TC objective function. An inclusive evaluation has been made in a simulated environment, and the compared results have demonstrated the superiority and efficacy of the proposed approach on reducing TC. [ABSTRACT FROM AUTHOR]

Many scholars have attempted to use an encryption method to resolve the problem of data leakage in data outsourcing storage. However, encryption methods reduce data availability and are inefficient. Vertical fragmentation perfectly solves this problem. It was first used to improve the access performance of the relational database, and nowadays some researchers employ it for privacy protection. However, there are some problems that remain to be solved with the vertical fragmentation method for privacy protection in the relational database. First, current vertical fragmentation methods for privacy protection require the user to manually define privacy constraints, which is difficult to achieve in practice. Second, there are many vertical fragmentation solutions that can meet privacy constraints; however, there are currently no quantitative evaluation criteria evaluating how effectively solutions can protect privacy more effectively. In this article, we introduce the concept of information entropy to quantify privacy in vertical fragmentation, so we can automatically discover privacy constraints. Based on this, we propose a privacy protection model with a minimum entropy fragmentation algorithm to achieve minimal privacy disclosure of vertical fragmentation. Experimental results show that our method is suitable for privacy protection with a lower overhead. [ABSTRACT FROM AUTHOR]