[db tech showcase Tokyo 2017] D33: Deep Learningや、Analyticsのワークロードを加速するには-Ten...Insight Technology, Inc.
Deep Learningでは、GPUを用いた、コンピューティング環境を用意される事が多いですが、こちらを加速させる足回りについてはあまり意識されてきていませんでした。また、SparkでのAnalyticsについても、Pipeline処理の高速化が可能となりました。ピュアストレージが最新のユースケースのご紹介も兼ねて、AI時代のワークロードを実現する方法をお伝えします。
ほんとに使える?Big Data SQL検証結果から見る、その有益性(性能編)オラクルエンジニア通信
パートナー企業のNTTデータ先端技術株式会社様によるOracle Big Data SQL 検証の検証に関する資料です。
Oracle Exadata のDWH処理を Oracle Big Data Appliance にオフロードした構成で、DWH処理の性能やOLTP処理への影響などの観点で、検証頂いています。
より詳細なデータやノウハウについては、是非、NTTデータ先端技術株式会社様にお問い合わせください。
NTTデータ先端技術㈱オラクル事業部
oracle-sales@intellilink.co.jp
This is Couchbase japan community meet-up material for introduce Couchbase Mobile solution. Demonstration of Couchbase Mobile application(this is SFA demo application using GPS location data, message data, company data as so on, intercommunicate 2 iOS simulator via Couchbase Sync Gateway and Couchbase Server).
マイクロソフトは より効率的、かつ大量のデータを使ったデータ分析のための基盤を急ピッチで拡充しています。
分析自体やデータ準備の前処理における手段の1つとして使って頂くことを想定している各種製品・サービスについて説明します。
具体的には、R の並列実行環境である Microsoft R Server、Power BI、並列処理基盤である Azure Data Lake Analytics、Azure Machine Learning を取り上げます。
Database Integration to Improve Accessibility to High-Throughput Sequence DataTazro Ohta
This document discusses the need for reliable and accessible databases to store high-throughput sequencing (HTS) data. It describes how integrating databases with publications and metadata can improve searchability and reuse of HTS data. Specifically, it notes that integrating Japan's DRA HTS database with PubMed, PMC, and sequence read quality data sources allows for more efficient searches based on metadata. This enhanced metadata searchability addresses the previous problem of data being difficult to find without descriptions. The integration demonstrates the power of combining resources to improve data accessibility while maintaining reliability through curation. Going forward, the document suggests the database could further integrate alignment data and require analysis pipelines be publicly available to improve reproducibility.
Kusarinoko: developing the public next generation sequencing data search inte...Tazro Ohta
The Kusarinoko project aims to develop a public next generation sequencing data search interface that makes it easier to find and access data from the Sequence Read Archive (SRA). Problems with managing and searching the large amount of metadata in public databases motivated the creation of Kusarinoko. It integrates metadata from various SRA files, adds publication information and quality check results to provide more support for users. Statistics analyzed by the project provide insights into trends in the SRA, such as changes in sequencing platforms over time, and show that some data may lack sufficient quality despite being associated with published articles.
This document appears to be a collection of random Twitter links from various accounts posted over time. It does not form a coherent story or provide any essential information in its current state. The document references Twitter accounts and post IDs but does not include any tweet text or summaries.
This document discusses large-scale data in life sciences. It covers next-generation sequencing data such as short reads from sequencing platforms like Illumina HiSeq 2000. It also discusses techniques for analyzing sequencing data such as de novo assembly and reference alignment. Key algorithms mentioned include Velvet and SOAPdenovo for de novo assembly. Issues around processing large datasets with tools like Galaxy are also briefly covered.
The document provides information about an NGS database that maps public expression data to chromosomes and CPUs. It also includes stock photos and image links from photo sharing websites to illustrate the mapping of data.
This document discusses RNA-seq analysis using next-generation sequencing (NGS) tools. It introduces common NGS RNA-seq mapping tools like Bowtie, TopHat, and Cufflinks. The typical workflow involves using Bowtie to index the reference genome, TopHat to map RNA-seq reads to the genome, and Cufflinks to assemble transcripts and construct a transcriptome from the mapped RNA-seq reads. Cuffcompare can then be used to compare the assembled transcriptome to a reference annotation.