I-Corps: Faster than Light Big Data Analytics

数据驱动的决策正在改变社会。政治候选人现在整理和分析数据库，以确定可能的竞选支持者。现在，情侣们通过婚介网站在网上相识，这些网站根据个人资料和调查问卷将个人配对。营销人员现在根据广泛的网络和社交网络信号提供的细粒度市场细分来微调他们的信息。公司现在基于A/B测试或测量客户行为来开发产品。然而，用于分析的基本软件平台和算法的传播并不均衡。大公司可以雇佣程序员和数据科学家来运行复杂的分析，但大多数组织无法负担维护分析基础设施的大量资本和运营费用。这使得他们无法受到数据驱动决策的潜在变革性影响。该方案通过利用更通用、更高效的并行编程平台，拓展了高性能机器学习的空间。巨大的资本支出源于缺乏预测分析的交钥匙解决方案。应用程序是临时的，根据特定的组织需求重新开发。这些应用程序的数据需求产生了大量的操作费用，这需要许多机器小时才能产生结果。该团队已经开发了一个可重用的软件平台，该平台可以并行化分析应用程序，并具有数量级的性能改进，并且与通常部署的商业产品相比，可以使用一小部分硬件资源运行。在这个平台之上，该团队已经开始开发一套最先进的大规模可扩展机器学习算法。在这个项目的过程中，团队打算与数据分析的潜在客户广泛接触，以帮助了解拟议技术的可能商业模式。

Data-driven decision making is changing society. Political candidates now collate and analyze databases to identify possible campaign supporters. Couples now meet online through matchmaking websites that pair individuals based on profiles and questionnaires. Marketers now fine-tune their messages based on fine-grain market segmentation provided by extensive web and social network signals. Companiesnow develop products based on A/B testing or measured customer behavior. However, the diffusion of the basic software platforms and algorithms for analytics has been uneven. Large firms can afford to hire programmers and data scientists to run sophisticated analytics, but most organizations cannot afford the large capital and operational expenses to maintain analytics infrastructure. This leaves themout of the potentially transformative impact of data-driven decision making. This proposal expands the space of high-performance machine learning by harnessing a more general and more efficient parallel programming platform.The large capital expense stems from the lack of a turnkey solution for predictive analytics. Applications are ad-hoc, developed anew based on specific organizational requirements. The large operational expense arises from the data demand of these applications, which requires many machine-hours to produce results. The team has developed a reusable software platform that can parallelize analytics applications with orders of magnitude performance improvements and can run with a fraction of the hardware resources compared to commonly deployed commercial products. On top of this platform, the team has begun to develop a suite of state-of-the-art massively scalable machine learning algorithms. Through the course of this project, the team intends to broadly engage with potential customers of data analytics to help understand possible business models for the proposed technology.