Abstract
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.
Highlights
An increase of a large and complex database can decrease the performance and cost overruns of data access information system
The results of the calculation of the cost of access to data by using Partition Evaluator (PE) show differences Partition Evaluator value of both Bond Energy Algorithm (BEA) and Graph-Based Vertical Partitioning (GBVP) algorithms which are shown in the table
The time required is: O(I ∗k ∗m∗n) where, I is the number of iterations required for convergence, as mentioned I is often small and can usually be safely bound as most changes typically occur in the first few iterations which can be seen in Fig. 5 split of GBVP algorithm
Summary
An increase of a large and complex database can decrease the performance and cost overruns of data access information system. A clustering method based on vertical fragmentation to increase the system performance has become trend in a distributed database study, especially in determining the cost of query access. The main part of making vertical fragmentation in a distributed database is to find groups which contain relevant attributes in a relation table based on the affinity matrix value. Affinity matrix contains a number of attributes with other attributes (the number of simultaneously accessing two attributes) The iteration in this algorithm is used and based on the grouping matrix n × n affinity matrix that will be used as the basic matrix in table fragmentation process that will be done (Rodríguez and Li, 2011). The important thing in creating a vertical fragmentation in a distributed database is finding attributes which have been clustered in a relational table based on the affinity value in matrix of an attribute.
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