Using Big Data Analytics to Create Better Outcomes for Cancer Patients
公司规模
1,000+
地区
- Europe
国家
- Germany
产品
- Datameer
- Fujitsu Prime Flex Integrated System for Hadoop
技术栈
- Hadoop
- Big Data Analytics
实施规模
- Enterprise-wide Deployment
影响指标
- Innovation Output
- Productivity Improvements
技术
- 分析与建模 - 大数据分析
- 基础设施即服务 (IaaS) - 云计算
适用行业
- 医疗保健和医院
适用功能
- 产品研发
用例
- 临床图像分析
- 预测性维护
服务
- 数据科学服务
关于客户
DKFZ is the largest biomedical research Institute in Germany. They are working to understand the mechanisms of cancer, identify risk factors, and find new ways to prevent people from getting cancer. A key focus of DKFZ’s medical researchers is genomic data research. By analyzing human genomes, they can identify DNA problems which are the root cause of cancer in an individual — information that can be used to personalize cancer treatment — as well as understand the genetic evolution of disease and monitor patient responses to different treatments to better understand their efficacy.
挑战
Cancer diagnosis is complicated due to the uniqueness of each case, and treatment outcomes vary greatly from patient to patient. DKFZ, the largest biomedical research Institute in Germany, is working to understand the mechanisms of cancer, identify risk factors, and find new ways to prevent people from getting cancer. A key focus of DKFZ’s medical researchers is genomic data research. However, due to the massive volumes of genomic data involved in this research, DKFZ faced huge challenges on the data and analytics front. Their analytic systems were overwhelmed by many petabytes of data, and analyzing an entire patient data set took weeks and even months to complete. These huge bottlenecks greatly slowed research and frustrated staff.
解决方案
DKFZ collaborated with Fujitsu to deploy the Fujitsu Prime Flex Integrated System for Hadoop, a powerful and scalable server cluster that uses Datameer’s analytic platform for parallel processing power and incredibly fast, actionable analytics. With Datameer, researchers can analyze the complete, raw genomic data sets of multiple patients in parallel, along with patient data records, detail selection data, and reference genome data. Analysis on the complete, unreduced data set can be performed by block of data and by patient. Using this new parallelization technique the analysis on the complete data set can be completed between five and twenty minutes. By comparisons, the analysis on the greatly reduced data set used to take 24-48 hours to complete.
运营影响
数量效益
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