Vertical Partitioning Algorithm Research Articles

Vertical Fragmentation Impacts in the Performance and Management Database Health Information System (Comparative Study of Fragmentation Vertically Algorithm)

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.

Query Optimization on Distributed Database Dengue Fever by Minimizing Attribute Involvement

Query optimization is an important task in a client/server environment of a distributed database, whereas a health epidemiologist data distribution based on DBD data on Geographic Information Systems (GIS). A proper method for a particular query process function is needed to generate query optimization on a distributed database. The query process requires important attention especially in distributed databases because the result of a cost-based query process is accessed by involving a number of attributes and visited sites. A query operation typically will search for data from various attributes in a scattered database table, although the processes do not require all table attributes. Query optimization requires minimum query operating costs (communication costs and access fees). The query cost can be optimized by separating attributes that are not required by the query. This can reduce the amount of communication and access time. The attributes should not be divided indiscriminately to obtain the best result of the query process and a vertical fragmentation method can be used to perform such attribute separation. In this research, attributes separation using vertical fragmentation method for a database health table is studied by comparing Bond Energy Algorithm (BEA) and Graphic Based Vertical Partitioning (GBVP) algorithm. The initial result of vertical fragmentation in both algorithms is the determination of types of attributes separated from a number of specific query process. The result of the separation of attributes from each algorithm is compared and evaluated using Partitioned Evaluator (PE) in order to achieve the access cost of several attributes. The results show that GBVP algorithm is more optimal for use in vertical table fragmentation process applied as query operation on distributed DBD database in a health field. The GBVP algorithm has less computational complexity, results a higher partition evaluator value and has lower query execution time than BEA.

Active rule base development for dynamic vertical partitioning of multimedia databases

Currently, vertical partitioning has been used in multimedia databases in order to take advantage of its potential benefits in query optimization. Nevertheless, most vertical partitioning algorithms are static; this means that they optimize a vertical partitioning scheme (VPS) according to a workload, but if this workload suffers changes, the VPS may be degraded, which would result in long query response time. This paper presents a set of active rules to perform dynamic vertical partitioning in multimedia databases. First of all, these rules collect all the information that a vertical partitioning algorithm needs as input. Then, they evaluate this information in order to know if the database has experienced enough changes to trigger a performance evaluator. In this case, if the performance of the database falls below a threshold previously calculated by the rules, the vertical partitioning algorithm is triggered, which gets a new VPS. Finally, the rules materialize the new VPS. Our active rule base is implemented in the system DYMOND, which is an active rule-based system for dynamic vertical partitioning of multimedia databases. DYMOND's architecture and workflow are presented in this paper. Moreover, a case study is used to clarify and evaluate the functionality of our active rule base. Additionally, authors of this paper performed a qualitative evaluation with the aim of comparing and evaluating DYMOND's functionality. The results showed that DYMOND improved query performance in multimedia databases.

A Vertical Partitioning Algorithm for Distributed Object Oriented Databases

Oriented Databases (OODB) is becoming popular day by day and being used in a large number of application domains. In order to support homogeneous distributed OODBs a clear understanding of partitioning of class and how to do it by using different partitioning algorithms is needed. In this paper an algorithm for vertical fragmentation in a model consisting of class and comprising of complex attributes and complex methods is presented. The approach for fragmentation is top-down and entity of fragmentation is class. The algorithm presented here is an enhancement to the previous work of vertical partitioning algorithms in OODB management systems. The algorithm takes input as the class to be partitioned into fragments or groups, generates Method Usage Matrix as its first step from the methods and queries provided. It then generates Method Affinity matrix which is constructed using above matrix and based on method affinity values of two methods. Two new factors are introduced Method Linking Factor and Group Linking Factor which provides more control on deciding groups and increasing the flexibility of the algorithm.

Vertically Partitioning of Database for Secured Data Release

There is a huge database which contains some private and public information. When we are mining the useful information from the enterprise data, there can be issues regarding disclosure of the private data. Many times, the sensitive data can be directly or indirectly derived from the answered queries, to overcome these issues we extend the differential privacy model. In this privacy model, source database table is divided into some parties which hold different attributes for the same set of individuals. We have addressed the problem of private data exposure, which can be prevented by forming vertically partitioned databases. This partitioning is by an exponential mechanism algorithm which guarantees that the other party can’t derive extra information from the answered query. The proposed algorithm also provides the security for the data which is release from the scatter pattern. To improve query response time of the system some schemes are used like Vertical Partitioning Scheme (VPS), Statistics Collector, and Partitioning Generator. General Terms Vertical Partitioning Algorithm; Differential Privacy.

A comparison of knives for bread slicing

Vertical partitioning is a crucial step in physical database design in row-oriented databases. A number of vertical partitioning algorithms have been proposed over the last three decades for a variety of niche scenarios. In principle, the underlying problem remains the same: decompose a table into one or more vertical partitions. However, it is not clear how good different vertical partitioning algorithms are in comparison to each other. In fact, it is not even clear how to experimentally compare different vertical partitioning algorithms. In this paper, we present an exhaustive experimental study of several vertical partitioning algorithms. We categorize vertical partitioning algorithms along three dimensions. We survey six vertical partitioning algorithms and discuss their pros and cons. We identify the major differences in the use-case settings for different algorithms and describe how to make an apples-to-apples comparison of different vertical partitioning algorithms under the same setting. We propose four metrics to compare vertical partitioning algorithms. We show experimental results from the TPC-H and SSB benchmark and present four key lessons learned: (1) we can do four orders of magnitude less computation and still find the optimal layouts, (2) the benefits of vertical partitioning depend strongly on the database buffer size, (3) HillClimb is the best vertical partitioning algorithm, and (4) vertical partitioning for TPC-H-like benchmarks can improve over column layout by only up to 5%.

PcAVP - Adaptable Vertical Partitioning Algorithm Based on Privacy Constraint for Distributed Database System

Vertical partitioning is a process of generating fragments, each of which is composed of attributes with high affinity. It is widely used in the distributed database to improve the efficiency of system by reducing the connection between the table access operations. The current research on vertical partitioning is mainly focused on how to measure the "affinity" to get the best-fit vertical partitioning and the n-way vertical partitioning which support generating the specific number of fragments required by the user. In this paper, we propose a vertical partitioning algorithm based on privacy constraint. It supports both the best-fit vertical partitioning and the n-way vertical partitioning. It also provides the data privacy protection by privacy constraint checking. We conduct several experimental results to show that our algorithm not only keeps higher efficiency, but also provides data privacy protection.

Vertical Partitioning Method for Secret Sharing Distributed Database System

Secret sharing is a method for distributing a secret among a party of participants. Each of them is allocated a share of the secret, and the secret can only be reconstructed when the shares are combined together. We have been proposing a secret sharing distributed database system (SSDDB) that uses a secret sharing scheme to improve confidentiality and robustness of distributed database systems. This paper proposes a vertical partitioning algorithm for the SSDDB, and evaluates the algorithm by computational experiments.

Partitioning strategies for distributed association rule mining

In this paper a number of alternative strategies for distributed/parallel association rule mining are investigated. The methods examined make use of a data structure, the T-tree, introduced previously by the authors as a structure for organizing sets of attributes for which support is being counted. We consider six different approaches, representing different ways of parallelizing the basic Apriori-T algorithm that we use. The methods focus on different mechanisms for partitioning the data between processes, and for reducing the message-passing overhead. Both ‘horizontal’ (data distribution) and ‘vertical’ (candidate distribution) partitioning strategies are considered, including a vertical partitioning algorithm (DATA-VP) which we have developed to exploit the structure of the T-tree. We present experimental results examining the performance of the methods in implementations using JavaSpaces. We conclude that in a JavaSpaces environment, candidate distribution strategies offer better performance than those that distribute the original dataset, because of the lower messaging overhead, and the DATA-VP algorithm produced results that are especially encouraging.

On fragmentation approaches for distributed database design

In this paper, two-phase horizontal partitioning of distributed databases is addressed. First, primary horizontal fragmentation is carried out on each relation based on the predicate affinity matrix and the bond energy algorithm. This is an application of a vertical partitioning algorithm to the horizontal fragmentation problem. Second, the derived horizontal fragmentation is further performed by considering information related to the global relational database schema and its transactions. A necessary and sufficient condition for the correctness of derived fragmentations is also proved.

Vertical partitioning for database design: a graphical algorithm

Vertical partitioning is the process of subdividing the attributes of a relation or a record type, creating fragments. Previous approaches have used an iterative binary partitioning method which is based on clustering algorithms and mathematical cost functions. In this paper, however, we propose a new vertical partitioning algorithm using a graphical technique. This algorithm starts from the attribute affinity matrix by considering it as a complete graph. Then, forming a linearly connected spanning tree, it generates all meaningful fragments simultaneously by considering a cycle as a fragment. We show its computational superiority. It provides a cleaner alternative without arbitrary objective functions and provides an improvement over our previous work on vertical partitioning.