The GEOSS clearinghouse high performance search engine

Author(s):  
Kai Liu ◽  
Chaowei Yang ◽  
Wenwen Li ◽  
Zhenlong Li ◽  
Huayi Wu ◽  
...  
2016 ◽  
Vol 58 (2) ◽  
Author(s):  
Lambert Schomaker

AbstractThis article gives an overview of design considerations for a handwriting search engine based on pattern recognition and high-performance computing, “Monk”. In order to satisfy multiple and often conflicting technological requirements, an architecture is used which heavily relies on high-performance computing, interactivity, and a Posix file-access model for the scientific programmers. The resulting system is able to handle billions of image files, in the order of petabytes of storage capacity, with a single mount point. Monk is operational since the year 2009.


2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Bin Li ◽  
Poshen B Chen ◽  
Yarui Diao

Abstract CRISPR is a revolutionary genome-editing tool that has been broadly used and integrated within novel biotechnologies. A major component of existing CRISPR design tools is the search engines that find the off-targets up to a predefined number of mismatches. Many CRISPR design tools adapted sequence alignment tools as the search engines to speed up the process. These commonly used alignment tools include BLAST, BLAT, Bowtie, Bowtie2 and BWA. Alignment tools use heuristic algorithm to align large amount of sequences with high performance. However, due to the seed-and-extend algorithms implemented in the sequence alignment tools, these methods are likely to provide incomplete off-targets information for ultra-short sequences, such as 20-bp guide RNAs (gRNA). An incomplete list of off-targets sites may lead to erroneous CRISPR design. To address this problem, we derived four sets of gRNAs to evaluate the accuracy of existing search engines; further, we introduce a search engine, namely CRISPR-SE. CRISPR-SE is an accurate and fast search engine using a brute force approach. In CRISPR-SE, all gRNAs are virtually compared with query gRNA, therefore, the accuracies are guaranteed. We performed the accuracy benchmark with multiple search engines. The results show that as expected, alignment tools reported an incomplete and varied list of off-target sites. CRISPR-SE performs well in both accuracy and speed. CRISPR-SE will improve the quality of CRISPR design as an accurate high-performance search engine.


Author(s):  
Lin-Chih Chen

Both the blog search engine and the general search engine automatically crawl the pages from the web and produce relevant search results based on the user's query. The first difference between the two types is that the blog search engine focuses on dealing with blog posts and filters out other types of pages. This difference allows bloggers only to care about posts rather than all pages that are indexed by general search engines. The second difference is the post, considering more time-related issues compared to the page. The semantic analysis model is widely used to analyze the various semantic relationships that may arise in the document. In this article, the authors propose a new semantic analysis model to find possible time relationships between posts. The main contribution of this paper has two points: first is that this paper builds a high-performance search system that considers the discussion topic and updated time between posts; second, is that the authors consider the time relationships between posts that can rank the relevant blog topics based on the popularity of the posts.


2018 ◽  
Vol 31 (3) ◽  
pp. 341-349 ◽  
Author(s):  
Ren-zhi Li ◽  
Bo-jie Li ◽  
Guo-zhen Zhang ◽  
Jun Jiang ◽  
Yi Luo

Author(s):  
N. Soni ◽  
N. Richardson ◽  
Lun-Bin Huang ◽  
S. Rajgopal ◽  
G. Vlantis

Author(s):  
Ali Cevahir ◽  
Junji Torii

The authors propose an online image search engine based on local image keypoint matching with GPU support. State-of-the-art models are based on bag-of-visual-words, which is an analogy of textual search for visual search. In this work, thanks to the vector computation power of the GPU, the authors utilize real values of keypoint descriptors and realize real-time search at keypoint level. By keeping the identities of each keypoint, closest keypoints are accurately retrieved. Image search has different characteristics than textual search. The authors implement one-to-one keypoint matching, which is more natural for images. The authors utilize GPUs for every basic step. To demonstrate practicality of GPU-extended image search, the authors also present a simple bag-of-visual-words search technique with full-text search engines. The authors explain how to implement one-to-one keypoint matching with text search engine. Proposed methods lead to drastic performance and precision improvement, which is demonstrated on datasets of different sizes.


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