Query Optimization on Distributed Health Database DBD by Minimizing Attribute Involvement
Slamet Sudaryanto N, Sudaryanto, Maryani S
Journal of Digital Information Management, Vol. 16, No. 3, 2018.
DOI: https://doi.org/10.6025/jdim/2018/16/3/105-113
Link: http://dline.info/fpaper/jdim/v16i3/jdimv16i3_1.pdf
Abstract:
Query optimization is an important task in
the client / server environment of a distributed database,
where large data locations are widely distributed, such as
distribution of health epidemiology data based DBD on
geographic information systems (GIS). In order to generate
query optimization on a distributed database it is necessary
to have a proper method on a particular query process
function. The query process requires important attention
especially in distributed databases, because the
result of a cost-based query process (access fees and
communication costs) is affected by the involvement of
the number of attributes and sites visited. If a query can
be decomposed into subqueries that require operations
on a separate database (geographically) and can determine
the exact site access sequence of a query process
query circuit then the operating costs for the query process
will be minimal. When a query process in a distributed
database occurs, queries operations will search for
data from various attributes in a scattered database table,
whereas query processes often do not require all the attributes
of the table. Therefore, in order to optimize the
query required minimum query operation cost (communication
cost and access cost). One way to minimize the
cost of queries is to separate attributes that are not required
by a query, thereby reducing the amount of time
communication and access. Can not make mistakes in
the separation of attributes, attributes should not be split
indiscriminately, because if not appropriate it will result in
the amount of access costs are getting larger and ultimately
reduce the performance of the query process itself.
To perform such attribute separation can be done
by Vertical Fragmentation method. In this experiment will
be conducted by comparing the results of separation attributes.
Separation of attributes will be done by using
Vertical Fragmentation method to source health database
tables (database testing), while the algorithm used for
attribute separation is Bond Energy Algorithm (BEA) and
Graphic Based Vertical Partitioning (GBVP). The initial
result of vertical fragmentation in both algorithms is the
determination of what attributes will be separated from a
number of specific query processes. The result of separation
of attributes from each algorithm will be compared
and evaluated using Partitioned Evaluator (PE). The purpose
of evaluation of the result of separation of attribute
is to know the amount of access cost of some attributes
from each algorithm. Thus it will be known the most optimal
algorithm in the operation of the query process. Algorithms
that have better performance are algorithms that
have the lowest execution time. The purpose of this research
is to perform query optimization by applying the
correct algorithm, by way of framing the method of fragmentation
between BEA algorithm with GBVT to then
concluded algorithm which has query optimization performance
from the most optimal query process so it is suitable
to be applied as query operation on distributed database
in field health.