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Ev ve Ofis taşıma sektöründe lider olmak.Teknolojiyi takip ederek bunu müşteri menuniyeti amacı için kullanmak.Sektörde marka olmak.
İstanbul evden eve nakliyat
Misyonumuz sayesinde edindiğimiz müşteri memnuniyeti ve güven ile müşterilerimizin bizi tavsiye etmelerini sağlamak.
Uncertain Spatial Data Handling: Modeling, Indexing and Query
Managing and manipulating uncertainty in spatial databases are important problems for various
practical applications of geographic information systems. We present a probability-based method to
model and index uncertain spatial
data. In this scheme, each object is represented by a probability density function (PDF) and a
general measure is proposed for measuring similarity between the objects. To index objects, an optimized
Gaussian mixture hierarchy (OGMH) is designed to support both certain/uncertain data and certain/uncertain
queries. As an example of uncertain query support OGMH is applied
to the Mojave Desert endangered species protection real dataset. It is found that OGMH provides more selective,
efficient and flexible search than the results provided by the existing trial and error approach for endangered
species habitat search.
Handling Uncertain Spatial Data: Comparisons Between Indexing Structures
Managing and manipulating uncertainty in spatial
databases are important problems for various practical
applications. Unlike the traditional fuzzy approaches in relational
databases, in this research we propose a probability-based method to
model and index uncertain spatial data where every object is
represented by a probability density function (PDF). To index
PDFs, we construct an optimized Gaussian mixture hierarchy
(OGMH) and two variants of uncertain R-tree. We provide a
comprehensive comparison among these three indices and plain
R-tree on TIGER/Line Southern California landmark point
dataset. We find that uncertain R-tree is the best for fixed query
and OGMH is suitable for both certain and uncertain queries.
Moreover, OGMH is suitable not only for spatial databases, but
also for multi-dimensional indexing applications like content
based image retrieval, where R-tree is inefficient in high
dimensions.
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