IndexingKnowledge based systemsClustering algorithmsHeuristic algorithmsSearch problemsDNATextual information is ubiquitous in our lives and is becoming an important component of our cognitive society. In the age of big data, we consistently need to traverse substantial amounts of data even to find...
Graphs algorithms Graph indexing Shortest path Tree decomposition k Nearest Neighbors problems 1. Introduction Querying and manipulating large scale graph-like data have attracted much attention in the database community, due to the wide application areas of graph data, such as ranked keyword search, ...
During the last few years, there has been a significant advancement in Machine Learning (ML) algorithms, providing a framework to automatically extract information from data to enhance and accelerate numerical methods for scientific computing [56], [57], including for effective polygonal mesh agglomera...
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions, 459–468 (2006) Google Scholar Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions, 253–262 (2004) Google ...
Algorithms Used single insertion: non-recursive R-tree insertion with overlap minimizing split routine from R*-tree (split is very effective in JS, while other R*-tree modifications like reinsertion on overflow and overlap minimizing subtree search are too slow and not worth it) ...
3.4. Associativity Reconfigurable Cache By changing the indexing policy, cache blocks that originally belonged to two d8iofffe1r7- ent ways are concatenated to tune the associativity from four to two. Figure 7 illustrates the associativity reconfigurable cache with fixed cache size. Algorithms 2021, ...
An In-Depth Analysis of Concurrent B-Tree Algorithms The B tree is a data structure designed to efficiently support dictionary operations for a variety of applications. In order to increases throughput, many ... Wang, P - 《Journal of Hubei Engineering University》 被引量: 32发表: 1991年 Pre...
The current R-tree spatial clustering algorithms use a predefined k-value for clustering, and choose the initial clustering centers arbitrarily. The clustering results are easily dominated by the initial k-value and the outlier data. In order to solve these problems, this paper proposes a novel ...
The remainder of this paper will proceed as follows: Section 2 will detail the design of the Skiplist-Based LSM (sLSM), including the in-memory component, the on-disk component, indexing structures, key algorithms, theoretical guarantees, and the range of designknobs; Section 3 will provide ...
The latter two stages used an ensemble of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting), and a large set of potential predictors. Previous studies have used an ensemble of machine learning algorithms to produce NO2 concentrations at a high spatial...