Πλοήγηση ανά Θέμα "Nearest neighbor search"
Αποτελέσματα 1-20 από 28
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Algorithms for processing the group K nearest-neighbor query on distributed frameworks
(2021)Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum of distances to every point of the Query. This spatial query ... -
Approximate kNN Classification for Biomedical Data
(2020)We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution ... -
Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach
(2022)Two modeling approaches for the estimation of durum wheat yield based on Sentinel-2 data are presented for 66 fields across three growing periods. In the first approach, a previously developed multiple linear regression ... -
Classification of Driving Behaviour using Short-term and Long-term Summaries of Sensor Data
(2020)The classification of driving behaviour is important for monitoring driving risk and fuel efficiency, as well as for adaptive driving assistance and car insurance industry. Starting from raw measurements of acceleration ... -
Crowd Sourcing as an Improvement of N-Grams Text Document Classification Algorithm
(2020)A common task in a world of natural language processing is text classification useful for e.g.spam filters, documents sorting, science articles classification or plagiarism detection. This can still be done best and most ... -
Efficient distance join query processing in distributed spatial data management systems
(2020)Due to the ubiquitous use of spatial data applications and the large amounts of such data these applications use, the processing of large-scale distance joins in distributed systems is becoming increasingly popular. Distance ... -
Efficient processing of all-k-nearest-neighbor queries in the MapReduce programming framework
(2019)Numerous modern applications, from social networking to astronomy, need efficient answering of queries on spatial data. One such query is the All k Nearest-Neighbor Query, or k Nearest-Neighbor Join, that takes as input ... -
Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces
(2019)We are in the era of single-cell RNA sequencing technology, which offers a great potential for uncovering cellular differences with a higher resolution, shedding light in various complex biological processes and complex ... -
Enhancing Sedona (formerly GeoSpark) with Efficient k Nearest Neighbor Join Processing
(2021)Sedona (formerly GeoSpark) is an in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, indexes, and ... -
Enhancing spatialhadoop with closest pair queries
(2016)Given two datasets P and Q, the K Closest Pair Query (KCPQ) finds the K closest pairs of objects from P×Q. It is an operation widely adopted by many spatial and GIS applications. As a combination of the K Nearest Neighbor ... -
GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Disk-Resident Data
(2021)Algorithms for answering the k Nearest-Neighbor (k-NN) query are widely used for queries in spatial databases and for distance classification of a group of query points against a reference dataset to derive the dominating ... -
An Improved GPU-based Algorithmfor Processing the k Nearest Neighbor Query
(2020)The k Nearest Neighbor (k-NN) query is a common spatial query that appears in several big data applications. We propose and implement a new GPU-based algorithm for the k-NN query, which improves our previous Symmetric ... -
Improving Distance-Join Query processing with Voronoi-Diagram based partitioning in SpatialHadoop
(2020)SpatialHadoop is an extended MapReduce framework supporting global indexing techniques that partition spatial datasets across several machines and improve spatial query processing performance compared to traditional Hadoop ... -
In-memory k nearest neighbor GPU-based query processing
(2020)The k Nearest Neighbor (k-NN) algorithm is widely used for classification in several application domains (medicine, economy, entertainment, etc.). Let a group of query points, for each of which we need to compute the k-NNs ... -
Initialization of dynamic time warping using tree-based fast Nearest Neighbor
(2016)An efficient way to perform Dynamic Time Warping (DTW) search is by using the LBKeogh lower bound, which can eliminate a large number of candidate vectors out of the search process. Although effective, LBKeogh begins the ... -
Intrusion detection system for platooning connected autonomous vehicles
(2019)The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication ... -
The K group nearest-neighbor query on non-indexed RAM-resident data
(2016)Data sets that are used for answering a single query only once (or just a few times) before they are replaced by new data sets appear frequently in practical applications. The cost of buiding indexes to accelerate query ... -
Machine learning approaches for predicting health risk of cyanobacterial blooms in Northern European Lakes
(2020)Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail ... -
MapReduce algorithms for the k group nearest-neighbor query
(2019)Given two datasets of points (called Query and Training), the Group (K) Nearest Neighbor (GNN) query retrieves (K) points of the Training dataset with the smallest sum of distances to every point of the Query one. This ... -
MRSLICE: Efficient RkNN Query Processing in SpatialHadoop
(2019)Nowadays, with the continuously increasing volume of spatial data, it is difficult to execute spatial queries efficiently in spatial data-intensive applications, because of the limited computational capability and storage ...