Journal of Kufa for Mathematics and Computer

For a given nonnegative integer number n, we can find a monotone function f depending on n,
defined on the interval I=[-1,1], and an absolute constant c>0, satisfying the following relationship:
( ́)

( )


( ) ( ́)

,
where

( ) is the degree of the best Lp monotone approximation of the function f by algebraic
polynomial of degree not exceeding n+1. ( ́)
is the degree of the best Lp approximation of the function ́
by algebraic polynomial of degree not exceeding n.

Distributed information retrieval (DIR) is a model enables a user to access many searchable databases
reside in different locations. DIR is more complex than the centralized information retrieval (IR). It requires
addressing two significant additional problems that are the resource selection and the results merging. Many
techniques for addressing the two problems have been published in the literature. However, they still have a
negative impact on retrieving quality and response time. This paper aims to improve the DIR efficiency through
using a meta-heuristic algorithm and improving the result quality through a query expansion. The algorithm has
been strengthened using the nearest neighbor graph in order to improve the search performance. The performance
in the proposed system outperforms the one in the traditional system in a rate from 6% to 9% while reduces the
latency in an approximate rate from 0.047 to 0.134 second for each query.

In this paper , we investigate solution for singular fuzzy fractional order control system ,
which in turn gives us two subsystem by drazin inverse . One of these subsystems contain
fuzzy fractional order control system used fuzzy laplace transform as well as lower function
and upper function in order to get the solution

The performance of different Data Mining Algorithms including Classification, Clustering, Association, Prediction and
others are highly related to the approaches used in Data Warehouse design and to the way the data is stored (lightly
summarized, highly summarized and detailed).Detailed data is important to get detailed reports but as the amount of
data is huge this represents a big challenge to the mining algorithms, on the other hand, the summarized data leads to
better algorithms performance but the lack of the required knowledge may affect the overall mining process.
Knowledge extraction and mining algorithms performance and complexities represent a big challenge in data analysis
field, hence the work in this paper represents a proposed approach to improve the algorithms performance throughout
well designed warehouse and data reduction technique.
The work in this paper presents a hybrid warehouse galaxy model that stores data in three different formats including
detailed, summarized and highly summarized data. The time and space complexity are the major criteria in the proposed approach. Real data was collected about schools, students and teachers from different AlNajaf AlAshraf cities, the data was preprocessed, reduced mainly through concept hierarchy and then converted into dimensions and fact tables
(Warehouse Galaxy Model) which in turn are converted into multidimensional cubes. Roll up and drill down queries
were highly used to get the required information. The resultant data cubes and in turn the corresponding warehouse model presented in this work showed a reasonable improvement in knowledge extraction algorithms for the data under discussion. The results of the queries showed better performance of different roll up and drill down queries compared to detailed data queries.