Big Data Solutions for Healthcare
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This document discusses big data solutions for healthcare. It outlines trends driving huge increases in healthcare data from sources like medical imaging, patient monitoring, and genomics. This data holds value for personalized medicine, clinical decision support, and fraud detection. However, managing such varied and voluminous data presents challenges around volume, variety, and velocity. The document proposes methods for managing big data through distributed storage, optimization, security, and specialized platforms. Use cases are highlighted for connecting new analytics to healthcare applications and services.
Big Data Solutions for Healthcare
- 1. Big Data Solutions for Healthcare Wayne Wu, Global Health Solution Architect, Intel Hubert Ding, Healthcare Solution Architect, Intel BIGS001
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- 3. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps The PDF for this Session presentation is available from our Technical Session Catalog at the end of the day at: intel.com/go/idfsessionsBJ URL is on top of Session Agenda Pages in Pocket Guide 3
- 4. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 4
- 5. We are at an Inflection Point in Healthcare - TRENDS % of population over age 60 30+ % 25-29% 20-24% 2050 10-19% 0-9% WW Average Age 60+: 21% Source: United Nations “Population Aging 2002” Healthcare costs are Global AGING U.S. Healthcare BIG DATA RISING Average age 60+: Value Significant % of GDP growing from 10% to $300 Billion in value/year 21% by 2050 ~ 0.7% annual productivity growth Source: McKinsey Global Institute Analysis ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast 5
- 6. We are at an Inflection Point in Healthcare - TRENDS Storage Growth Total Data Healthcare Providers (PB) 15000 Admin Imaging Medical Imaging Archive Projection 10000 Case from just 1 healthcare system EMR Email 5000 File Non Clin Img 0 Research 2010 2011 2012 2013 2014 2015 Data Explosion projected to reach 35 Zetabytes by 2020, with a 44-fold increase from 2009 Source: McKinsey Global Institute Analysis ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast 6
- 7. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 7
- 8. Big Data in Healthcare Where is the data coming from? 2. Clinical Decision Support & 1. Pharma/Life Sciences Trends (includes Diagnostic Imaging) 4. Patient Behavior/Social 3. Claims, Utilization and Fraud Networking How do we create value? (examples) 1. Personalized Medicine 2. Clinical Decision Support 4. Analytics for Lifestyle and 3. Enhanced Fraud Detection Behavior-induced Diseases McKinsey Global Institute Analysis 8
- 9. Big Data Solution for Healthcare Health Info Personal Health Primary Care Aging Society Services Management New Healthcare Clinical Decision Personalized Applications Cancer Genomics Support Medicine Analytics and Medical Imaging SQL-like Query Machine Learning Visualization Analytics Data Processing/ Medical Records Genome Data Medical Images Management Distributed Storage Security and Imaging Platform Optimization Privacy Acceleration 9
- 10. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 10
- 11. Big Data Challenges are More than Data Size... And Require New Technologies Lab results, billing data, images, sensors data from Volume devices, genomics • Structured data in standard formats like HL7 Variety • Unstructured data from dictations, transcription, photos, images Analyzing data from existing databases for claims, Value patient history, archived images, real-time data analytics for clinical decision support • Realtime rather than batch-style analysis Velocity • Data streamed in, tortured, and discarded • Making impact on the spot rather than after-the-fact Traditional business solutions connecting to new data and analytics models for real-time value opportunities 11
- 12. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 12
- 13. All Eyes on Data for Value Big Data Storage Considerations Traditional Solution Traditional Storage Approaches Environments Large Analytics – Hadoop* ERP, CRM, Batch, Large DB – Hive* OLTP-DB Large Backup – Lustre* Edge Servers Analytical Synchronization End-to-End Machine-to-Machine Source-to-Source Data Source Text-Voice-Video-Sensor Requesting Or M2M Communication Batch – Business Applications Rich Visualization – Secure Data Analysis and Caching 13
- 14. All Eyes on Data for Value Data Center Provisioning Discrete Virtual Cloud – As A Service Big Data Storage Considerations HPC Traditional Solution Traditional Storage Approaches Environments Large Analytics – Hadoop* ERP, CRM, Batch, Large DB – Hive* OLTP-DB Large Backup – Lustre* Edge Servers Analytical Synchronization End-to-End Machine-to-Machine Source-to-Source Operational Solution Stack Example Applications & Services Data Source Text-Voice-Video-Sensor Visualization – File Structure & Analytical Requesting Or M2M Tools Communication Data Delivery, Operational & Graphical Batch – Business Applications Analytics Data Management & Computational Analytics Rich Visualization – Secure Data Analysis and Caching Compute – Storage & Infrastructure Platforms 14
- 15. Enterprise Big Data Architecture STRUCTURED ENTERPRISE DATA PLATFORMS DATA PLATFORMS TOOLS Legacy Node Node Node ODS & Data Marts Data Mining Hadoop* Dev IDE Logs Create Map CONSUME REDUCE IMPORT Enterprise Data Warehouse UNSTRUCTURED Visualize Social & Web No-SQL Spreadsheets INSIGHTS Legacy APPS Document In Memory DB Types SQL Web Transcriptions & Apps Notes RDBMS MashUps 15
- 16. Big Data Architectural Framework Data Provisioning Models-Storage & Connectivity Considerations Velocity NAS - SAS and Databases 10GBe MPP Databases Data Distributed DBMS / NoSQL Fast Fabric DW Appliances Vulnerability Storage Human Data Sources Security Genome Surveillance and Text, Video GIS Services Diagnostic Medical Social Medical & Drug Images Medical Device Log and Audio Privacy Devices Streaming Data Media Records Discovery Files Compliance Provisioning Models Can Vary by Data Characteristics 16
- 17. Big Data Architectural Framework Data as a Services Distributed Virtual Persistence HPC / TCP Vertically Event, Message Real-Time, Cached, Federated EDW, Marts MIC Integrated Data Software Characteristics Intel Data ingestion, Integration and Processing Services AIM Data Volume Suite and Distributed High Performance Integration Data Processing Quality Tools Hadoop* MapReduce Cloud Data Provisioning Models-Storage & Connectivity Considerations Velocity NAS - SAS and Databases 10GBe MPP Databases Data Distributed DBMS / NoSQL Fast Fabric DW Appliances Vulnerability Storage Human Data Sources Security Genome Surveillance and Text, Video GIS Services Diagnostic Medical Social Medical & Drug Images Medical Device Log and Audio Privacy Devices Streaming Data Media Records Discovery Files Compliance Provisioning Models Can Vary by Data Characteristics 17
- 18. Big Data Architectural Framework Data Access Data User Visibility Authentication NLP/Semantic Custom Analytic Solutions BI & Predictive Analytics Search/ Integrated Machine MapReduce Textual Analytics Analytics with Existing BI/Analytics Learning Hadoop with in-database Knowledge Streaming Analytics Support data processing support Management Data as a Services Distributed Virtual Persistence HPC / TCP Vertically Event, Message Real-Time, Cached, Federated EDW, Marts MIC Integrated Data Software Characteristics Intel Data ingestion, Integration and Processing Services AIM Data Suite Volume Distributed High Performance Integration Data Processing and Tools Quality Hadoop* MapReduce Cloud Data Provisioning Models-Storage & Connectivity Considerations Velocity NAS - SAS and Databases 10GBe MPP Databases Data Distributed DBMS / NoSQL Fast Fabric DW Appliances Vulnerability Storage Human Data Sources Security Genome Surveillance and Text, Video GIS Services Diagnostic Medical Social Medical & Drug Images Medical Device Log and Audio Privacy Devices Streaming Data Media Records Discovery Files Compliance Provisioning Models Can Vary by Data Characteristics 18
- 19. Accessing Big Data (Clients) “Know Me” “Free Me” “Express Me” “Link Me” Mobile Laptops, Smart Clinical Tablet Ultrabook™ Fixed Digital Phone Assistant PCs Devices PCs Signage Kiosk Mobility Vital sign, I & O entry Medication administration Template data entry Free-format text data entry Large diagnostic images Data inquiry Manageability 19
- 20. Building on the Ecosystem Database and Analytics Environments Optimized on Intel Life Sciences Database and compute infrastructure Analytics engines Workloads & Solutions Relational VOLTDB EXALYTICS Nonrelational Open Source: BLAST, FASTA, ClustalW, HMMER, Darwin, etc. No Matter the Choice: All optimized on Intel® Xeon® processor based hardware 20
- 21. Intel® Products and Software For Big Data Scaling Compute Flexible Workloads & Intel® Xeon® processor E5- and E7 based servers up to Analysis Technical Compute 80% performance boost with Optimized Intel Xeon processor E5- hardware-enhanced security Data Delivery & based servers for TCP Intel® Xeon Phi™ co- Storage Management processor Intelligent scale-out storage Software Ecosystem Integrated Systems built with Intel Xeon Interconnect Embedded Analysis Solutions processor E5-based storage From Intel ISG Efficiency Robust & Secure Interconnect Visibly Mobile Intel Software EcoSystem Fast Fabric & Caching Performance Client Hadoop* Investing in new fabric Lustre* approaches Rich Visualization In-memory non-volatile memory that Seamless Access In stream data analysis provide capacity caching for End to end security data velocity Performance Client Network Rich modeling support Intelligent scale-out Client – server application networking management from 10GBe – 40GBe 21
- 22. Examples of Intel-powered Servers in Big Data and Analytics Cisco* UCS Server1 Dell* PowerEdge* C Series2 Oracle* Sun Fire* server3 Intel® Xeon® Intel Xeon processor Intel Xeon processor E7- processor 5600 5500/5600 4800 Cisco UCS server with EMC The Dell | Cloudera* solution Oracle Exalytics* In-Memory Greenplum MR software - for Apache* Hadoop combines Machine, features the Oracle “enterprise-class” BI Foundation Suite and Hadoop* distribution that Oracle TimesTen In-Memory features technology from Database for Exalytics MapR Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. Baseline score of 271 published by Itautec on the Servidor Itautec MX203* and Servidor Itautec MX223* platforms based on the prior generation Intel® Xeon® processor X5690. New score of 492 submitted for publication by Dell on the PowerEdge T620 platform and Fujitsu on the PRIMERGY RX300 S7* platform based on the Intel® Xeon® processor E5-2690. For additional details, please visit www.spec.org. Intel does not control or audit the design or implementation of third party benchmark data or Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase. 1 http://gigaom.com/cloud/ciscos-servers-now-tuned-for-hadoop/ 2 http://www.businesswire.com/news/home/20110804005376/en/Dell-Cloudera-Collaborate-Enable-Large-Scale-Data 3 http://www.itp.net/mobile/588145-oracle-unveils-exalytics-in-memory-machine 22
- 23. Big Data Applications in Healthcare (PRC) 2. Clinical •药品研发 Decision •临床数据比对 对药品实际 作用进行分析；实 1. Pharma/Life Support & 匹配同类型的病人，用药 施药品市场预测 Sciences Trends •临床决策支持 •基因测序 利用规则和数据实时分析给 •分布式计算加快基因测序计算 (includes 出智能提示 效率 Diagnostic Imaging) •远程监控 •公共卫生实时统计分析 采集并分析病人随身携带仪 发现公共卫生疫情及公民健康 4. Patient 器数据，给出智能建议 状况 3. Claims, Behavior/ •人口统计学分析 •新农合基金数据分析 Utilization & Social 对不同群体人群的就医，健 及时了解基金状况，预测风险 Fraud 康数据实施人口统计分析 辅助制定农合基金的起付线， Networking 赔付病种等 •了解病人就诊行为 发现病人的特定就诊行为， •基本药物临床应用分析 分配医疗资源 分析基本药物在处方中的比例 23
- 24. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 24
- 25. Use Case: Regional Health Info Network – China Real-time Clinical Decision Support Data Analytic Care Coordination • Real-time and recursive information R&D … Clinical decision support … processing of health data (EHR, medical images) to support care RHIN coordination, clinical decision Ancillary Health EHR Registries support, and public health Data & Services Information DW Data & Services Data & Services management • Enabling health data analytic with Longitudinal Record Services Hadoop* (HBase*/Hive*) • Potential to scale cross geos and Health Information Access Layer across sectors/segments Primary care Public • Involving local ISVs, local OEMs Hospital (Grassroots) Health • Technical Challenges – Transforming the relational DB to Hadoop (HBase/Hive) – Addressing the usages of big data analytics in RHIN 25
- 26. RHIN/Grassroots Solution with Big Data (Hadoop*) Integrated User Interface（Citizen, Physician, Health Authority） Cloud -based Regional Healthcare Service Distributed Data Service System System Multi-Tenancy Application Presentation (Report, Viewer) Pubic Medical Health Service New Rural Data Mining （Mahout*） Language & Compiler (Hive*) Medical Distributed Batch Processing Real-time Insurance Framework Database Operation Drug Mgt. (MapReduce) (HBase*) Mgt. Service Distributed File System Coordination Service (HDFS) (ZooKeeper*) Infrastructure Virtualization Structured Data Collector Log Date Collector (Sqoop*) (Flume*) Network Storage Server Virtualization Virtualization Virtualization EHR data Repository Health Information Access Layer (HIAL) Grassroots Hospital Hospital Care 26 Institution
- 27. Use Case: NEXTBIO Analytics for Genomics Data • Cost to sequence a genome has fallen by 800x in the last 4 years • Each genome has ~4 million variants • Growth in the genomics data in the public and private domain • Data available in variety of sources – Structured, semi-structured, unstructured • New aggregated data growing exponentially Data Interpretation & Commercializing Sequencing Processing Analytics Targeted Cloud Storage Therapeutics Visualization Companion 3 Billion Base Pairs Millions of Millions of Variants Diagnostics Variants Millions of Patients Actionable Biomarkers 27
- 28. Use Case: NEXTBIO Patient Correlation Data Novel Discoveries Biomarkers Disease Mechanism Drug Indications Clinical Trial Parameters Patient Care Options Large content repository of public and private genomic data combined with proprietary and patented correlation engine 28
- 29. Use Case: NEXTBIO Nextbio & Intel Collaboration Technical Challenge: • Immutable Data – write once, never change, read many times • Traditional Bloom Filters works • Hadoop* & HBase* well suited 1 genome 10 million rows 100 genomes 1billion rows 1M genomes 10 trillion rows 100M genomes 1 quadrillion 1,000,000,000,000,000 rows • App can dynamically partitions HBase as data size grows Intel Optimizations for Hadoop: • Optimized Hadoop stack in open source • Stabilize HBase to provide reliable scalable deployment • Optimize and support scale-out as data size dramatically grows • Exploring cluster auto tuning, Security & Compliance, etc. 29
- 30. Use Case: Big Data at Kaiser Permanente 30
- 31. Data Trends World’s Data Kaiser’s Data 90% 80% STRUCTURED UNSTRUCTURED STRUCTURED UNSTRUCTURED UNSTRUCTURED DATA DATA DATA DATA DATA • 80% of world’s data is unstructured • 90% of Kaiser’s data is (Rise of Mobility devices, and machine unstructured (80% of EHR and Image generated data) data) • 44x as much data over the coming • 25x as much data over the coming decade (35 zettabytes by 2020) decade (One exabyte by 2020) • Majority of data growth is driven by • Majority of data growth is driven by unstructured data (Active archives, unstructured data (Medical Images, Medical images, Online movies and Videos, Text, Voice) storage, Pictures) • Information is centric to providing • Information is centric to new wave Real-time Personalized Healthcare of opportunities (Retail, Financing, (Requires Contextual – device, Insurance, Manufacturing, Healthcare,…) environment, spatial, Demographics, Social and Behavioral profiles in addition to medical information) • Industry is employing Big Data Technologies for Information • Kaiser is evaluating Big Data extraction Technologies… 31 Source: Kaiser
- 32. Data Platform Compute Trends – Distributed Compute Kaiser is looking to exploit this capability… • Structured, Relational • Unstructured, Non-tabular Tabular Data Data • Interactive Query Support • Rich Ad Hoc Integration Slave(s) • Real-time Analytics • Real-time Analytics • SQL Transaction Data • UQL ALL Data Master DAS Share-Nothing Distributed Storage and SAN/NAS Compute ($) • Fault-tolerant MasterSlave Architecture In-Memory capable of withstanding partial system failures (50$) SAN/NAS • Data is distributed across processing slave nodes MPP (10$) • Resources containing data are not shared • Master manages the data distribution, job scheduling across slave nodes and aggregating SAN/NAS result sets SMP (Disk Caching, • Integrate built/bought Real-time Predictive SAN/NAS High Speed Network) Analytical Solutions or Processing logic (10$) SMP (5$) Discontinuous Change 32
- 33. Big Data Platform – Requirements Data Process Characteristics Characteristics Information drives process optimizations with strategic impact. Modeling business intuition Intuition from data deluge. (Simulation, Volume Optimization, Stochastic Optimization) (Sensors, EMR, Ability to model information and transition from Claims, Pharmacy, Images) multiple access methods to generating, sharing, Information collaborating and acting on insights anytime, (Standard & Ad Hoc anywhere on any device. reporting, Query, Alerts, Forecasting, Access) Velocity Support current BI tools focused on structured (SLAs, Real-time information. Build/buy packaged unstructured Interrogation Decision Support & data processing and analytics tools. (Clustering, Statistical, Contextual Quality, Semantics) Intelligence) A portfolio of tools to manage (profile, cleanse, classify, synchronize, aggregate, Integration (Alignment, Semantics, integrate, share) ALL types of data. Completeness, Quality) Variety (Structured, Text, Unstructured, A unified information storage methodology Ingestion Documents, Images) enabling users to manage data from ALL sources. (Data Model, Metadata Reference Data, Store) 33
- 34. Big Data – Selection Criteria DATA SIZE Gigabytes, Terabytes, Petabytes DATA TYPE Structured, Semi-Structured, Unstructured DATA CLASS Human Generated, Machine Generated DATA CATALOG Text, Image, Audio, Video DATA VELOCITY Batch, Streaming DATA ACCESS Analytics, Search, Transaction (ACID, BASE) DATABASE TYPE Relational , File Based, Columnar, NoSQL, Document, Graph, RDF SERVER ARCHITECTURE SMP, MMP, Distributed Processing DISTRIBUTED PROCESSING Appliance, Commodity Cluster (CC) < 1K nodes, CC >1K nodes STORAGE ARCHITECTURE NAS, SAN, Direct Access Storage, Spinning Disks, Flash, SSD Financial, Computer Vision Engine, Geospatial, Machine Learning, FRAMEWORKS Mathematical, Natural Language Processing, Neural Networks, Statistical Modeling, Time-Series Analysis, Voice Engine ANALYTICS Standard Reporting, Ad hoc Reporting, Query/Drill downs, Alerts Forecasting, Simulations, Optimization, Stochastic Optimizations 34
- 35. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 35
- 36. Summary • We are at an inflection point in Big Data and analytics in healthcare • We need to make Big Data efficient and accessible • Focus on innovation, rely on the ecosystem for services outside your core competency • Adopt standards and best practices leveraging worldwide models 36
- 37. Next Steps Help build the Big Data Health Continuum: • Create technology-differentiated offerings, advocating open standards and best practices • Identify potential customers and ecosystem partners in core healthcare usage models • Deliver industry proof points to accelerate adoption • Develop joint marketing programs to raise awareness, amplify our thought leadership and generate customer value Together, We Create the Network Effect 37 37
- 38. Additional Sources of Information • Big Data and Analytics at Intel - Intel® Big Data and Analytics • Healthcare Blogs – Intel Healthcare IT Professionals • Whitepapers – The Growing Importance of Big Data, Real Time Analytics – SAP In-Memory Appliance Software: Real-Time Business Intelligence – Oracle: Big Data for Enterprise – Big Data: The next frontier for innovation, competition, and productivity • Videos – SAP-HANA – A Collaboration Between SAP & Intel 38
- 39. Intel Technologies • Intel® Virtualization Technology (Intel® VT) – Provides flexibility and maximum system utilization by consolidating multiple environments into a single server, workstation, or PC • Intel® vPro™ Technology – Designed specifically for the needs of business, notebooks and desktops with Intel® vPro™ technology have security and manageability built right into the chip • Intel® Trusted Execution Technology (Intel® TXT) – Protect confidentiality and integrity of business data against software-based attacks. • Intel® Anti-Theft Technology (Intel® AT) – Providing the option to activate hardware-based client- side intelligence to secure the PC and its data in the event the notebook is lost or stolen • Intel® AES New Instructions (Intel® AES-NI) – The Advanced Encryption Standard (AES) algorithm is now widely used across the software ecosystem to protect network traffic, personal data, and corporate IT infrastructures • Intel® Identity Protection Technology (Intel® IPT) – Two-factor authentication directly into the processors of select 2nd generation Intel® Core™ processor-based PCs • Intel® Cloud Access 360 – Protection Enterprise Access to Cloud and Protecting Enterprise Applications in the Cloud • Intel® Expressway Service Gateway – High performance security, xml acceleration and routing. Cross-domain service mediation, threat prevention, policy enforcement. Interoperable ESB gateway • McAfee Cloud Security Platform* – Consistent security policies, reporting, and threat intelligence across all cloud traffic—now available from a single platform • Intel® Scale-out Storage – Tackle your data center’s challenges with enterprise storage solutions powered by the world’s most advanced multi-core architecture • Intel® Solid State Drives – High performance, Self-Encrypting Solid State Drives for protecting sensitive data at rest • Intel Unified Networking – Unified Networking enables cost-effective connectivity to the LAN and the SAN on the same Ethernet fabric 39
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- 41. Legal Disclaimer • Intel® vPro™ Technology is sophisticated and requires setup and activation. Availability of features and results will depend upon the setup and configuration of your hardware, software and IT environment. To learn more visit: http://www.intel.com/technology/vpro. • Ultrabook Touch/Convertibility: Ultrabook™ products are offered in multiple models. Some models may not be available in your market. Consult your Ultrabook™ manufacturer. For more information and details, visit http://www.intel.com/ultrabook . • Intel® Virtualization Technology (Intel® VT) requires a computer system with an enabled Intel® processor, BIOS, and virtual machine monitor (VMM). Functionality, performance or other benefits will vary depending on hardware and software configurations. Software applications may not be compatible with all operating systems. Consult your PC manufacturer. For more information, visit http://www.intel.com/go/virtualization. • Intel® AES-NI requires a computer system with an AES-NI enabled processor, as well as non-Intel software to execute the instructions in the correct sequence. AES-NI is available on select Intel® processors. For availability, consult your reseller or system manufacturer. For more information, see Intel® Advanced Encryption Standard Instructions (AES-NI) • Intel® Active Management Technology (Intel® AMT) requires activation and a system with a corporate network connection, an Intel® AMT-enabled chipset, network hardware and software. For notebooks, Intel AMT may be unavailable or limited over a host OS-based VPN, when connecting wirelessly, on battery power, sleeping, hibernating or powered off. Results dependent upon hardware, setup and configuration. For more information, visit Intel® Active Management Technology. • Intel® Anti-Theft Technology (Intel® AT): No system can provide absolute security under all conditions. Requires an enabled chipset, BIOS, firmware and software, and a subscription with a capable Service Provider. Consult your system manufacturer and Service Provider for availability and functionality. Intel assumes no liability for lost or stolen data and/or systems or any other damages resulting thereof. For more information, visit http://www.intel.com/go/anti-theft. • Intel® Trusted Execution Technology (Intel® TXT): No computer system can provide absolute security under all conditions. Intel® TXT requires a computer with Intel® Virtualization Technology, an Intel TXT enabled processor, chipset, BIOS, Authenticated Code Modules and an Intel TXT compatible measured launched environment (MLE). Intel TXT also requires the system to contain a TPM v1.s. For more information, visit http://www.intel.com/technology/security. • Intel® Identity Protection Technology (Intel® IPT): No system can provide absolute security under all conditions. Requires an Intel® Identity Protection Technology-enabled system, including a 2nd Generation Intel® Core™ processor enabled chipset, firmware and software, and participating website. Consult your system manufacturer. Intel assumes no liability for lost or stolen data and/or systems or any resulting damages. For more information, visit http://ipt.intel.com. 41
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