Fast Nearest Neighbor Search with Keywords

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. For example, instead of considering all the restaurants, a nearest neighbor query would instead ask for the restaurant that is the closest among those whose menus contain “steak, spaghetti, brandy” all at the same time. 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

Fast Nearest Neighbor Search with Keywords in Spatial Databases

In these days, many modern purposes name for novel varieties of queries that purpose to find objects pleasing both a spatial predicate, and a predicate on their related texts. Present answer for such queries has a couple of deficiencies that critically influence its effectivity. Prompted by way of this, in this venture, development of a new entry process called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and is derived with algorithms that may reply nearest neighbor queries with key words in actual time. As tested via experiments, the proposed approaches outperform the IR2-tree in question response time tremendously, more commonly through a factor of orders of magnitude. DOI: 10.17762/ijritcc2321-8169.15080

Fast Nearest Neighbor Search with Keywords Using IR2-Tree

Conventional abstraction queries, like vary search and nearest neighbor retrieval, involve alone conditions on objects geometric properties. Today, many trendy applications concern novel varieties of queries that aim to go looking out objects satisfying every a abstraction predicate, and a predicate on their associated texts. As associate example, instead of considering all the restaurants, a nearest neighbor question would instead provoke the eating place that is the nearest among those whose menus contain asteak, ˆ spaghetti, brandyaˆ all at identical time. Currently, the best answer to such queries depends on the IR2-tree, which, as shown throughout this paper, contains many deficiencies that seriously impact its efficiency. motivated by this, It tend to develop a latest access methodology called the abstraction inverted index that extends the traditional inverted index to subsume f-dimensional info, and comes with algorithms that will answer nearest neighbor queries with keywords in real time. As verified by experiments, the projected techniques trounce the IR2-tree in question latent amount considerably, generally by a part of orders of magnitude

BEST RESULTS FOR SPATIAL QUERIES USING FAST NEAREST NEIGHBOR SEARCH WITH KEYWORDS

Conventional spatial queries, such as range search and nearest neighbour 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. 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 “steak, spaghetti, brandy” all at the same time. 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 neighbour 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

Science Concierge: A fast content-based recommendation system for scientific publications

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.Comment: 12 pages, 5 figure

HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search

Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity

High-dimensional approximate nearest neighbor: k-d Generalized Randomized Forests

We propose a new data-structure, the generalized randomized kd forest, or kgeraf, for approximate nearest neighbor searching in high dimensions. In particular, we introduce new randomization techniques to specify a set of independently constructed trees where search is performed simultaneously, hence increasing accuracy. We omit backtracking, and we optimize distance computations, thus accelerating queries. We release public domain software geraf and we compare it to existing implementations of state-of-the-art methods including BBD-trees, Locality Sensitive Hashing, randomized kd forests, and product quantization. Experimental results indicate that our method would be the method of choice in dimensions around 1,000, and probably up to 10,000, and pointsets of cardinality up to a few hundred thousands or even one million; this range of inputs is encountered in many critical applications today. For instance, we handle a real dataset of $10^6$ images represented in 960 dimensions with a query time of less than $1$sec on average and 90\% responses being true nearest neighbors