Accelerated Real-Time Information Extraction System (ARIES)

CMOS半导体技术的技术进步为数字革命铺平了道路。正如摩尔预测的那样，在过去的40年里，硅集成能力每18个月就会翻一番，为低成本计算和存储技术提供了基础。信息和通信技术的数字化引发了一系列创新，彻底改变了我们的计算和通信方式。通过WiFi和3G/4G技术实现的无处不在的高带宽通信，促进了对大量应用和位置特定信息的按需访问，包括多媒体和广播内容、视频和语音通信、电子邮件和短信/彩信。此外，它还实现了对个性化存储和计算资源的按需访问，为开发云计算基础设施和各种基于网络的在线服务和应用程序奠定了基础。随着通信和存储成本的降低，互联网也成为各种自主传感器技术的全球通信基础设施，被称为物联网。重点应用领域包括监测/监控、智能电网、智能家居和智能城市。监控互联网流量并从在线流量和存储的信息中挖掘有意义的信息已经成为许多关键应用和服务的关键。例如，资源管理、市场情报、物理和网络犯罪调查和取证、网络空间治安、了解情况以及监测出于犯罪和恐怖主义意图的恶意行为。随着当前和新兴数据资产的规模、多样性和分布式性质的增加，以及随着数据变得越来越普遍和对决策至关重要，有效的实时挖掘有用信息变得至关重要。考虑到互联网流量和存储数据的指数级增长，传统的基于软件的方法已经变得不足和不可持续。根据摩尔定律获得的性能提升跟不上当前和近期生成的数据资产所需的计算带宽。互联网流量带宽每12个月翻一番，而新兴的内容多样性显著增加了挖掘的复杂性。随着企业变得更加以数据为中心，公共云和私有云中的数据资产显著增加，传统的通过增加计算资源数量的扩展由于成本和功率消耗而无法持续。大多数数据挖掘算法是由软件社区派生的，并针对基于冯-诺伊曼体系结构的平台的数据结构进行了优化。现在，一个有效的解决方案需要在我们处理数据的方式以及如何从大量部分存储或传输中不断变化的分布式数据中提取有意义的信息方面进行范式转换。

Technological advances in CMOS semiconductor technology paved the way for the digital revolution. As predicted by Moore, silicon integration capability has been doubling every 18 months over the past four decades, providing the foundation for low-cost computing and memory technology.The digitisation of information and communication technologies sparked a number of innovations revolutionising the way we compute and communicate. Ubiquitous high-bandwidth communication, enabled by WiFi and 3G/4G technologies, facilitates on-demand access to a vast amount of application and location specific information including multimedia and broadcast content, video and voice communications, email and SMS/MMS. Furthermore, it has enabled on-demand access to personalised storage and computing resources, providing the foundation for the development of cloud computing infrastructures and a wide range of online web-based services and applications. With the decreasing cost of communication and storage the Internet has also become the global communication infrastructure for a wide range of autonomous sensor technologies, referred to as the "Internet of things". Key application areas include monitoring/surveillance, smart grid, smart homes and smart cities. Monitoring internet traffic and mining meaningful information from both the online traffic and the stored information has emerged as essential for many critical applications and services. For example resource management, market intelligence, physical and cybercrime investigations and forensics, cyber space policing, situation awareness and the monitoring of malicious behaviour for criminal and terrorist intent. As the scale, diversity and distributed nature of current and emerging data assets increases and as data becomes ever more ubiquitous and critical to decision making, effective real-time mining of useful information becomes essential. Considering the exponential increase of internet traffic and stored data, traditional software based approaches have become inadequate and unsustainable. Performance gain achieved due to Moore's law does not keep up with the required computing bandwidth of current and near future generated data assets. Internet traffic bandwidth is doubling every 12 months while the emerging content diversity is significantly increasing mining complexity. As the enterprise becomes more data centric, with a significant increase in data assets within the public and private cloud, traditional scaling by increasing the number of computing resources can no longer be sustained due to cost and power dissipation.Most data mining algorithms are derived by the software community and are optimised for data structures for platforms based upon the Von-Neumann architecture. An effective solution now requires a paradigm shift in the way we process data and also how we extract meaningful information from a large amount of distributed, constantly changing data that is partially stored or in-transit.