Arvand: A method to integrate ...

  تاریخ انتشار : 1396/1/21   نام نشریه : JOURNAL OF INFORMATION SCIENCE AND ENGINEERING   شمارهء صفخه در نشریه : 14
Arvand: A method to integrate multidimensional data sources into big data analytic structures

چکیده مقاله

OLAP (online analytic processing) systems provide valuable insights into organizations;
thus, it becomes necessary to integrate legacy OLAP systems into scalable and distributable
architectures. This project comprises two important tasks: the first is transferring
OLAP cubes to share nothing architectures. The second task is integrating OLAP
information with other OLAP systems over distributable and scalable architectures. The
main problem is to convert conceptual model OLAP data sources to shared nothing architectures.
An additional problem is query execution time on the shared nothing architectures
because by default, complete data locality is not considered in these environments.
In this paper, Arvand is proposed. This method can transfer multidimensional data
sources into shared nothing architectures. Data are captured from multidimensional data
sources and converted into a unified format. Through unification, multidimensional data
sources can be easily distributed over homogeneous and heterogeneous nodes because
the nodes will not need additional information from other nodes. As an added benefit,
MapReduce methods can be used properly and with maximum performance for query retrieval.
Arvand is implemented by adding some components to Hadoop. In this paper,
architectures with different heterogeneous and homogenous nodes are proposed and
evaluated using a TPC-DS benchmark.

نویسندگان : محمد حسین برخورداری، مهدی نیامنش