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BD Spokes: PLANNING: MIDWEST: Cyberinfrastructure to Enhance Data Quality and Support Reproducible Results in Sensor Originated Big Data

BD 发言人：规划：中西部：网络基础设施可提高数据质量并支持传感器产生的大数据的可重复结果

基本信息

批准号：
1636891
负责人：
Elisa Bertino
金额：
$ 9.99万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2018-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636891&HistoricalAwards=false
关键词：
BD Spokes PLANNING MIDWEST Cyberinfrastructure

项目摘要

Today, scientists within numerous biophysical and agricultural sciences utilize sensors to collect a multitude of data including weather, plant pathogens, energy distribution and water consumption, and apply data management techniques to derive additional knowledge. A critical requirement for the use of such data is to make sure that data do not have errors, are accurate, complete and up to date. Low quality data may be caused by a variety of real world issues including faulty equipment, software faults, or adverse environmental conditions. However manually validating data quality is not viable for very large sensor systems with high volumes of generated data, and delays in validation may result in losing data which is impossible to regenerate due to changing temporal conditions. Also in scientific research it is today critical that research processes be reproducible in order to validate research results and detect scientific frauds. Research reproducibility is particularly challenging for experiments that include sensors because re-creating the same physical conditions in which data has been captured may often be difficult if possible at all. This project aims at creating and fostering a multi-disciplinary community focusing on data quality and research results reproducibility for sensor-based experiments. Two workshops will be organized to define the foundational concepts and requirements for data quality and research reproducibility, and to identify related requirements for the development of suitable cyberinfrastructures (CI). Initial approaches will be tested within the CRIS system, developed at Purdue University, as a scalable CI, and advances will be demonstrated in an integrated pilot system and tested at the Purdue University ACRE research and education facility.The project will result in an understanding of the requirements that a CI must address in order to support data quality and research results reproducibility also when sensors are part of the CI. These requirements will lead to scientific advances in several areas including: data quality for big data; data quality assessment for sensor-originated data; management techniques for sensor-based experiments; extended metadata for research results reproducibility; scientific workflows systems. The project will also result in advances in the use of CI in the area of agriculture applications with respect to the use of sensors and to the quality of data. Our project will have a broader impact on any data-intensive application, especially applications characterized by big interrelated data, as data inter-relationships offer interesting opportunities for data quality. The project will contribute to the discussion on research results reproducibility in that it will show CI tools that can help reproducibility.

今天，许多生物物理和农业科学领域的科学家利用传感器收集大量数据，包括天气、植物病原体、能源分布和水消耗，并应用数据管理技术获得更多知识。使用这些数据的一个关键要求是确保数据没有错误，准确，完整和最新。低质量数据可能是由各种真实的问题引起的，包括设备故障、软件故障或不利的环境条件。然而，手动验证数据质量对于具有大量生成的数据的非常大的传感器系统是不可行的，并且验证中的延迟可能导致丢失由于变化的时间条件而不可能重新生成的数据。同样在科学研究中，为了验证研究结果和发现科学欺诈，研究过程的可复制性今天也至关重要。研究再现性对于包括传感器的实验来说特别具有挑战性，因为重新创建捕获数据的相同物理条件通常可能很困难。该项目旨在创建和培养一个多学科社区，专注于基于传感器的实验的数据质量和研究结果的可重复性。将组织两次研讨会，以确定数据质量和研究再现性的基本概念和要求，并确定开发合适的网络基础设施（CI）的相关要求。最初的方法将在普渡大学开发的CRIS系统中进行测试，作为一个可扩展的CI，并将在一个集成的试点系统中展示进展，并在普渡大学ACRE研究和教育设施进行测试。该项目将导致对CI必须满足的要求的理解，以支持数据质量和研究结果的再现性，当传感器也是CI的一部分。这些要求将导致多个领域的科学进步，包括：大数据的数据质量;传感器源数据的数据质量评估;基于传感器的实验的管理技术;研究结果再现性的扩展元数据;科学工作流程系统。该项目还将促进在农业应用领域使用信息和传播技术，包括传感器的使用和数据质量。我们的项目将对任何数据密集型应用程序产生更广泛的影响，特别是以大量相互关联的数据为特征的应用程序，因为数据相互关系为数据质量提供了有趣的机会。该项目将有助于研究结果的可重复性的讨论，因为它将展示有助于可重复性的CI工具。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The Challenge of Access Control Policies Quality

访问控制策略质量的挑战

DOI：
10.1145/3209668
发表时间：
2018
期刊：
Journal of Data and Information Quality
影响因子：
0
作者：
Bertino, Elisa;Jabal, Amani Abu;Calo, Seraphin;Verma, Dinesh;Williams, Christopher
通讯作者：
Williams, Christopher

Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures