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Transcript of Big Data Solutions for Healthcare
Big Data Solutions for Healthcare
BIGS001
Wayne Wu, Global Health Solution Architect, Intel Hubert Ding, Healthcare Solution Architect, Intel
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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
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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
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We are at an Inflection Point in Healthcare - TRENDS
Source: McKinsey Global Institute Analysis ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast
Healthcare costs are RISING
Significant % of GDP
Source: United Nations “Population Aging 2002”
25-29%
30+ %
20-24%
10-19%
0-9%
% of population over age 60
2050 WW Average Age 60+: 21%
Global AGING Average age 60+:
growing from 10% to 21% by 2050
U.S. Healthcare BIG DATA Value
$300 Billion in value/year ~ 0.7% annual productivity
growth
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We are at an Inflection Point in Healthcare - TRENDS
Source: McKinsey Global Institute Analysis ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast
Storage Growth
0
5000
10000
15000
2010 2011 2012 2013 2014 2015
Total Data Healthcare Providers (PB)
Admin
Imaging
EMR
File
Non Clin Img
Research
Medical Imaging Archive Projection Case from just 1 healthcare system
Data Explosion projected to reach 35 Zetabytes by 2020, with a 44-fold increase from 2009
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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
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Big Data in Healthcare
1. Pharma/Life Sciences
3. Claims, Utilization and Fraud
2. Clinical Decision Support & Trends (includes Diagnostic
Imaging)
4. Patient Behavior/Social Networking
Where is the data coming from?
How do we create value? (examples)
1. Personalized Medicine
3. Enhanced Fraud Detection
2. Clinical Decision Support
4. Analytics for Lifestyle and Behavior-induced Diseases
McKinsey Global Institute Analysis
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Big Data Solution for Healthcare
Distributed Platform
Storage Optimization
Security and Privacy
Imaging Acceleration
New Healthcare Applications
Personalized Medicine
Clinical Decision Support Cancer Genomics
Health Info Services Primary Care Personal Health
Management Aging Society
Analytics and Visualization SQL-like Query Medical Imaging
Analytics Machine Learning
Data Processing/ Management Medical Images Medical Records Genome Data
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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
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Big Data Challenges are More than Data Size... And Require New Technologies
Lab results, billing data, images, sensors data from devices, genomics Volume
• Structured data in standard formats like HL7 • Unstructured data from dictations, transcription,
photos, images Variety
Traditional business solutions connecting to new data and analytics models for real-time value opportunities
Analyzing data from existing databases for claims, patient history, archived images, real-time data analytics for clinical decision support
Value
• Realtime rather than batch-style analysis • Data streamed in, tortured, and discarded • Making impact on the spot rather than after-the-fact
Velocity
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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
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All Eyes on Data for Value
Data Source Text-Voice-Video-Sensor
Requesting Or M2M Communication
Batch – Business Applications
Traditional Solution Environments
ERP, CRM, Batch, OLTP-DB
Edge Servers
Big Data Storage Considerations Traditional Storage Approaches
Large Analytics – Hadoop*
Large DB – Hive*
Large Backup – Lustre*
Rich Visualization – Secure Data Analysis and Caching
Analytical Synchronization
End-to-End Machine-to-Machine Source-to-Source
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All Eyes on Data for Value
Data Source Text-Voice-Video-Sensor
Requesting Or M2M Communication
Batch – Business Applications
Traditional Solution Environments
ERP, CRM, Batch, OLTP-DB
Edge Servers
Data Center Provisioning Discrete Virtual
Cloud – As A Service HPC Big Data Storage Considerations
Traditional Storage Approaches Large Analytics – Hadoop*
Large DB – Hive*
Large Backup – Lustre*
Rich Visualization – Secure Data Analysis and Caching
Analytical Synchronization
End-to-End Machine-to-Machine Source-to-Source
Operational Solution Stack Example
Applications & Services
Visualization – File Structure & Analytical Tools
Data Delivery, Operational & Graphical Analytics
Data Management & Computational Analytics
Compute – Storage & Infrastructure Platforms
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Enterprise Big Data Architecture
Enterprise Data Warehouse
Spreadsheets
Visualize
Data Mining Dev IDE
ODS & Data Marts
ENTERPRISE TOOLS
Legacy Document
Types
Logs
Social & Web
Legacy
STRUCTURED
UNSTRUCTURED
Transcriptions & Notes
DATA PLATFORMS
RDBMS
No-SQL
In Memory DB
SQL
APPS
Node Node Node
Hadoop*
DATA PLATFORMS
Web Apps
MashUps
IMPORT
INSIGHTS
CONSUME
Create Map
REDUCE
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Big Data Architectural Framework
Data Sources GIS Diagnostic
Images
Human Genome & Drug Discovery
Medical Devices
Surveillance and Medical Device Streaming Data
Medical Records
Data Velocity
Security Services Privacy
Compliance
Social Media Log
Files
Provisioning Models-Storage & Connectivity Considerations
MPP Databases DW Appliances
Databases DBMS / NoSQL
10GBe Fast Fabric
Text, Video and Audio
Data Vulnerability
NAS - SAS and Distributed
Storage
Provisioning Models Can Vary by Data Characteristics
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Big Data Architectural Framework
Data Sources
Data as a Services
GIS Diagnostic
Images
Human Genome & Drug Discovery
Medical Devices
Surveillance and Medical Device Streaming Data
Medical Records
Data Velocity
Data Volume and
Quality
Security Services Privacy
Compliance
Social Media
Integration Tools
Distributed High Performance Data Processing
Hadoop* MapReduce
Data ingestion, Integration and Processing Services
Log Files
Provisioning Models-Storage & Connectivity Considerations
MPP Databases DW Appliances
Databases DBMS / NoSQL
10GBe Fast Fabric
Vertically Integrated Software
Intel AIM Suite
Text, Video and Audio
Data Vulnerability
HPC / TCP MIC
NAS - SAS and Distributed
Storage
Provisioning Models Can Vary by Data Characteristics
Data Characteristics
Persistence EDW, Marts
Distributed Event, Message
Virtual Real-Time, Cached, Federated
Cloud
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Big Data Architectural Framework
Data Sources
BI & Predictive Analytics MapReduce
Data as a Services
GIS
Existing BI/Analytics with in-database
data processing support
Textual Analytics
Streaming Analytics
Diagnostic Images
Human Genome & Drug Discovery
Medical Devices
Surveillance and Medical Device Streaming Data
Medical Records
Data Velocity
Data Volume
and Quality
Security Services Privacy
Compliance
Social Media
Integrated Analytics with
Hadoop Support
Integration Tools
Distributed High Performance Data Processing
Hadoop* MapReduce
Data ingestion, Integration and Processing Services
Log Files
Provisioning Models-Storage & Connectivity Considerations
MPP Databases DW Appliances
Databases DBMS / NoSQL
Custom Analytic Solutions
10GBe Fast Fabric
Vertically Integrated Software
Intel AIM Suite
Text, Video and Audio
NLP/Semantic Search/ Machine Learning
Knowledge Management
Data Vulnerability
HPC / TCP MIC
NAS - SAS and Distributed
Storage
Data Access User
Authentication
Provisioning Models Can Vary by Data Characteristics
Data Characteristics
Persistence EDW, Marts
Distributed Event, Message
Virtual Real-Time, Cached, Federated
Data Visibility
Cloud
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Accessing Big Data (Clients) “Know Me” “Free Me” “Express Me”
Smart Phone
Mobile Clinical
Assistant Tablet PCs
Laptops, Ultrabook™
Devices Fixed PCs
Digital Signage Kiosk
Mobility
Vital sign, I & O entry
Medication administration
Template data entry
Free-format text data entry
Large diagnostic images
Data inquiry
Manageability
“Link Me”
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Building on the Ecosystem Database and Analytics Environments Optimized on Intel
No Matter the Choice: All optimized on Intel® Xeon® processor based hardware
Database and compute infrastructure Analytics engines
Relational
Nonrelational
VOLTDB
EXALYTICS
Life Sciences Workloads & Solutions
Open Source: BLAST, FASTA, ClustalW, HMMER, Darwin, etc.
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Intel® Products and Software For Big Data
Network Intelligent scale-out
networking from 10GBe – 40GBe
Performance Client Rich modeling support
Client – server application management
Fast Fabric & Caching Investing in new fabric
approaches non-volatile memory that
provide capacity caching for data velocity
Intel Software EcoSystem Hadoop*
Lustre*
In-memory In stream data analysis
End to end security
Compute Intel® Xeon® processor E5-and E7 based servers up to
80% performance boost with hardware-enhanced security
Storage
Intelligent scale-out storage built with Intel Xeon
processor E5-based storage
Technical Compute Intel Xeon processor E5-
based servers for TCP Intel® Xeon Phi™ co-
processor Integrated Systems
Embedded Analysis Solutions From Intel ISG
Scaling Flexible Workloads &
Analysis Optimized
Data Delivery & Management
Software Ecosystem Interconnect
Efficiency Robust & Secure
Interconnect Visibly Mobile
Performance Client Rich Visualization Seamless Access
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Examples of Intel-powered Servers in Big Data and Analytics
The Dell | Cloudera* solution for Apache* Hadoop combines
Cisco* UCS Server1
Intel® Xeon® processor 5600
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
Dell* PowerEdge* C Series2
Intel Xeon processor 5500/5600
Cisco UCS server with EMC Greenplum MR software - “enterprise-class” Hadoop* distribution that features technology from MapR
Oracle* Sun Fire* server3
Intel Xeon processor E7-4800
Oracle Exalytics* In-Memory Machine, features the Oracle BI Foundation Suite and Oracle TimesTen In-Memory Database for Exalytics
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.
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Big Data Applications in Healthcare (PRC)
1. Pharma/Life Sciences
3. Claims, Utilization &
Fraud
4. Patient Behavior/
Social Networking
2. Clinical Decision
Support & Trends
(includes Diagnostic Imaging)
•药品研发 对药品实际 作用进行分析;实施药品市场预测 •基因测序 •分布式计算加快基因测序计算效率
•临床数据比对 匹配同类型的病人,用药 •临床决策支持 利用规则和数据实时分析给出智能提示
•公共卫生实时统计分析 发现公共卫生疫情及公民健康状况 •新农合基金数据分析 及时了解基金状况,预测风险 辅助制定农合基金的起付线,赔付病种等 •基本药物临床应用分析 分析基本药物在处方中的比例
•远程监控 采集并分析病人随身携带仪器数据,给出智能建议 •人口统计学分析 对不同群体人群的就医,健康数据实施人口统计分析 •了解病人就诊行为 发现病人的特定就诊行为,分配医疗资源
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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
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Use Case: Regional Health Info Network – China Real-time Clinical Decision Support • Real-time and recursive information
processing of health data (EHR, medical images) to support care coordination, clinical decision support, and public health management
• Enabling health data analytic with Hadoop* (HBase*/Hive*)
• Potential to scale cross geos and across sectors/segments
• Involving local ISVs, local OEMs • Technical Challenges
– Transforming the relational DB to Hadoop (HBase/Hive)
– Addressing the usages of big data analytics in RHIN
Public Health Hospital Primary care
(Grassroots)
Ancillary Data &
Services
Health Information
DW
EHR Data &
Services
Registries Data &
Services
Longitudinal Record Services
Health Information Access Layer
Care Coordination Clinical decision support …
Data Analytic R&D …
RHIN
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Distributed Data Service System
Presentation (Report, Viewer)
Integrated User Interface(Citizen, Physician, Health Authority)
Data Mining (Mahout*)
Distributed Batch Processing Framework
(MapReduce)
Coordination Service (ZooKeeper*)
Structured Data Collector (Sqoop*)
Log Date Collector (Flume*)
Distributed File System (HDFS)
Health Information Access Layer (HIAL)
Cloud -based Regional Healthcare Service System
Hospital Hospital
Real-time Database (HBase*)
Language & Compiler (Hive*)
Grassroots Care
Institution
Pubic Health
Medical Service
Drug Mgt. Service
New Rural Medical
Insurance
Server Virtualization
Storage Virtualization
Network Virtualization
Infrastructure Virtualization
Multi-Tenancy Application
EHR data Repository
Operation Mgt.
RHIN/Grassroots Solution with Big Data (Hadoop*)
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Sequencing
3 Billion Base Pairs
Data Processing
Cloud Storage Visualization
Millions of Variants
Interpretation & Analytics
Millions of Variants Millions of Patients
Commercializing Targeted
Therapeutics Companion Diagnostics
Actionable Biomarkers
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
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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
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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.
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Use Case: Big Data at Kaiser Permanente
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Data Trends
STRUCTURED DATA
80% UNSTRUCTURED
DATA
• 80% of world’s data is unstructured (Rise of Mobility devices, and machine generated data)
• 44x as much data over the coming decade (35 zettabytes by 2020)
• Majority of data growth is driven by unstructured data (Active archives, Medical images, Online movies and storage, Pictures)
• Information is centric to new wave of opportunities (Retail, Financing, Insurance, Manufacturing, Healthcare,…)
• Industry is employing Big Data Technologies for Information extraction
World’s Data
UNSTRUCTURED DATA
• 90% of Kaiser’s data is unstructured (80% of EHR and Image data)
• 25x as much data over the coming decade (One exabyte by 2020)
• Majority of data growth is driven by unstructured data (Medical Images, Videos, Text, Voice)
• Information is centric to providing Real-time Personalized Healthcare (Requires Contextual – device, environment, spatial, Demographics, Social and Behavioral profiles in addition to medical information)
• Kaiser is evaluating Big Data Technologies…
Kaiser’s Data
STRUCTURED DATA
90% UNSTRUCTURED
DATA
Source: Kaiser
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Data Platform Compute Trends – Distributed Compute
Discontinuous Change
SAN/NAS
Master
Slave(s)
• Fault-tolerant MasterSlave Architecture capable of withstanding partial system failures
• Data is distributed across processing slave
nodes
• Resources containing data are not shared
• Master manages the data distribution, job scheduling across slave nodes and aggregating result sets
• Integrate built/bought Real-time Predictive Analytical Solutions or Processing logic
SMP (5$)
MPP (10$)
In-Memory (50$)
SAN/NAS
SAN/NAS
Share-Nothing Distributed Storage and
Compute ($)
DAS
SAN/NAS
SMP (Disk Caching, High Speed Network)
(10$)
Kaiser is looking to exploit this capability…
• Structured, Relational Tabular Data
• Interactive Query Support • Real-time Analytics • SQL Transaction Data
• Unstructured, Non-tabular Data
• Rich Ad Hoc Integration • Real-time Analytics • UQL ALL Data
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Big Data Platform – Requirements
(Sensors, EMR, Claims, Pharmacy, Images)
(SLAs, Real-time Decision Support & Contextual Intelligence)
Variety
Ingestion
Integration
Interrogation
Information
(Data Model, Metadata Reference Data, Store)
(Alignment, Semantics, Completeness, Quality)
(Clustering, Statistical, Quality, Semantics)
Intuition (Simulation, Optimization, Stochastic Optimization)
(Standard & Ad Hoc reporting, Query, Alerts, Forecasting, Access)
Volume
(Structured, Text, Unstructured, Documents, Images)
Process Characteristics
A unified information storage methodology enabling users to manage data from ALL sources.
A portfolio of tools to manage (profile, cleanse, classify, synchronize, aggregate,
integrate, share) ALL types of data.
Support current BI tools focused on structured information. Build/buy packaged unstructured
data processing and analytics tools.
Ability to model information and transition from multiple access methods to generating, sharing, collaborating and acting on insights anytime,
anywhere on any device.
Velocity
Information drives process optimizations with strategic impact. Modeling business intuition
from data deluge.
Data Characteristics
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Big Data – Selection Criteria DATA SIZE
DATA TYPE
DATA CLASS
DATA CATALOG
DATA VELOCITY
DATA ACCESS
DATABASE TYPE
SERVER ARCHITECTURE
STORAGE ARCHITECTURE
Gigabytes, Terabytes, Petabytes Structured, Semi-Structured, Unstructured Human Generated, Machine Generated Text, Image, Audio, Video
Batch, Streaming
Analytics, Search, Transaction (ACID, BASE)
Relational , File Based, Columnar, NoSQL, Document, Graph, RDF
SMP, MMP, Distributed Processing
NAS, SAN, Direct Access Storage, Spinning Disks, Flash, SSD
FRAMEWORKS Financial, Computer Vision Engine, Geospatial, Machine Learning, 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
DISTRIBUTED PROCESSING Appliance, Commodity Cluster (CC) < 1K nodes, CC >1K nodes
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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
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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
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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
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Together, We Create the Network Effect
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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
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• 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
Intel Technologies
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• 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.
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Risk Factors The above statements and any others in this document that refer to plans and expectations for the first quarter, the year and the future are forward-looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,” “intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand could be different from Intel's expectations due to factors including changes in business and economic conditions; customer acceptance of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns including order cancellations; and changes in the level of inventory at customers. Uncertainty in global economic and financial conditions poses a risk that consumers and businesses may defer purchases in response to negative financial events, which could negatively affect product demand and other related matters. Intel operates in intensely competitive industries that are characterized by a high percentage of costs that are fixed or difficult to reduce in the short term and product demand that is highly variable and difficult to forecast. Revenue and the gross margin percentage are affected by the timing of Intel product introductions and the demand for and market acceptance of Intel's products; actions taken by Intel's competitors, including product offerings and introductions, marketing programs and pricing pressures and Intel’s response to such actions; and Intel’s ability to respond quickly to technological developments and to incorporate new features into its products. The gross margin percentage could vary significantly from expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and associated costs; start-up costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials or resources; product manufacturing quality/yields; and impairments of long-lived assets, including manufacturing, assembly/test and intangible assets. Intel's results could be affected by adverse economic, social, political and physical/infrastructure conditions in countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters, infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Expenses, particularly certain marketing and compensation expenses, as well as restructuring and asset impairment charges, vary depending on the level of demand for Intel's products and the level of revenue and profits. Intel’s results could be affected by the timing of closing of acquisitions and divestitures. Intel’s current chief executive officer plans to retire in May 2013 and the Board of Directors is working to choose a successor. The succession and transition process may have a direct and/or indirect effect on the business and operations of the company. In connection with the appointment of the new CEO, the company will seek to retain our executive management team (some of whom are being considered for the CEO position), and keep employees focused on achieving the company’s strategic goals and objectives. Intel's results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as the litigation and regulatory matters described in Intel's SEC reports. An unfavorable ruling could include monetary damages or an injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most recent Form 10-Q, report on Form 10-K and earnings release. Rev. 1/17/13