Abstract: In recent years, real-time spatial applications, like
location-aware services and traffic monitoring, have become more
and more important. Such applications result dynamic environments
where data as well as queries are continuously moving. As a result,
there is a tremendous amount of real-time spatial data generated
every day. The growth of the data volume seems to outspeed the
advance of our computing infrastructure. For instance, in real-time
spatial Big Data, users expect to receive the results of each query
within a short time period without holding in account the load
of the system. But with a huge amount of real-time spatial data
generated, the system performance degrades rapidly especially in
overload situations. To solve this problem, we propose the use of
data partitioning as an optimization technique. Traditional horizontal
and vertical partitioning can increase the performance of the system
and simplify data management. But they remain insufficient for
real-time spatial Big data; they can’t deal with real-time and
stream queries efficiently. Thus, in this paper, we propose a novel
data partitioning approach for real-time spatial Big data named
VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial
Big data). This contribution is an implementation of the Matching
algorithm for traditional vertical partitioning. We find, firstly, the
optimal attribute sequence by the use of Matching algorithm. Then,
we propose a new cost model used for database partitioning, for
keeping the data amount of each partition more balanced limit and
for providing a parallel execution guarantees for the most frequent
queries. VPA-RTSBD aims to obtain a real-time partitioning scheme
and deals with stream data. It improves the performance of query
execution by maximizing the degree of parallel execution. This affects
QoS (Quality Of Service) improvement in real-time spatial Big Data
especially with a huge volume of stream data. The performance of
our contribution is evaluated via simulation experiments. The results
show that the proposed algorithm is both efficient and scalable, and
that it outperforms comparable algorithms.