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1 技术解析 : 大数据巨大的机遇 Fayé A Briggs, PhD Intel Fellow and Chief Server Platform Architect Data Center and Connected Systems Group Intel Architecture Group

2 议程大数据现象及其状况定义与传统数据的区别 4 要素传感数据的主要来源智能城市大数据的特征它对数据中心有何意义分析智能框架基于 IA 的技术堆栈与标准化分布式分析, 协作型智能所有权, 互操作性, 隐私与安全 : 端到端解决方案分布式以数据为中心的架构, 支持内嵌式连续性总结 2

大数据现象 1.8ZB 2011 年 2 天 > 文明起源至 2003 年 7.

10 年个人位置数据 3 千亿美元每年美国医疗从大数据中的节省 $32+B 2010 年以来 4

3 大数据现象 1.8ZB 2011 年 2 天 > 文明起源至 2003 年 7.5 亿两天内上传到 Facebook 的照片数 966PB 美国制造业存储 (2009) 2090 亿 2021 年 RFID 标签销售 : 从 2011 年的 1200 万 200+TB 麻省理工学院媒体实验室一位极客青年的 24 万个小时 200PB 中国一个智能城市项目的存储 8 千亿美元 10 年个人位置数据 3 千亿美元每年美国医疗从大数据中的节省 $32+B 2010 年以来 4 大公司收购数据正在成为商业的新型原材料 : 经济投入几乎相当于资本与劳动力经济学家,2010 年信息将成为 21 世纪的石油 Gartner 公司,2010 年 3

4 什么是大数据? 非结构化数据对象规模超出体量, 多样性, 数值与速度普通数据库软件工具采集, 存储, 管理与分析的能力 * 大传感数据非结构化数据分析体量大公司数据管理大网络数据结构化数据存储公司数据时间采集 4 * 大数据 : 创新竞争与生产效率的下一前沿领域, 麦肯锡全球研究所

5 大数据的 4 大要素及相关挑战体量多样性非结构化数据的超大规模与增长总数据的 80%~90% 比结构化 ( 关系 ) 数据的增长速度快 10 倍到 50 倍是传统数据仓库的 10 倍到 100 倍大数据的异构与多样性本质很多不同形式 ( 文本, 文档, 图片, 视频, ) 无模式或模式不明显不连贯的语法和语义数值对未来趋势与模式的可预测分析深度复杂分析 ( 机器学习, 统计建模, 图形算法, ) VS 传统商务智能 ( 咨询, 报告, ) 速度实时分析而不是批量式分析数据输入处理, 与丢弃立竿见影而非事后见效 5

6 大数据不同于传统数据新工作负载 / 方法以设计新平台处理数据管理分析实时分析分布式分析处理速度描述式分析关系数据库 (SQL) 纵向扩展平台预测性 / 规定性分析 NoSQL 和 NewSQL 分布式层级式批量式分析数据仓库横向扩展集群平台灵活模式 1 倍 10 倍传统数据大数据 100 倍体量 6

7 传感数据的主要来源物联网 (IoT) 与智能城市物联网 (IoT) 是传感数据主要来源大多数物联网应用和智能城市相关, 由政府资助智能 ( 处理 ) 互联 ( 通信 ) 智能 ( 控制 ) 仪表 ( 传感 ) 智能农业公共安全智能交通智能电网环境保护智能物流电子医疗智能家庭工业自动化 7 资料来源 : 长城战略咨询

8 8 物联网

9 E2E 大数据架构框架 MIC 可视化与解释横向扩展横向与纵向扩展 EX EP 微型服务器 EN 核心数据采集流分析本地分析存储 [ 非 ] 结构化数据数据采集复杂事件处理分析处理预处理 / 清理 / 过滤 / 聚合批分析视频分析核心 SOC MinuteIA 传感器摄像机 9

10 智能城市大数据的特征不能向边缘传输实时情报信息在网络移动 PB 量级数据成本昂贵多数据流间的内联关系所有权, 互操作性, 安全, 隐私问题供水交通系统智能网格时空 : 本地数据, 本地消费者公共安全数据杂乱无章, 需要处理超大规模体量, 连续流动该特性提出了深层次技术要求 10

11 分布式以数据为中心的架构, 支持内嵌式连续性中心云服务交付分布式架构构成了内嵌式连续性移动元数据贴近服务边缘云分布式数据管理需要一个定量函数以决定数据与计算的最佳分布状态移动计算贴近大数据智能边缘设备与传感器协作型智能通过分布式分析创建, 并由前面的函数引导实时控制数据所有权, 互操作性, 隐私与安全需要端到端解决方案和标准, 覆盖整个数据生命周期 11

12 分析智能框架代表与服务分布式云与数据分析网络互联网, 2G, 3G, 4G, 局域网, 无线局域网, 前端智能传感与驱动 12

13 协作型智能示例分析 API 可视化轨迹 EP 分析 API 云端解码, 目标分类识别与建模 MIC EX 分析 API 低级别特性, 设备端解码原始视频计算基于函数的分析分布, 并端到端映射到内嵌式连续性 13

14 互操作性, 隐私与安全端到端解决方案数据流和使用的跟踪能力透明性以及数据使用和附加值的定价数据过滤和隐匿 (obscurity) 标准化数据模式数据分散与遗忘数据标签识别所有权, 环境, 等等始于采集的保护数据 API 中间件标准平台 14

15 基于 IA 的技术堆栈与标准化 API 安全访问可跟踪性透明性与定价中间件数据隐匿 (Obscurity) 数据过滤数据遗忘数据分散标准数据标签与模式数据设备架构平台服务就绪数据仓库数据标签加速标签处理 15

16 总结 1 大数据来了且快速增长 2 英特尔在软件堆栈与平台层面做好了充分准备 3 英特尔致力于投资新技术以解决未来更加苛刻的大数据需求 16

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untitled Safe Harbor This presentation contains statements of a forward-looking nature. These statements are made under the safe harbor provisions of the U.S. Private Securities Litigation Reform Act of 1995. You