Abstract: For a spatiotemporal database management system,
I/O cost of queries and other operations is an important performance
criterion. In order to optimize this cost, an intense research on
designing robust index structures has been done in the past decade.
With these major considerations, there are still other design issues
that deserve addressing due to their direct impact on the I/O cost.
Having said this, an efficient buffer management strategy plays a key
role on reducing redundant disk access. In this paper, we proposed an
efficient buffer strategy for a spatiotemporal database index
structure, specifically indexing objects moving over a network of
roads. The proposed strategy, namely MONPAR, is based on the data
type (i.e. spatiotemporal data) and the structure of the index
structure. For the purpose of an experimental evaluation, we set up a
simulation environment that counts the number of disk accesses
while executing a number of spatiotemporal range-queries over the
index. We reiterated simulations with query sets with different
distributions, such as uniform query distribution and skewed query
distribution. Based on the comparison of our strategy with wellknown
page-replacement techniques, like LRU-based and Prioritybased
buffers, we conclude that MONPAR behaves better than its
competitors for small and medium size buffers under all used query-distributions.
Abstract: With the rapid development in the field of life
sciences and the flooding of genomic information, the need for faster
and scalable searching methods has become urgent. One of the
approaches that were investigated is indexing. The indexing methods
have been categorized into three categories which are the lengthbased
index algorithms, transformation-based algorithms and mixed
techniques-based algorithms. In this research, we focused on the
transformation based methods. We embedded the N-gram method
into the transformation-based method to build an inverted index
table. We then applied the parallel methods to speed up the index
building time and to reduce the overall retrieval time when querying
the genomic database. Our experiments show that the use of N-Gram
transformation algorithm is an economical solution; it saves time and
space too. The result shows that the size of the index is smaller than
the size of the dataset when the size of N-Gram is 5 and 6. The
parallel N-Gram transformation algorithm-s results indicate that the
uses of parallel programming with large dataset are promising which
can be improved further.