Proximity Search with a Triangulated Spatial Model

2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), 2017

This paper proposes a k-nearest neighbour search method inspired by grid space partitioning and the compact-trie structure. A compact trie structure, and a k-nearest neighbour search strategy are presented. Then, a k-nearest neighbour search performance comparison is carried out against two well-known methods, using one million two-dimensional spatial points and finding up to 1000 nearest neighbours. The results of the comparison shows that the proposed compact-trie method can perform up to 300 times better when k is small, and up to 25 times better for larger k values, suggesting that the proposed method is suitable for low dimensions and location-dependent spatial queries in applications such as mobile computing.

In this paper we present a method for abstracting an environ- ment represented using constrained Delaunay triangulations in a way that significantly reduces pathfinding search effort , as well as better representing the basic structure of the env i- ronment. The techniques shown here are ideal for objects of varying sizes and environments that are not axis-aligned or that contain many dead-ends, long corridors, or jagged wall s that complicate other search techniques. In fact, the abstr ac- tion simplifies pathfinding to deciding to which side of each obstacle to go. This technique is suited to real-time comput a- tion both because of its speed and because it lends itself to a n anytime algorithm, allowing it to work when varying amounts of resources are assigned to pathfinding. We test search algo - rithms running on both the base triangulation (Triangulati on A* - TA*) and our abstraction (Triangulation Reduction A* - TRA*) against A* and PRA* on grid-based maps from the commercia...

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016

Nearest Neighbour (NN) is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO) operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN) search. The test...

— Users may search for different type of things from anywhere. But Search results depend on the user entered query which has to satisfy their searched properties that is stored in the spatial database. Due to rapid growth of users it becomes essential to optimize search results based on nearest neighbour property in spatial databases. Conventional spatial queries, such as range search and nearest neighbour retrieval, involve only geometric properties of objects which satisfies condition on geometric objects. Nowadays many modern applications aim to find objects satisfying both a spatial condition and a condition on their associated texts which is known as Spatial keyword search. For example, instead of considering all the hotels, a nearest neighbor query would instead ask for the hotel that is closest to among those who provide services such as pool, internet at the same time. For this type of query a variant of inverted index is used that is effective for multidimensional points and comes with an R-tree which is built on every inverted list, and uses the algorithm of minimum bounding method that can answer the nearest neighbor queries with keywords in real time.

Lecture Notes in Computer Science, 2009

Many pattern recognition tasks can modeled as proximity searching. From nearest neighbor classification to multimedia databases the common task is to quickly find all the elements close to a given query. This task can be accomplished very easily by sequentially examining all the elements in the collection, but turns to be impractical in two situations: when the distance used to compare elements is expensive or when the numbers of elements is very large (in the order of billions of objects).

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016

Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour ...

2006

To quantify the impact of increased urbanization on the peri-urban hydro-systems, it is necessary to design adapted models. Currently, most of the hydrological models, based on classical raster approach, suffer from a lack of efficiency because they essentially do not take into account the urban artifacts and do not match complex geometries such as urban ones (heterogeneous objects, strong space's partition, roads, pits, buildings...). That is the reason why, we have decided to choose a Triangulated Irregular Network (TIN) model. This approach requires a Constrained Delaunay Triangulation (CDT) preprocessing phase. Also CDT algorithms are numerous, we must admit that there is a real lack of free, efficient and robust implementations adapted to our technological choices. Indeed, all the hydrological simulations we have developed, had been made in the context of the OrbisGIS platform (an open source GIS developed in Java in the French IRSTV Institute). The main objective, with OrbisGIS, is to remain coherent and

Proceedings 2006 Ieee International Conference on Robotics and Automation 2006 Icra 2006, 2006

This paper presents a comparative analysis relative to the experimental performances of an asymptotically fast and incremental algorithm, recently developed to compute collision translations for pairs of convex polyhedra. The algorithm may be worth considering because it solves a proximity problem which is less widely addressed than distance, as well as because of its peculiar computation strategy, well suited to work without initialization, but also endowed with an inherently embedded mechanism to exploit spatial coherence. Numerical data characterizing the behavior of the algorithm with respect to the complexity of the polyedra have already been discussed elsewhere, thus here the main focus is on contrasting its performances with those of two popular algorithms designed to compute distances between polyhedra. Although the considered "yardsticks" answer different proximity queries, and although one of the techniques is meant to deal with general polyhedra, the results presented in this paper should help to assess the efficacy and potential of the approach under analysis. All the three algorithms, indeed, share the same kind of application context; moreover, on the basis of the asymptotic bounds discussed in the literature, distances and collision translations require similar computational efforts. A thorough comparison of the reported query times and, more significantly, of the corresponding trends seems to show that the behavior of the novel algorithm is quite interesting, especially when used without initialization, what should encourage further work on its peculiar approach.

International Journal of Computer Applications, 2015

Today's applications requesting for finding spatial objects closest to a specified location or within some range which satisfy constraint of keywords. Initially, spatial queries finding nearest neighbor or range queries having conditions on only geometric properties of object points. For example, in the emergency of accident taking all hospital in consideration is not useful rather than finding hospital having facilities like ICU and emergency facilities at the same time. Currently, solution to these queries is based on the IR 2 -tree is not capable to provide real time effective answers. A new method named the SI-index Spatial Inverted) is developed extends the capabilities of conventional inverted index manages with multidimensional data, along with the solution of moving range queries answered by using SI-index results to algorithm which solves the problem in real time.

Lecture Notes in Computer Science, 2011

Nearest Neighbor Searching, i.e. determining from a set S of n sites in the plane the one that is closest to a given query point q, is a classical problem in computational geometry. Fast theoretical solutions are known, e.g. point location in the Voronoi Diagram of S, or specialized structures such as so-called Delaunay hierarchies. However, practitioners tend to deem these solutions as too complicated or computationally too costly to be actually useful. Recently in ALENEX 2010 Birn et al. proposed a simple and practical randomized solution. They reported encouraging experimental results and presented a partial performance analysis. They argued that in many cases their method achieves logarithmic expected query time but they also noted that in some cases linear expected query time is incurred. They raised the question whether some variant of their approach can achieve logarithmic expected query time in all cases. The approach of Birn et al. derives its simplicity mostly from the fact that it applies only one simple type of geometric predicate: which one of two sites in S is closer to the query point q. In this paper we show that any method for planar nearest neighbor searching that relies just on this one type of geometric predicate can be forced to make at least n − 1 such predicate evaluations during a worst case query.