FAST NEAREST NEIGHBOUR SEARCH WITHIN ARBITRARY SUBSPACES

International Journal of Future Computer and Communication

2018

Spatial information processing has been a centre of attention of research in the previous decade. In spatial databases, data related with spatial coordinates and extents are retrieved based on spatial proximity. A large number of spatial indexes have been proposed to make ease of efficient indexing of spatial objects in large databases and spatial data retrieval. The goal of this paper is to review the advance techniques of the access methods. This paper tries to classify the existing multidimensional access methods, according to the types of indexing, and their performance over spatial queries. K-d trees out performs quad tress without requiring additional memory usage.

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2015

The ubiquity of location-aware devices and smartphones has unleashed an unprecedented proliferation of location-based services that require processing queries with both spatial and relational predicates. Many algorithms and index structures already exist for processing k-Nearest-Neighbor (kNN, for short) predicates either solely or when combined with textual keyword search. Unfortunately, there has not been enough study on how to efficiently process queries where kNN predicates are combined with general relational predicates, i.e., ones that have selects, joins and group-by's. One major challenge is that because the kNN is a ranking operation, applying a relational predicate before or after a kNN predicate in a query evaluation pipeline (QEP, for short) can result in different outputs, and hence leads to different query semantics. In particular, this renders classical relational query optimization heuristics, e.g., pushing selects below joins, inapplicable. This paper presents various query optimization heuristics for queries that involve combinations of kNN select/join predicates and relational predicates. The proposed optimizations can significantly enhance the performance of these queries while preserving their semantics. Experimental results that are based on queries from the TPC-H benchmark and real spatial data from OpenStreetMap demonstrate that the proposed optimizations can achieve orders of magnitude enhancement in query performance.

Spatial query which focus only on the geometrics properties of an object like points, rectangle etc. Now a day’s many new applications which involve the queries that completely aim to return an object which satisfy equally on spatial predicate and their associated text. Spatial query takes the given location and a keyword as the input and finds the object that matches the both spatial predicate and the text related to the given query. Some of the spatial queries are range search and nearest neighbor retrieval which includes only geometric properties of an object. For example, In case of considering all the hotels, a nearest neighbor query would find for the hotel which is near, along with menu that user required to have in hotel among all the hotels in particular location simultaneously. At present the better solution is based on IR2-Tree which as few drawbacks that affect the efficiency in query retrieval. So we develop a new method Spatial inverted index that cope with 3D data to answer the nearest neighbor query using keyword along with key values in real time. Searching nearest neighbor query using key values will result in quick response of query when compared to keyword in real time.

1998

Structural queries constitute a special form of content-based retrieval where the user specifies a set of spatial constraints among query variables and asks for all configurations of actual objects that (totally or partially) match these constraints. Processing such queries can be thought of as a general form of spatial joins, i.e., instead of pairs, the result consists of n-tuples of objects, where n is the number of query variables. In this paper we describe a flexible framework which permits the representation of configurations in different resolution levels and supports the automatic derivation of similarity measures. We subsequently propose three algorithms for structural query processing which integrate constraint satisfaction with spatial indexing (R-trees). For each algorithm we apply several optimization techniques and experimentally evaluate performance using real data.

arXiv (Cornell University), 2015

Emerging location-based systems and data analysis frameworks requires efficient management of spatial data for approximate and exact search. Exact similarity search can be done using space partitioning data structures, such as KD-tree, R*-tree, and ball-tree. In this paper, we focus on ball-tree, an efficient search tree that is specific for spatial queries which use euclidean distance. Each node of a ball-tree defines a ball, i.e. a hypersphere that contains a subset of the points to be searched. In this paper, we propose ball*-tree, an improved ball-tree that is more efficient for spatial queries. Ball*-tree enjoys a modified space partitioning algorithm that considers the distribution of the data points in order to find an efficient splitting hyperplane. Also, we propose a new algorithm for KNN queries with restricted range using ball*-tree, which performs better than both KNN and range search for such queries. Results show that ball*-tree performs 39%-57% faster than the original ball-tree algorithm.

Iberoamerican Congress on Pattern Recognition CIARP, 2009

Many pattern recognition tasks can be modeled as proximity searching. Here the common task is to quickly find all the elements close to a given query without sequentially scanning a very large database. A recent shift in the searching paradigm has been established by using permutations instead of distances to predict proximity. Every object in the database record how the set of reference objects (the permutants) is seen, i.e. only the relative positions are used. When a query arrives the relative displacements in the permutants between the query and a particular object is measured. This approach turned out to be the most efficient and scalable, at the expense of loosing recall in the answers. The permutation of every object is represented with κ short integers in practice, producing bulky indexes of 16 κn bits. In this paper we show how to represent the permutation as a binary vector, using just one bit for each permutant (instead of logκ in the plain representation). The Hamming distance in the binary signature is used then to predict proximity between objects in the database. We tested this approach with many real life metric databases obtaining faster queries with a recall close to the Spearman ρ using 16 times less space.

The recent development in the technology leads to the introduction of various mobile terminals and there is a demand that the client requires effective location based services. The valid regions expand and also query retrieval time increases which lead to poor performance of query processing. The spatial indexing techniques are one of the most effective optimization methods to improve the quality of services. In existing system NN queries and window queries are used. In that R-tree and grid indexing has been used for increasing the query efficiency. But the Grid-index technique support low memory and thus large databases cannot be handled effectively. In the proposed system we are using Ordered grid index and EVR-tree to minimize the query retrieval time and to decrease the depth of the search index. The Ordered grid index and EVR-tree to speed up the spatial query processing.

1998

Structural queries constitute a special form of content-based retrieval where the user specifies a set of spatial constraints among query variables and searches for all configurations of actual objects that (totally or partially) match these constraints. Processing of such queries can be thought of as a general form of spatial joins, i.e., instead of pairs, the result consists of n-tuples of objects, where n is the number of query variables. In this paper we propose a flexible framework which permits the representation of configurations in different resolution levels and supports the automatic derivation of similarity measures. We subsequently describe three algorithms for structural query processing which integrate constraint satisfaction with spatial indexing. For each algorithm we apply several optimization techniques and experimentally evaluate performance using real data.

In spatial databases search operations take an important role. These operations consist of the point query (find all objects that contain a given search point), the range query (find all objects that overlap a given search range) [GAE98] and the nearest neighbor query (find k objects (k >= 1) that are closest to a given object). They are very costly operations. Their performance is affected by both CPU-time and IO-cost. The long history of researches on spatial databases has resulted in many multidimensional access methods to efficiently support such operations. Each of them has strengths and deficiencies as well. This paper preliminary develops taxonomy for these multidimensional access methods and describes some prominent multidimensional access methods, which have been recently introduced as well as their comparative studies. Moreover, the important role of multidimensional access methods for supporting real-world applications in the next decade is also discussed. Keywords: multidimensional access method (MAM), similarity search, spatial database, multidimensional database, bounding sphere (BS), minimum bounding rectangle (MBR), feature vector, similarity indexing.

2018

Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects’ geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. Currently, the best solution to such queries is based on the IR2 -tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR2 -tree in query response time significantly, often by a factor of orders of magnitude.

Technologies, Techniques and Trends, 2005

In order to generate efficient execution plans for queries comprising spatial data types and predicates, the database system has to be equipped with appropriate index structures, query processing methods, and optimization rules. Although available extensible indexing frameworks provide a gateway for seamless integration of spatial access methods into the standard process of query optimization and execution, they do not facilitate the actual implementation of the spatial access method itself. An internal enhancement of the database kernel is usually not an option for database developers. The embedding of a custom block-oriented index structure into concurrency control, recovery services and buffer management would cause extensive implementation efforts and maintenance cost, at the risk of weakening the reliability of the entire system. The server stability can be preserved by delegating index operations to an external process, but this approach induces severe performance bottlenecks due to context switches and inter-process communication. Therefore, we present the paradigm of object-relational spatial access methods that perfectly fits to the common relational data model and is highly compatible with the extensible indexing frameworks of existing object-relational database systems allowing the user to define application-specific access methods.

Conventional spatial queries such as vary search and nearest neighbor retrieval, involve solely conditions on objects' geometric properties. Today, several trendy applications call for novel varieties of queries that aim to seek out objects satisfying both a spatial predicate, and a predicate on their associated texts. As an example, rather than considering all the restaurants, a nearest neighbor query would instead invite the restaurant that is the nearest among those whose menus contain ―steak, spaghetti, brandy‖ all at identical time. Presently the simplest solution to such queries relies on the IR2-tree, which, as shown in this paper, includes a few deficiencies that seriously impact its efficiency. Impelled by this, we have a tendency to develop a brand new access methodology called the spatial inverted index that extends the standard inverted index to address multidimensional information, and comes with algorithms which will answer nearest neighbour queries with keywords in real time. As verified by experiments, the projected techniques outperform the IR2-tree in query reaction time significantly, typically by an element of orders of magnitude. Spatial queries, such as range search and nearest neighbour retrieval, involve only conditions on objects geometric properties. A spatial database manages multidimensional objects (such as points, rectangles, etc.), and provides fast access to those objects based on different selection criteria. Now-a-days many applications call a new form of queries to find the objects that satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbour query would instead ask for the restaurant that is the closest among those whose menus contain the specified keywords all at the sametime.IR2-tree is used in the existing system for providing best solution for finding nearest neighbour. This method has few deficiencies. So we implement the new method called spatial inverted index to improve the space and query efficiency. And enhanced search is used to search the required objects based on the user priority level. Thus the proposed algorithm is scalable to find the required objects.

GeoInformatica, 2012

Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline