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Titlebook: Biomedical Data Management and Graph Online Querying; VLDB 2015 Workshops, Fusheng Wang,Gang Luo,Cong Yu Conference proceedings 2016 Spring

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發(fā)表于 2025-3-21 20:03:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Biomedical Data Management and Graph Online Querying
期刊簡稱VLDB 2015 Workshops,
影響因子2023Fusheng Wang,Gang Luo,Cong Yu
視頻videohttp://file.papertrans.cn/189/188005/188005.mp4
發(fā)行地址Includes supplementary material:
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Biomedical Data Management and Graph Online Querying; VLDB 2015 Workshops, Fusheng Wang,Gang Luo,Cong Yu Conference proceedings 2016 Spring
影響因子.This book constitutes the refereed proceedings of the two International Workshops on Big-Graphs Online Querying, Big-O(Q) 2015, and Data Management and Analytics for Medicine and Healthcare, DMAH 2015, held at Waikoloa, Hawaii, USA on August 31 and September 4, 2015, in conjunction with the 41st International Conference on Very Large Data Bases, VLDB 2015. . The 9 revised full papers presented together with 5 invited papers and 1 extended abstract were carefully reviewed and selected from 22 initial submissions. The papers are organized in topical sections on information retrieval and data analytics for electronic medical records; data management and visualization of medical data; biomedical data sharing and integration; medical imaging analytics; and big-graphs online querying..
Pindex Conference proceedings 2016
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LUCAS Associative Array Processorare, essentially, small segments of the terminology graphs. Compositional expressions add logical and linguistic relations to the standard technique of post-coordination. In indexing medical text, many instances of compositional expressions must be stored, and in performing retrieval on that index,
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Conclusions and continued research, algorithms, we have implemented an innovative two-layer multiplex network of human diseases. Specifically, we extract the International Classification of Diseases, Ninth Revision (ICD9) codes from an Electronic Medical Records (EMRs) database to build our map of human diseases. In the lower layer,
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LUCAS Associative Array Processores. The availability and scalability of cloud resources along with techniques for caching and distributed computation can be used to address these problems, but bring up new optimization challenges, often with competing concerns. A user wants a quick response using minimal device resources, while a
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Parallel and associative processing,xisting analytic approaches, however, require expert hand-digitization to extract parameters of interest. This produces repeatable measurements, but remains subjective and does not offer information on the precision of the measured parameters or strict reproducibility of analyzed data. We developed
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LUCAS Associative Array Processorlts, and facilitate algorithm sensitivity studies. The sizes of images and analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present SparkGIS, a distributed, in-memory spatial data processing framework to query, retrieve, and compare large volumes o
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