In discuss and descried cast model in different


In this
study we present and analyze different Cost Model and query planning technique
in Database Management System specially for big data where we do need a very
fast and efficient query execution. Analyzing graphs is a very basic problem in
big data analytics, for which DBMS technology does not seem competitive. On the
other hand, SQL recursive queries are a fundamental mechanism to analyze graphs
in a DBMS, whose processing and optimization is significantly harder than
traditional SPJ queries. We try our best to cover all those this and give a
complete survey on query planning and cost modeling.  


and fast querying is a most important challenge in real word for modern
database systems.  Fast data is becoming crucial
and asset for researchers. In this study we investigate the following problem: how can we
generate a high-quality query plans which runs fast, in a result we can
minimizing the response time and query run more fast also we will discuss and descried
cast model in different dimensional data space.

There are
many query planning techniques are presented and also there are several
techniques for cost model are presented. We will discuss pattern matching over
compressed graph1, distributed SQL query execution on multiple engine2, compare
column, row and array data base management system to process queries, cost
model for neighbor search and real time processing techniques and cost model in
high dimensional data for query processing3. We will discuss different factor
on query planning and review the related cost models describe by the different
author form all over the word.


For data
processing the most popular platforms on the cloud are based on MapReduce
presented by Google. On top of the MapReduce, Google has also build a systems
FlumeJava, Tenzing and Sawzall. To write data pipelines FlumeJava is a library
is used MapReduce jobs are transformed into it. Over big datastes
Sawezall can be expressed that is a scripting language. To minimize the latency
Tenzing is a analytical query engine that is used to pre-allocate machine. Hodoop
is the main implementation source of MapReduce by Yahoo. For facebook Hive is a
wherehouse solution. The query language of Hive (HiveQL)  a subset optimization techniques and SQL are
limited to simple transformation rules. Our optimization goal is to maximize
the parallelism and minimize the No of MapReduce jobs and minimize the
execution time of the query. HadoopDB  is recent hybrid
system that combines MapReduce with databases. It uses multiple single node
databases and rely on Hadoop to schedule the jobs to each database. The
optimization goal is to create as much parallelism as possible by assigning
sub-queries to the singe node databases.

Condor/DAGMan/Stork is the state-of-the-art technology of High Performance
Computing. Nevertheless, Condor was designed for CPU to harvest cycles on idle
machines. However with DAGMan running data intensive workflow is very
inefficient. DAGManis used as middleware in many systems, like Pegasus and
GridDB . To deal with data intensive scientific workflows proposal for
extensions of condor do exist , but they have not been materialized yet to the
best of our knowledge. It is presented a case study of executing the Montage
dataflow on the cloud examining the trade-offs of different dataflow execution
modes and provisioning plans for cloud resources. By Microsoft Dryad is a
commercial middleware that has a more general architecture than MapReduce. it
can parallelize any dataflow. Its schedule optimization, however, relies
heavily on hints requiring knowledge of node proximity, which are generally not
available in a cloud environment. It deals with job migration by instantiating
another copy of a job and not by moving the job to another machine. When
optimizing solely time but not allocating additional containers matter when
financial cost it might be acceptable. On top of Dryad dryadLinQ it is built
and use LINQ , a set of .NET constructs for manipulating data. LINQ queries are
transformed into Dryad graphs and executed in a distributed fashion. One of the
first distributed database systems that takes into consideration  the monetary cost of answering the queries was
Mariposa. The user provides a budget function and the system optimizes the cost
of accessing the individual databases using auctioning.

3-Query planning and Cost modling Techniques:

In this
study we reviewed ten paper and study different technique for Query Planning
and Cost Modeling technique. To improving the performance of particular types
of graph operations one solution, is to reduce the size of the original graph G
6-7 by turning it into a smaller graph G as proposed by both the data mining
and theoretical computer science communities.

Maccioni proposed a solution to improves the performance of various graph
operations, such as the bounded approximation of the laplacian matrix or the bounded
approximation of graph isomorphisms1. They introduce the concept of
dedensification and parameterized the algorithem via threshold. he is more focused
on query execution and he experiment his work with indexing and non indexing
records. His experiments show that dedensification improves performance for
queries involving high-degree nodes, sometimes by an order of magnitude.

Giannakouris, Nikolaos Papailiou Worked on distributed query execution over
multiple engine environment2. In there Proposed solution MuSQLE can efficiently
utilize external SQL engines allowing for both intra and inter engine
optimizations. There system adopt a novel API based strategy. MuSQLE
specifies a generic API, used for the cost estimation and query execution, Instead
of manual integration, that needs to be implemented for each SQL engine
endpoint.It individually perform sub query optimization. As a Result MuSQLE can
provide speedups of up to 1 order of magnitude for TPCH queries, leveraging
different engines for the execution of individual query parts.

Berchtold, Christian Böhm and Daniel A. Keim Present there work about cost
modeling For Nearest Neighbor Search in High-Dimensional Data Space4 They first analyze different nearest neighbor algorithms and
present a new cost model for nearest neighbor search in high-dimensional data
space. The results that they get after 
applying there model to Hilbert and X-tree indices show that it provides
a good estimation of the query performance, which is considerably better than
the estimates by previous models especially for high dimensional data.

In an
another study Carlos Ordonez, Wellington Cabrera, Achyuth Gurram present there
solution about recursive query Graph3. In this paper, they present a new cost
model for nearest neighbor search in high-dimensional data space by analyzing different
nearest neighbor algorithms. Their works for an arbitrary number of data points
and for data sets with an arbitrary number of dimensions , is applicable to
different index structures and data distributions , and provides accurate
estimates of the expected query execution time.

A Cost
model for index structures for point databases such as the R *-tree and the
X-tree is presented by Christian Bohm10. he BBKK
model introduced two techniques, Minkowski sum and data space clipping, to
estimate the number of page accesses when performing range queries and
nearest-neighbor queries in a high dimensional Euclidean space