Boosting Multimedia Big Data Systems

推动多媒体大数据系统

• 批准号: RGPIN-2017-06594
• 负责人: He, Wenbo
• 金额: $ 1.68万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750432
• 关键词: Boosting; Multimedia; Big; Data; Systems

Video data is considered as biggest big data. We have witnessed gigantic volume of data generated at a very fast speed in recent years. According to Cisco Systems, video data counts for around 80% of the Internet data in United States. Also, YouTube users upload more than 300 hours' video clips in every minute. Besides the large volume and velocity, the irregularities and ambiguities (variety property) of the video data make it difficult to compare, understand, and annotate the data using traditional technology and systems. In the proposed research program, our objective is to systematically address the challenges in understanding the complexity of multimedia big data; to handle scalability; to make trade-off between efficiency and accuracy of various solutions; and to design and implement systems to organize, classify, search, and retrieve multimedia contents. Specifically, we will conduct the following research activities, but not limited to (1) the investigation of video-feature representation based on individual frame features while preserve the temporal and spatial correlation among frames, (2) the development of underlying operating systems and indexing structures to support parallel computation on multimedia data in multi-core and distributed environments, (3) the use of semi-supervised graphical model to downplay the dependency of the state-of-the-art deep learning models on large size of high-quality training sets by exploiting the joint distribution between labeled and unlabeled data; (4) the use of systems approach to iteratively optimize individual components and system integration, which will eventually lead to both locally and globally optimization.My research group has been working on multimedia big data since 2013, and we have made a great progress on building a testbed to measure the performance of various combinations of video features, developing a pipeline architecture to support parallel multimedia content computation based on SPARK streaming, and implementing a prototype for multimedia data storage and indexing on a single multi-core server. We will continuously apply multiple system-oriented strategies to optimize the system performance, and extend the systems on GPUs (Graphics Processing Units) servers and in distributed environments. Meanwhile, the testbed, prototype system, as well as the models and theories that we have developed in the past a few years will serve as the basis and tools in design, implement, and verify the proposed research in the future.On the frontier of multimedia big data research, the proposed research program will also provide industrially-relevant and the most up-to-date training for HQP, helping to equip them with the skills and knowledge to make an impact in the rapidly growing big data industry in Canada.

视频数据被认为是最大的大数据。近年来，我们目睹了以非常快的速度产生的巨大数据量。根据思科系统公司的数据，视频数据占美国互联网数据的80%左右。此外，YouTube用户每分钟上传超过300小时的视频剪辑。除了大容量和速度之外，视频数据的不规则性和模糊性（多样性）使得难以使用传统技术和系统来比较、理解和注释数据。在拟议的研究计划中，我们的目标是系统地解决在理解多媒体大数据的复杂性方面的挑战;处理可扩展性;在各种解决方案的效率和准确性之间进行权衡;并设计和实现系统来组织，分类，搜索和检索多媒体内容。具体而言，我们将进行以下研究活动，但不限于（1）基于单个帧特征的视频特征表示的研究，同时保留帧之间的时间和空间相关性，（2）开发底层操作系统和索引结构，以支持多核和分布式环境中多媒体数据的并行计算，（3）使用半监督图形模型，通过利用标记数据和未标记数据之间的联合分布来淡化最先进的深度学习模型对大规模高质量训练集的依赖性;（4）使用系统方法反复优化单个组件和系统集成，这将最终导致局部和全局优化。我的研究小组自2013年以来一直致力于多媒体大数据，我们在建立测试平台以测量各种视频特征组合的性能方面取得了很大进展，开发了一种基于SPARK流的流水线架构来支持并行多媒体内容计算，并在单个多核服务器上实现了多媒体数据存储和索引的原型。我们将继续采用多种面向系统的策略来优化系统性能，并在GPU（图形处理单元）服务器和分布式环境中扩展系统。同时，我们在过去几年中开发的测试平台、原型系统以及模型和理论将作为未来设计、实现和验证所提出研究的基础和工具。在多媒体大数据研究的前沿，所提出的研究计划还将为HQP提供行业相关的最新培训，帮助他们掌握技能和知识，在加拿大快速发展的大数据行业中发挥作用。