Broadcast Schedules and Query Processing for k Nearest Neighbors Search on Multi-dimensional Index Trees in a Multi-Channel Environment

Effective protocols for kNN search on broadcast multi-dimensional index trees

Nearest Neighbor (NN) search plays important roles in Computer Vision algorithms. Especially, NN search on immensely large amount of image data set stored on the Internet is getting highlighted. For dealing with such huge data, main memory of a single PC is insufficient. As a solution, we propose an approximate NN search on hard disk drive (HDD) in this paper. This algorithm is based on recently proposed Principal Component Hashing (PCH). In our algorithm “PCH on HDD” (PCHD), the hash bins are represented by the leaf nodes of B+ tree for dealing with the dynamic addition and deletion of the data. Of course, the search time is slower than the original PCH. However, we found some advantages of this approach through the experiments using standard PC and 10000 stored images: 1) the memory consumption is 42 times smaller, 2) the first search time including the cold start-up time is 4.3 times faster (PCH:31.8[s], PCHD: 7.4[s]), 3) and interestingly, the successive searches are accelerated owing to the cache mechanism embedded in the operating system (mean search time decreases from 7.4[s] to 0.64[s]). We also confirmed that our algorithm performs NN search on 1 million image datasets with only 193MB memory consumption; however, PCH cannot, because of the huge memory consumption. These properties reveal that this algorithm is suitable for non-time-critical NN search applications and NN search engine called by web servers, where the search engine starts up in response to occasional queries.

Nearest Neighbor (NN in short) queries are important in emerging applications,\n\nsuch as wireless networks, location-based services, and data stream applications,\n\nwhere the data obtained are often imprecise. The imprecision or imperfection of\n\nthe data sources is modeled by uncertain data in recent research works. Handling\n\nuncertainty is important because this issue affects the quality of query answers.\n\nAlthough queries on uncertain data are useful, evaluating the queries on them can\n\nbe costly, in terms of I/O or computational efficiency. In this thesis, we study how\n\nto efficiently evaluate NN queries on uncertain data.\n\n\n\nGiven a query point q and a set of uncertain objects O, the possible nearest neighbor query returns a set of candidates which have non-zero probabilities to be the\n\nquery answer. It is also interesting to ask \\which region has the same set of possible nearest neighbors", and \\which region has one specific object as its possible\n\nnearest neighbor". To reveal the relationship between the query space and nearest\n\nneighbor answers, we propose the UV-diagram, where the query space is split into\n\ndisjoint partitions, such that each partition is associated with a set of objects. If a\n\nquery point is located inside the partition, its possible nearest neighbors could be\n\ndirectly retrieved. However, the number of such partitions is exponential and the\n\nconstruction effort can be expensive. To tackle this problem, we propose an alternative concept, called UV-cell, and efficient algorithms for constructing it. The UV-cell has an irregular shape, which incurs difficulties in storage, maintenance,\n\nand query evaluation. We design an index structure, called UV-index, which is\n\nan approximated version of the UV-diagram. Extensive experiments show that\n\nthe UV-index could efficiently answer different variants of NN queries, such as\n\nProbabilistic Nearest Neighbor Queries, Continuous Probabilistic Nearest Neighbor\n\nQueries.\n\n\n\nAnother problem studied in this thesis is the trajectory nearest neighbor query.\n\nHere the query point is restricted to a pre-known trajectory. In applications (e.g.\n\nmonitoring potential threats along a flight/vessel's trajectory), it is useful to derive\n\nnearest neighbors for all points on the query trajectory. Simple solutions, such as\n\nsampling or approximating the locations of uncertain objects as points, fails to\n\nachieve a good query quality. To handle this problem, we design efficient algorithms\n\nand optimization methods for this query. Experiments show that our solution can\n\nefficiently and accurately answer this query. Our solution is also scalable to large\n\ndatasets and long trajectories.

Data broadcasting is an effective way to disseminate information to a large amount of mobile clients in wireless mobile environments. K nearest neighbors (kNN) search is one of the important location-based services and allows clients to get the points of interests around them in a given data set. In this paper, we propose a kNN search protocol using multiple data broadcast channel. We provide several data broadcast schedules for multiple channels and compare them with the ones for single channel using the latency and tuning time as the measurements. The latency is the time elapsed between issuing and termination of the query and the tuning time is the amount of time spent on listening to the channel. In our design, we use Voronoi diagram and no index structure is referred in the broadcast. The proposed protocol will use some additional information for each broadcast data instead to achieve the objectives. The effectiveness and performance of the proposed protocol will be verified by proofs and simulation experiments.

Scalable nearest neighbor query processing based on Inverted Grid Index

Location-based services (LBSs) provide the information based on location information as specified in a query. Queries that support LBS are called Location-Dependent Queries (LDQ). LDQ contain: range query, nearest neighbor (NN) query, k-nearest neighbor (KNN) query and reverse nearest neighbor (RNN) query. Examples of mobile LBSs include location-dependent information access (e.g., traffic reports and attractions) and nearest neighbor queries (e.g. fmding the nearest restaurant). While LDQ is well studied in the traditional wired, disk-based client-server environment, it has not yet been tackled in relation to a wireless broadcasting environrent. In this paper, the issues involved with organizing location-dependent data and answering LDQ queries on the air, aze investigated. The lineaz property of wireless broadcasting media and the power conservation requirement of mobile devices make the problem particularly interesting and challenging. An efficient data organization, called Jump Rdnn-Tree, and...

VRP distributions have had difficulty overcoming the problem of finding channels with minimal depots to locations that have different places with different total demand. The purpose of this study is to analyze the problem of transportation routes in the distribution of products obtained from the initial location of distribution to users. This type of research is qualitative research. This research was conducted at PT. Nusa Persada Concrete Creations. The Nearest Neighbor method is used to determine the distribution of routes. The Local Search method is carried out to evaluate and improve the distribution of routes carried out at the beginning with the Nearest Neighbors method. The data analysis process consists of several stages with the Nearest Neighbor method and the LocalSearch method. The results of the study, namely the Model Vehicle Routing Problem (VRP) applied in determining ready mix delivery routes at PT. Nusapersada Concrete Creation using nearest and local neighbor methods. Vehicle Routing Problem (VRP) models using nearest and local neighbor methods can be used applied in determining ready mix delivery routes to limited companies. Nusapersada Concrete Creations. This makes distance and time more effective, as well as more cost efficient. New routes generated This is a route improvement solution that PT. The application of the Nusapersada Concrete Creations model results in a new route that reduces the distance closer, faster completion time, and fuel cost savings for truck vehicles compared to the initial route. This makes distance and time more effective, as well as more cost efficient.

Nearest neighbor proximity search in large graphs is an important analysis primitive with a variety of applications in graph data from different domains. We propose a novel proximity measure for weighted graphs called Effective Importance which incorporates multiple paths between nodes and captures the inherent structural clusters within a network. We develop effective bounds on the EI value using a modified small subnetwork around a query node, enabling scalable exact nearest neighbor (NN) search at query time. Our NN search does not require heavy offline analysis or holistic knowledge of the graph, making our method suitable for very large dynamically changing networks or composite network overlays.We employ our NN search algorithm on social, information and biological networks and demonstrate the effectiveness and scalability of the approach. For million-node networks, our method retrieves the exact top 20 neighbors using less than $0.2%$ of the network edges in a fraction of a second on a conventional desktop machine. We also evaluate the effectiveness of our proximity measure and NN search for three applications, namely (i) finding good local clusters, (ii) network sparsification and (iii) prediction of node attributes in information networks. The EI measure and NN search method outperform recent counterparts from the literature in all applications.

Nearest neighbor search and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph construction are two fundamental issues that arise from many disciplines such as multimedia information retrieval, data-mining, and machine learning. They become more and more imminent given the big data emerge in various fields in recent years. In this paper, a simple but effective solution both for approximate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor search and approximate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph construction is presented. These two issues are addressed jointly in our solution. On one hand, the approximate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph construction is treated as a search task. Each sample along with its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbors is joined into the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph by performing the nearest neighbor search sequentially on the graph under construction. On the other hand, the built <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph is used to support <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor search. Since the graph is built online, the dynamic update on the graph, which is not possible for most of the existing solutions, is supported. This solution is feasible for various distance measures. Its effectiveness both as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor construction and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor search approaches is verified across different types of data in different scales, various dimensions, and under different metrics.

Recently, complicated spatial search algorithms have emerged as spatial-information-based applications, such as location-based services (LBS), and have become very diverse and frequent. The aggregate nearest neighbor (ANN) search is an extension of the existing nearest neighbor (NN) search; it finds the object p* that minimizes G{d(p*,qi),qi∈Q} from a set Q of M (≥1) query objects, where G is an aggregate function and d() is the distance between two objects. The flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search by introducing flexibility factor ϕ(0&lt;ϕ≤1); it finds the object p* that minimizes G{d(p*,qi),qi∈Qϕ} from Qϕ, a subset of Q with |Qϕ|=ϕ|Q|. This paper proposes an efficient ϵ-approximate k-FANN (k≥1) search algorithm for an arbitrary approximation ratio ϵ (≥1) in road networks. In general, ϵ-approximate algorithms are expected to give an improved search performance at the cost of allowing an error ratio of up to the given ϵ. Since the optimal value of ϵ varies greatly depending on applications and cases, the approximate algorithm for an arbitrary ϵ is essential. We prove that the error ratios of the approximate FANN objects returned by our algorithm do not exceed the given ϵ. To the best of our knowledge, our algorithm is the first ϵ-approximate k-FANN search algorithm in road networks for an arbitrary ϵ. Through a series of experiments using various real-world road network datasets, we demonstrated that our approximate algorithm always outperformed the previous state-of-the-art exact algorithm and that the error ratios of the approximate FANN objects were significantly lower than the given ϵ value.

Nearest neighbor (NN) search in high-dimensional space plays a fundamental role in large-scale image retrieval. It seeks efficient indexing and search techniques, both of which are simultaneously essential for similarity search and semantic analysis. However, in recent years, there has been a rare breakthrough. Achievement of current techniques for NN search is far from satisfactory, especially for exact NN search. A recently proposed method, HB, addresses the exact NN search efficiently in high-dimensional space. It benefits from cluster-based techniques which can generate more compact representation of the data set than other techniques by exploiting interdimensional correlations. However, HB suffers from huge cost for lower bound computations and provides no further pruning scheme for points in candidate clusters. In this paper, we extend the HB method to address exact NN search in correlated, high-dimensional vector data sets extracted from large-scale image database by introducing two new pruning/selection techniques and we call it HB+. The first approach aims at selecting more quickly the subset of hyperplanes/clusters that must be considered. The second technique prunes irrelevant points in the selected subset of clusters. Performed experiments show the improvement of HB+ with respect to HB in terms of efficiency (I/O cost and CPU response time) and also demonstrate the superiority over other exact NN indexes.

Nearest neighbor (NN) search is an essential operation in many applications, such as one/few-shot learning and image classification. As such, fast and low-energy hardware support for accurate NN search is highly desirable. Ternary content-addressable memories (TCAMs) have been proposed to accelerate NN search for few-shot learning tasks by implementing $L$ ∞ and Hamming distance metrics, but they cannot achieve software-comparable accuracies. This paper proposes a novel distance function that can be natively evaluated with multi-bit content-addressable memories (MCAMs) based on ferroelectric FETs (Fe-FETs) to perform a single-step, in-memory NN search. Moreover, this approach achieves accuracies comparable to floating-point precision implementations in software for NN classification and one/few-shot learning tasks. As an example, the proposed method achieves a 98.34% accuracy for a 5-way, 5-shot classification task for the Omniglot dataset (only 0.8% lower than software-based implementations) with a 3-bit MCAM. This represents a 13% accuracy improvement over state-of-the-art TCAM-based implementations at iso-energy and iso-delay. The presented distance function is resilient to the effects of FeFET device-to-device variations. Furthermore, this work experimentally demonstrates a 2-bit implementation of FeFET MCAM using AND arrays from GLOBALFOUNDRIES to further validate proof of concept.

Abstract. 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 tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized.

Nearest neighbor search is a fundamental problem in computer vision and machine learning. The straightforward solution, linear scan, is both computationally and memory intensive in large scale high-dimensional cases, hence is not preferable in practice. Therefore, there have been a lot of interests in algorithms that perform approximate nearest neighbor (ANN) search. In this paper, we propose a novel addition-based vector quantization algorithm, Asymmetric Mapping Quantization (AMQ), to efficiently conduct ANN search. Unlike existing addition-based quantization methods that suffer from handling the problem caused by the norm of database vector, we map the query vector and database vector using different mapping functions to transform the computation of L-2 distance to inner product similarity, thus do not need to evaluate the norm of database vector. Moreover, we further propose Distributed Asymmetric Mapping Quantization (DAMQ) to enable AMQ to work on very large dataset by distributed learning. Extensive experiments on approximate nearest neighbor search and image retrieval validate the merits of the proposed AMQ and DAMQ.

Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii) its query cost should increase sublinearly with the dataset size, regardless of the data and query distributions. Locality-Sensitive Hashing (LSH) is a well-known methodology fulfilling both requirements, but its current implementations either incur expensive space and query cost, or abandon its theoretical guarantee on the quality of query results. Motivated by this, we improve LSH by proposing an access method called the Locality-Sensitive B-tree (LSB-tree) to enable fast, accurate, high-dimensional NN search in relational databases. The combination of several LSB-trees forms a LSB-forest that has strong quality guarantees, but improves dramatically the efficiency of the previous LSH implementation having the same guarantees. In practice, the LSB-tree itself is also an effective index which consumes linear space, supports efficient updates, and provides accurate query results. In our experiments, the LSB-tree was faster than: (i) iDistance (a famous technique for exact NN search) by two orders of magnitude, and (ii) MedRank (a recent approximate method with nontrivial quality guarantees) by one order of magnitude, and meanwhile returned much better results. As a second step, we extend our LSB technique to solve another classic problem, called Closest Pair (CP) search, in high-dimensional space. The long-term challenge for this problem has been to achieve subquadratic running time at very high dimensionalities, which fails most of the existing solutions. We show that, using a LSB-forest, CP search can be accomplished in (worst-case) time significantly lower than the quadratic complexity, yet still ensuring very good quality. In practice, accurate answers can be found using just two LSB-trees, thus giving a substantial reduction in the space and running time. In our experiments, our technique was faster: (i) than distance browsing (a well-known method for solving the problem exactly) by several orders of magnitude, and (ii) than D-shift (an approximate approach with theoretical guarantees in low-dimensional space) by one order of magnitude, and at the same time, outputs better results.