Proximity Queries Research Articles

Relational retrieval using a combination of path-constrained random walks

Scientific literature with rich metadata can be represented as a labeled directed graph. This graph representation enables a number of scientific tasks such as ad hoc retrieval or named entity recognition (NER) to be formulated as typed proximity queries in the graph. One popular proximity measure is called Random Walk with Restart (RWR), and much work has been done on the supervised learning of RWR measures by associating each edge label with a parameter. In this paper, we describe a novel learnable proximity measure which instead uses one weight per edge label sequence: proximity is defined by a weighted combination of simple path each corresponding to following a particular sequence of labeled edges. Experiments on eight tasks in two subdomains of biology show that the new learning method significantly outperforms the RWR model (both trained and untrained). We also extend the method to support two additional types of experts to model intrinsic properties of entities: query-independent experts, which generalize the PageRank measure, and popular entity experts which allow rankings to be adjusted for particular entities that are especially important.

ASSIGNMENT QUERY AND ITS IMPLEMENTATION IN MOVING OBJECT DATABASES

Moving object databases (MOD) are being used in a wide range of location-based services that are of growing interest in many application areas. In the literature, several query types such as nearest neighbor, reverse nearest neighbor, k-nearest neighbor, and proximity queries have been considered in MOD. In this paper, we propose a novel operator called the assignment operator as a query type for MOD. The assignment operator is an operator used in a query to solve the assignment problem (also known as the weighted bipartite graph-matching problem). Assignment operator finds a perfect match between two sets of objects in a manner that minimizes a total cost. For instance, a set of moving objects such as taxi cabs are assigned to a set of customers in a manner that minimizes the total cost of traveling for the taxis. A possible implementation of the assignment operator in MOD and its performance evaluation are given.

Proximity queries using separating bounding volumes

This paper presents a fast method for collision detection and proximity queries for 3D models using Separating Bounding Volumes (SBVs). In order to achieve real-time performance for complex, non-convex models, we propose an algorithm to speed-up the computation of SBVs by using the support vector machines and a robust scheme to conduct penetration search under the framework of SBVs. The SBVs-based proximity queries require no specific knowledge of the object's geometry or topology. The examples indicate that this method, the meaningful local penetration information can be computed for complex models within a few milliseconds.

A bidimensional data structure and spatial optimization for supermassive crowd simulation on GPU

Computing and presenting emergent crowd simulations in real time is a computationally intensive task. This intensity is mostly due to the complexity of the traversal algorithm needed for the interactions of all elements against each other on the basis of a proximity query. By using special data structures such as grids, and due to the parallel nature of graphics hardware, relevant previou work reduced this complexity considerably, making it possible to achieve interactive frame rates. However, existing proposals tend to be heavily bound by the maximum density of such grids, which is usually high, leading to arguably inefficient algorithms. In this article we propose the use of a fine- grained grid and accompanying data manipulation, to lead to scalable algorithmic complexity. We also implement a representative flocking boids case study, from which we ran benchmarks with more than one million simulated and rendered boids at nearly 30fps. We remark that related previous work achieved no more than 15,000 boids with interactive frame rates.

Finding and ranking compact connected trees for effective keyword proximity search in XML documents

In this paper, we study the problem of keyword proximity search in XML documents. We take the disjunctive semantics among the keywords into consideration and find top- k relevant compact connected trees (CCTrees) as the answers of keyword proximity queries. We first introduce the notions of compact lowest common ancestor (CLCA) and maximal CLCA (MCLCA), and then propose compact connected trees and maximal CCTrees (MCCTrees) to efficiently and effectively answer keyword proximity queries. We give the theoretical upper bounds of the numbers of CLCAs, MCLCAs, CCTrees and MCCTrees, respectively. We devise an efficient algorithm to generate all MCCTrees, and propose a ranking mechanism to rank MCCTrees. Our extensive experimental study shows that our method achieves both high efficiency and effectiveness, and outperforms existing state-of-the-art approaches significantly.

Optimisation-based proximity queries and penetration depth computation

The virtual reality (VR) was found to be a perfect technique that could be used as a training approach, since it shows many advantages despite its weakness. In the VR some major bottlenecks arises ...

Optimisation-based proximity queries and penetration depth computation

The virtual reality (VR) was found to be a perfect technique that could be used as a training approach, since it shows many advantages despite its weakness. In the VR some major bottlenecks arises namely the proximity queries (PQ) and penetration depth computation. This paper shows a novel algorithm used to solve those problems. Problems of PQ are ubiquitous within many tasks in computer graphics, virtual environments, robotics, manufacturing, and mechanical design. Interactions in any virtual scene usually involve contact or close proximity between its objects. Determining which pairs of objects are in contact or at close proximity is a complex task in most of the virtual environments. The PQ is the shortest vector over which one object needs to be translated in order to bring the pair in contact.

A Math Query Language with an Expanded Set of Wildcards

Math search is a new area of research with many enabling technologies but also many challenges. Some of the enabling technologies include XML, XPath, XQuery, and MathML. Some of the challenges involve enabling search systems to recognize mathematical symbols and structures. Several math search projects have made considerable progress in meeting those challenges. One of the remaining challenges is the creation and implementation of a math query language that enables the general users to express their information needs intuitively yet precisely. This paper will present such a language and detail its features. The new math query language offers an alternative way to describe mathematical expressions that is more consistent and less ambiguous than conventional mathematical notation. In addition, the language goes beyond the Boolean and proximity query syntax found in standard text search systems. It defines a powerful set of wildcards that are deemed important for math search. These wildcards provide for more precise structural search and multi-levels of abstractions. Three new sets of wildcards and their implementation details will also be discussed.

High Performance GPU‐based Proximity Queries using Distance Fields

Abstract Proximity queries such as closest point computation and collision detection have many applications in computer graphics, including computer animation, physics‐based modelling, augmented and virtual reality. We present efficient algorithms for proximity queries between a closed rigid object and an arbitrary, possibly deformable, polygonal mesh. Using graphics hardware to densely sample the distance field of the rigid object over the arbitrary mesh, we compute minimal proximity and collision response information on the graphics processing unit (GPU) using blending and depth buffering, as well as parallel reduction techniques, thus minimizing the readback bottleneck. Although limited to image‐space resolution, our algorithm provides high and steady performance when compared with other similar algorithms. Proximity queries between arbitrary meshes with hundreds of thousands of triangles and detailed distance fields of rigid objects are computed in a few milliseconds at high‐sampling resolution, even in situations with large overlap.

Proximity Queries Between Convex Objects: An Interior Point Approach for Implicit Surfaces

This paper presents a general method for exact distance computation between convex objects represented as intersections of implicit surfaces. Exact distance computation algorithms are particularly important for applications involving objects that make intermittent contact, such as in dynamic simulations and in haptic interactions. They can also be used in the narrow phase of hierarchical collision detection. In contrast to geometric approaches developed for polyhedral objects, we formulate the distance computation problem as a convex optimization problem. We use an interior point method to solve the optimization problem and demonstrate that, for general convex objects represented as implicit surfaces, interior point approaches are globally convergent, and fast in practice. Further, they provide polynomial-time guarantees for implicit surface objects when the implicit surfaces have self-concordant barrier functions. We use a primal-dual interior point algorithm that solves the Karush-Kuhn-Tucker (KKT) conditions obtained from the convex programming formulation. For the case of polyhedra and quadrics, we establish a theoretical time complexity of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.5</sup> ), where n is the number of constraints. We present implementation results for example implicit surface objects, including polyhedra, quadrics, and generalizations of quadrics such as superquadrics and hyperquadrics, as well as intersections of these surfaces. We demonstrate that in practice, the algorithm takes time linear in the number of constraints, and that distance computation rates of about 1 kHz can be achieved. We also extend the approach to proximity queries between deforming convex objects. Finally, we show that continuous collision detection for linearly translating objects can be performed by solving two related convex optimization problems. For polyhedra and quadrics, we establish that the computational complexity of this problem is also O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.5</sup> ).

Exact and efficient construction of Minkowski sums of convex polyhedra with applications

We present an exact implementation of an efficient algorithm that computes Minkowski sums of convex polyhedra in R 3 . Our implementation is complete in the sense that it does not assume general position. Namely, it can handle degenerate input, and it produces exact results. We also present applications of the Minkowski-sum computation to answer collision and proximity queries about the relative placement of two convex polyhedra in R 3 . The algorithms use a dual representation of convex polyhedra, and their implementation is mainly based on the Arrangement package of Cgal, the Computational Geometry Algorithm Library. We compare our Minkowski-sum construction with the only three other methods that produce exact results we are aware of. One is a simple approach that computes the convex hull of the pairwise sums of vertices of two convex polyhedra. The second is based on Nef polyhedra embedded on the sphere, and the third is an output-sensitive approach based on linear programming. Our method is significantly faster. The results of experimentation with a broad family of convex polyhedra are reported. The relevant programs, source code, data sets, and documentation are available at http://www.cs.tau.ac.il/~efif/CD and a short movie [Fogel E, Halperin D. Video: Exact Minkowski sums of convex polyhedra. In: Proceedings of 21st annual ACM symposium on computational geometry. 2005. p. 382–3] that describes some of the concepts portrayed in this paper can be downloaded from http://www.cs.tau.ac.il/~efif/CD/Mink3d.avi.

Distributed computation of the knn graph for large high-dimensional point sets

High-dimensional problems arising from robot motion planning, biology, data mining, and geographic information systems often require the computation of k nearest neighbor ( knn) graphs. The knn graph of a data set is obtained by connecting each point to its k closest points. As the research in the above-mentioned fields progressively addresses problems of unprecedented complexity, the demand for computing knn graphs based on arbitrary distance metrics and large high-dimensional data sets increases, exceeding resources available to a single machine. In this work we efficiently distribute the computation of knn graphs for clusters of processors with message passing. Extensions to our distributed framework include the computation of graphs based on other proximity queries, such as approximate knn or range queries. Our experiments show nearly linear speedup with over 100 processors and indicate that similar speedup can be obtained with several hundred processors.

Interactive collision detection in three-dimensional visualizations of simulated construction operations

This paper presents research that led to the design and implementation of fast and interactive collision detection methods that can be used to identify and report undesirable conflicts that occur among static (e.g., structure in-place, idle equipment), dynamic (e.g., active machines and workers), and abstract (e.g., hazard spaces) construction resources in 3D animations of construction operations modeled using discrete-event simulation. Computational efficiency and interactive speed in the designed interference detection methods were the primary challenges that the research addressed. In addition, the efficiency and speed were achieved with minimal trade-off against accuracy. In order to achieve this, the authors capitalized on advanced documented algorithms for proximity queries between arbitrarily moving 3D geometric objects to design mechanisms for interference detection, control, and response in construction process visualizations. The designed methods are implemented in a software tool called C-Collide that integrates as an add-on with the VITASCOPE visualization system.

Fast proximity computation among deformable models using discrete Voronoi diagrams

We present novel algorithms to perform collision and distance queries among multiple deformable models in dynamic environments. These include inter-object queries between different objects as well as intra-object queries. We describe a unified approach to compute these queries based on N-body distance computation and use properties of the 2 nd order discrete Voronoi diagram to perform N-body culling. Our algorithms involve no preprocessing and also work well on models with changing topologies. We can perform all proximity queries among complex deformable models consisting of thousands of triangles in a fraction of a second on a high-end PC. Moreover, our Voronoi-based culling algorithm can improve the performance of separation distance and penetration queries by an order of magnitude.

Keyword proximity search in XML trees

Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare's plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword, proximity queries to return the (possibly heterogeneous) set of minimum connecting trees (MCTs) of the matches to the individual keywords in the query. We consider efficiently executing keyword proximity queries on labeled trees (XML) in various settings: 1) when the XML database has been preprocessed and 2) when no indices are available on the XML database. We perform a detailed experimental evaluation to study the benefits of our approach and show that our algorithms considerably outperform prior algorithms and other applicable approaches.

Keyword proximity search in XML trees

Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare's plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword, proximity queries to return the (possibly heterogeneous) set of minimum connecting trees (MCTs) of the matches to the individual keywords in the query. We consider efficiently executing keyword proximity queries on labeled trees (XML) in various settings: 1) when the XML database has been preprocessed and 2) when no indices are available on the XML database. We perform a detailed experimental evaluation to study the benefits of our approach and show that our algorithms considerably outperform prior algorithms and other applicable approaches.

A metric index for approximate string matching

We present a radically new indexing approach for approximate string matching. The scheme uses the metric properties of the edit distance and can be applied to any other metric between strings. We build a metric space where the sites are the nodes of the suffix tree of the text, and the approximate query is seen as a proximity query on that metric space. This permits us finding the occ occurrences of a pattern of length m, permitting up to r differences, in a text of length n over an alphabet of size σ , in average time O ( m 1 + ε + occ ) for any ε > 0 , if r = o ( m / log σ m ) and m > ( ( 1 + ε ) / ε ) log σ n . The index works well up to r < ( 3 - 2 ) m / log σ m , where it achieves its maximum average search complexity O ( m 1 + 2 + ε + occ ) . The construction time of the index is O ( m 1 + 2 + ε n log n ) and its space is O ( m 1 + 2 + ε n ) . This is the first index achieving average search time polynomial in m and independent of n, for r = O ( m / log σ m ) . Previous methods achieve this complexity only for r = O ( m / log σ n ) . We also present a simpler scheme needing O ( n ) space.

Extending the SAND Spatial Database System for the Visualization of Three‐Dimensional Scientific Data

The three‐dimensional extension of the SAND (Spatial and Nonspatial Data) spatial database system is described as is its use for data found in scientific visualization applications. The focus is on surface data. Some of the principal operations supported by SAND involve locating spatial objects in the order of their distance from other spatial objects in an incremental manner so that the number of objects that are needed is not known a priori. These techniques are shown to be useful in enabling users to visualize the results of certain proximity queries without having to execute algorithms to completion as is the case when performing a nearest‐neighbor query where a Voronoi diagram (i.e., Thiessen polygon) would be computed as a preprocessing step before any attempt to respond to the query could be made. This is achieved by making use of operations such as the spatial join and the distance semijoin. Examples of the utility of such operations is demonstrated in the context of posing meteorological queries to a spatial database with a visualization component.

Robustness of k-gon Voronoi diagram construction

In this paper, we present a plane sweep algorithm for constructing the Voronoi diagram of a set of non-crossing line segments in 2D space using a distance metric induced by a regular k-gon and study the robustness of the algorithm. Following the algorithmic degree model [G. Liotta, F.P. Preparata, R. Tamassia, Robust proximity queries: an illustration of degree-driven algorithm design, SIAM J. Comput. 28 (3) (1998) 864–889], we show that the Voronoi diagram of a set of arbitrarily oriented segments can be constructed with degree 14 for certain k-gon metrics (e.g., k = 6 , 8 , 12 ). For rectilinear segments or segments with slope +1 or −1, the degree reduces to 2. The algorithm is easy to implement and finds applications in VLSI layout.

Spatial targeting using queries in a 3-D GIS environment with application to mineral exploration

A query framework for spatial targeting within a 3-D geographic information system (GIS) software environment is introduced. Input to a query consists of parameters relevant to the query type together with a set of Common Earth Modelling objects represented as point sets, polygonal lines, surfaces, and grids or a region set (subset) thereof. The result of a 3-D GIS query is a region within each of the input objects that consists of nodes or grid cells where the query criteria was satisfied. We provide example scenarios, drawn from mineral exploration, where 3-D queries are used to guide spatial targeting within a near-mine or regional map scale setting. Query types supported are: proximity query (to a “probe” object), property query (numeric attribute), shell query (containment within a closed surface), meta-data query, feature query (dome, depression, curvature), trend query (dip plane, vector) and intersection query (with a “probe” object). Queries can be specific for a given object type but in general transcend object types. Standard set theoretical operations for a query results in newly defined regions and are supported within the Gocad © development environment. This development focuses on queries relevant in the 3-D data integration and interpretation stages of mature geological model development as well as early analysis, typically undertaken before a fully partitioned and attributed 3-D topological model is available.