dc.contributor.advisor |
Silva T |
|
dc.contributor.author |
Udayanthi HPI |
|
dc.date.accessioned |
2021 |
|
dc.date.available |
2021 |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
Udayanthi, H.P.I. (2021). A Big data analytic framework over federated data centers for intelligent travel recommenders [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21354 |
|
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/21354 |
|
dc.description.abstract |
Big Data is a series of enormous and complex data sets that are nearly impossible to store and
process using traditional data storing and processing methods. The emergence of
heterogeneous data in different domains causes significant challenges in data manipulation
and decision making. In recent years, the requirement for analysis of heterogeneous data on
distributed data storages has been increased and has gained a lot of researchers’ attention.
Distributed data storage systems and parallel data processing techniques are typically used for
data-intensive computing today. Due to the rapid growth of data, a single-cluster environment
is inadequate to process that much data. At the same time, there are heterogeneous data sources
on different platforms, which need to inter-connect to derive meaningful analysis. The
MapReduce software paradigm has surfaced to fill the gap, and it has been successfully
operating on systems. However, only single cluster environments are supported by the current
implementation of MapReduce and cannot be applied to federated heterogeneous data centers.
Hence, it does not have enough capabilities to process heterogeneous data sources. This
research presents a big data analytic framework that supports the integration of heterogeneous
data sources on distributed computing models across different data centers. The architecture
of this framework is based on a master/slave distributed computing model and Map - Reduce
- Merge - GlobalReduce is presented as the programming model. Besides, the performance of
the novel approach is measured under different cluster configurations, and experimental
evaluations had shown promising results for the proposed framework compared to a single
cluster environment. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
BIG DATA |
en_US |
dc.subject |
MULTI-CLUSTER |
en_US |
dc.subject |
HETEROGENEOUS DATA CENTERS |
en_US |
dc.subject |
HIERARCHICAL MAPREDUCE |
en_US |
dc.subject |
CLOUD COMPUTING |
en_US |
dc.subject |
COMPUTATIONAL MATHEMATICS -Dissertation |
en_US |
dc.subject |
INFORMATION TECHNOLOGY -Dissertation |
en_US |
dc.title |
A Big data analytic framework over federated data centers for intelligent travel recommenders |
en_US |
dc.type |
Thesis-Abstract |
en_US |
dc.identifier.faculty |
IT |
en_US |
dc.identifier.degree |
MSc in Computational Mathematics by research |
en_US |
dc.identifier.department |
Department of Computational Mathematics |
en_US |
dc.date.accept |
2021 |
|
dc.identifier.accno |
Th4848 |
en_US |