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QuantiCode: Intelligent infrastructure for quantitative, coded longitudinal data

QuantiCode：定量、编码纵向数据的智能基础设施

基本信息

批准号：
EP/N013980/1
负责人：
Roy Ruddle
金额：
$ 124.6万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN013980%2F1
关键词：
QuantiCode Intelligent infrastructure quantitative coded

项目摘要

This cross-disciplinary project aims to develop novel data mining and visualization tools and techniques, which will transform people's ability to analyse quantitative and coded longitudinal data. Such data are common in many sectors. For example, health data is classified using a hierarchy of hundreds of thousands of Read Codes (a thesaurus of clinical terms), with analysts needing to provide business intelligence for clinical commissioning decisions, and researchers tacking challenges such modelling disease risk stratification. Retailers such as Sainsbury's sell 50,000+ types of products, and want to combine data from purchasing, demographic and other sources to understand behavioural phenomena such as the convenience culture, to guide investment and reduce waste.To solve these needs, public and private sector organisations require an infrastructure that provides far more powerful analytical tools than are available today. Today's analysis tools are deficient because they (a) are crude for assessing data quality, (b) often involve analysis techniques are designed to operate on aggregated, rather than fine-grained, data, and (c) are often laborious to use, which inhibits users from discovering important patterns.The QuantiCode project will address these deficiencies by bringing together experts in statistics, modelling, visualization, user evaluation and ethics. The project will be based in the Leeds Institute for Data Analytics (LIDA), which houses the ESRC Consumer Data Research Centre (£5m ES/L011891/1) and the MRC Medical Bioinformatics Centre (£7m ES/L011891/1), and provides a development facilities complete with high-performance computing (HPC), visualization and safe rooms for sensitive data. Our project will deliver proof of concept visual analytic systems, which we will evaluate with a wide variety of users drawn from our partners and researchers/external users based in LIDA.At the outset of the project we will engage with our partners to identify analysis use cases and requirements that drive the details of our research, which is divided into four workpackages (WPs). WP1 (Data Fusion) will develop governance principles for the analysis of fine-grained data from multiple sources, implement tools to substantially reduce the effort of linking those sources, and develop new techniques to visualize completeness, concordance, plausibility, and other aspects of data quality.WP2 (Analytical Techniques) and WP3 (Abstraction Models) are the project's technical core. WP2 will deliver a new, robust approach for modelling data as they appear naturally in health and retail data (irregularly dispersed or sampled over time), scaling that approach with stochastic control to guide learning and resource usage, and developing a low-effort 'question-posing' visual interface to drastically lower the human effort of investigating data and finding patterns. WP3 (Abstraction Models) focuses on data granularity, and will deliver a tool that implements a working version of the governance principles we develop in WP1, and new computational and interactive techniques for exploring abstraction spaces to create inputs suited to each aspect of analysis.WP4 will implement the above tools and techniques in three versions of our proof of concept system, evaluating each with our partners and LIDA researchers/users. This will ensure that our solutions are compatible with, and scale to, challenging real-world data analysis problems. Success criteria will be time saved, increased analysis scope, notable insights, and tackling previously unfeasible types of analysis - all compared against a baseline provided by users' current analysis tools. We will encourage adoption via showcases, workshops and licensed installations at our partners' sites. The project's legacy will include tools that are embedded as an integral part of the LIDA infrastructure, a plan for their on-going development, and a research roadmap.

这个跨学科项目旨在开发新的数据挖掘和可视化工具和技术，这将改变人们分析定量和编码纵向数据的能力。这样的数据在许多行业都很常见。例如，使用由数十万个Read Codes（临床术语的同义词库）组成的层次结构对健康数据进行分类，分析人员需要为临床委托决策提供商业智能，而研究人员则需要应对建模疾病风险分层等挑战。塞恩斯伯里（Sainsbury’s）等零售商销售5万多种产品，他们希望将采购、人口统计和其他来源的数据结合起来，了解便利文化等行为现象，以指导投资和减少浪费。为了满足这些需求，公共和私营部门组织需要一种基础设施，提供比目前更强大的分析工具。今天的分析工具是有缺陷的，因为它们(a)在评估数据质量方面是粗糙的，(b)通常涉及的分析技术被设计用于操作聚合数据，而不是细粒度数据，以及(c)通常使用起来很费力，这阻碍了用户发现重要的模式。QuantiCode项目将通过汇集统计、建模、可视化、用户评估和道德方面的专家来解决这些不足。该项目将以利兹数据分析研究所（LIDA）为基础，该研究所设有ESRC消费者数据研究中心（500万英镑ES/L011891/1）和MRC医学生物信息学中心（700万英镑ES/L011891/1），并为敏感数据提供高性能计算（HPC），可视化和安全室的开发设施。我们的项目将提供概念视觉分析系统的验证，我们将与来自LIDA的合作伙伴和研究人员/外部用户的各种用户进行评估。在项目开始时，我们将与我们的合作伙伴一起确定驱动我们研究细节的分析用例和需求，这些细节被划分为四个工作包（wp）。WP1（数据融合）将开发用于分析来自多个数据源的细粒度数据的治理原则，实现工具以大幅减少链接这些数据源的工作量，并开发新技术以可视化数据质量的完整性、一致性、合理性和其他方面。WP2（分析技术）和WP3（抽象模型）是项目的技术核心。WP2将提供一种新的、强大的方法来建模数据，因为它们自然地出现在健康和零售数据中（随着时间的推移不规则地分散或采样），用随机控制扩展该方法来指导学习和资源使用，并开发一个低成本的“提问”视觉界面，以大大降低调查数据和寻找模式的人力。WP3（抽象模型）专注于数据粒度，并将提供一个工具，实现我们在WP1中开发的治理原则的工作版本，以及用于探索抽象空间以创建适合分析各个方面的输入的新计算和交互技术。WP4将在我们的概念验证系统的三个版本中实现上述工具和技术，并与我们的合作伙伴和LIDA研究人员/用户一起对每个版本进行评估。这将确保我们的解决方案兼容并扩展到具有挑战性的现实世界数据分析问题。成功的标准将是节省时间、增加分析范围、显著的洞察力，以及处理以前不可行的分析类型——所有这些都与用户当前分析工具提供的基线进行比较。我们将在合作伙伴的网站上通过展示、研讨会和授权装置鼓励采用。该项目的遗产将包括作为LIDA基础设施的一个组成部分嵌入的工具，它们正在进行的开发计划和研究路线图。