Search and Analytics Platform for Text and Rich Media
Open Innovation is transforming everything
Connected people, apps and things generating massive data in many forms
How do you bridge the gap between data and outcomes?
Augmented Intelligence power apps for competitive advantage
Machine Learning at the Service of Business Augmented Intelligence
HPE Big Data Advanced Analytics Software Solutions
Strong information and weak information
HPE IDOL: Natural Language Processing (NLP) engine
Understanding human information
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
HPE IDOL 10 (Intelligent Data Operating Layer)Andrey Karpov
Understand virtually all of your information with high-performance analytics: Over 500 analytical functions available for text, audio, video, and image
• Derive actionable insights: Process data in near real time to gain a competitive edge
• Maximize your information reach: Connect to over 400 systems with support for over 1000 file formats, so you can find all relevant information
• Let social media work for you: Detect emerging trends and influencers in this powerful media with sophisticated sentiment analysis and clustering technology
8.0Transforming records management for Information Governance
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014pietvz
A customer service call can transform internal processes. Information in Tweets and reviews can lead to better products. Structured and unstructured data brought together can reveal patterns and relationships that unlock powerful business opportunities. We will discuss real-world use cases and best practices for building the infrastructure you need to power Big Data analytics solutions. From the latest in Hadoop innovation, cognitive computing, and cloud-based analytical web services, you will learn how organizations large and small can harness the power of unstructured human information to create, deploy, and deliver the next generation of analytics applications.
Powered by HP IDOL, HP Autonomy delivers intelligent applications that allow your organization to understand the concepts and context of all information in real time, mitigating risk and identifying opportunity. Join us at this session to learn how HP Autonomy can unlock the value of your company’s structured and unstructured data for better insight and greater competitive advantage. HP IDOL, the OS for human information, enables you to index, manage, and process all your data, both structured and unstructured. Learn how HP IDOL delivers unprecedented insights into optimized architecture, scalability, performance, mapped security, and connectivity. Find out more about IDOLOnDemand.com and how you can leverage this revolutionary technology in your own organization.
Analyzing Unstructured Data in Hadoop WebinarDatameer
Unstructured data is growing 62% per year faster than structured data. According to Gartner, data volumes are set to grow 800% in aggregate over the next 5 years, and 80% of it will be unstructured data.
This on-demand webinar will highlight and discuss:
How applying big data analytics to unstructured data can help you gain richer, deeper and more accurate insights to gain competitive advantages
The sources of unstructured data which include email, social media platforms, CRM systems, call center platforms (including notes and speech-to-text transcripts), and web scrapes
How monitoring the communications of your customers and prospects enables you to make time-sensitive decisions and jump on new business opportunities
"Empower Developers with HPE Machine Learning and Augmented Intelligence", Dr...Dataconomy Media
"Empower Developers with HPE Machine Learning and Augmented Intelligence", Dr. Abdourahmane Faye, Big Data SME Lead DACH at HPE
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Abdou Faye is Subject Matter Expert in Big Data, Predictive Analytics / Machine Learning and Business Intelligence, with more than 19 years of experience in that area in various leading and executive roles, both from a Technical, Architecture and Sales perspectives. He recently joins HPE coming from SAP, where he was leading the Predictive Analysis & Big Data CoE (Center Of Excellence) business since 2010 for DACH, CEE and CIS region, in charge of Business Development and Sales Support. Prior to SAP, he worked 4 Years at Microsoft as Senior BI & SQL-Server Consultant in Switzerland, after 10 years spent at Philip Morris (CH), Orange Telco (CH) and SEMA Group (FR). Abdou graduated from Paris 11 University in 2000, where he completed a PhD on Data Mining/Predictive Analytics, after completing a Master in Computer Science.
Understanding human information
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
HPE IDOL 10 (Intelligent Data Operating Layer)Andrey Karpov
Understand virtually all of your information with high-performance analytics: Over 500 analytical functions available for text, audio, video, and image
• Derive actionable insights: Process data in near real time to gain a competitive edge
• Maximize your information reach: Connect to over 400 systems with support for over 1000 file formats, so you can find all relevant information
• Let social media work for you: Detect emerging trends and influencers in this powerful media with sophisticated sentiment analysis and clustering technology
8.0Transforming records management for Information Governance
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014pietvz
A customer service call can transform internal processes. Information in Tweets and reviews can lead to better products. Structured and unstructured data brought together can reveal patterns and relationships that unlock powerful business opportunities. We will discuss real-world use cases and best practices for building the infrastructure you need to power Big Data analytics solutions. From the latest in Hadoop innovation, cognitive computing, and cloud-based analytical web services, you will learn how organizations large and small can harness the power of unstructured human information to create, deploy, and deliver the next generation of analytics applications.
Powered by HP IDOL, HP Autonomy delivers intelligent applications that allow your organization to understand the concepts and context of all information in real time, mitigating risk and identifying opportunity. Join us at this session to learn how HP Autonomy can unlock the value of your company’s structured and unstructured data for better insight and greater competitive advantage. HP IDOL, the OS for human information, enables you to index, manage, and process all your data, both structured and unstructured. Learn how HP IDOL delivers unprecedented insights into optimized architecture, scalability, performance, mapped security, and connectivity. Find out more about IDOLOnDemand.com and how you can leverage this revolutionary technology in your own organization.
Analyzing Unstructured Data in Hadoop WebinarDatameer
Unstructured data is growing 62% per year faster than structured data. According to Gartner, data volumes are set to grow 800% in aggregate over the next 5 years, and 80% of it will be unstructured data.
This on-demand webinar will highlight and discuss:
How applying big data analytics to unstructured data can help you gain richer, deeper and more accurate insights to gain competitive advantages
The sources of unstructured data which include email, social media platforms, CRM systems, call center platforms (including notes and speech-to-text transcripts), and web scrapes
How monitoring the communications of your customers and prospects enables you to make time-sensitive decisions and jump on new business opportunities
"Empower Developers with HPE Machine Learning and Augmented Intelligence", Dr...Dataconomy Media
"Empower Developers with HPE Machine Learning and Augmented Intelligence", Dr. Abdourahmane Faye, Big Data SME Lead DACH at HPE
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Abdou Faye is Subject Matter Expert in Big Data, Predictive Analytics / Machine Learning and Business Intelligence, with more than 19 years of experience in that area in various leading and executive roles, both from a Technical, Architecture and Sales perspectives. He recently joins HPE coming from SAP, where he was leading the Predictive Analysis & Big Data CoE (Center Of Excellence) business since 2010 for DACH, CEE and CIS region, in charge of Business Development and Sales Support. Prior to SAP, he worked 4 Years at Microsoft as Senior BI & SQL-Server Consultant in Switzerland, after 10 years spent at Philip Morris (CH), Orange Telco (CH) and SEMA Group (FR). Abdou graduated from Paris 11 University in 2000, where he completed a PhD on Data Mining/Predictive Analytics, after completing a Master in Computer Science.
This webinar featuring Claudia Imhoff, President of Intelligent Solutions & Founder of the Boulder BI Brain Trust (BBBT), Matt Schumpert, Director of Product Management and Azita Martin, CMO at Datameer, will highlight the latest technology trends in extending BI with big data analytics and the top high impact use cases.
Attendees will hear about:
-- The extended architecture for today's modern analytics environment
-- The Internet of Things (IoT) and big data
-- The evolution of analytics – from descriptive to prescriptive
-- High impact use cases as a result of the changing analytics world
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...Usama Fayyad
BigData in financial services and banking - a view from the on-line advanced analytics with case studies from Yahoo! and others. This is a shortened presentation, and the longer version available. Includes commentary on Hadoop and Map-Reduce grid and where appropriate to use.
HUG Hadoop User Group du 29 Janvier 2015 chez HP.
Slidedeck des 3 talks ci-dessous:
#1: Traitement des données non structurées (Vidéos, images, …) avec Haven pour Hadoop,
#2: Apache Flink: Fast and Reliable Large-scale Data Processing,
#3: Etude de cas, projet Hadoop dans le domaine des RH avec Capgemini.
La vectorisation des documents : rendre comparables des informations non structurées, de nouvelles opportunités pour un acteur de l’emploi
"Industrializing Machine Learning – How to Integrate ML in Existing Businesse...Dataconomy Media
"Industrializing Machine Learning – How to Integrate ML in Existing Businesses", Erik Schmiegelow, CEO at Hivemind Technologies AG
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Since 1996, Erik Schmiegelow has worked as a software architecht and consultant, building large data processing platforms for companies such as NTT DoCoMo, Royal Mail, Siemens, E-Plus, Allianz and T-Mobile; and until 2001 he was CTO at the Cologne-based digital agency denkwerk.
In 2007 he founded the telecommunications consulting agency Itellity, followed by Hivemind Technologies in 2014. Hivemind Technologies is a solutions and services company, focussed on big data analytics and stream processing technologies for web, social data and industrial applications. Erik studied computer sciences in Hamburg.
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating their infrastructures more efficiently, to analyze the success of their services, and to create a better personal experience for their customers.
CSP Top executives, Network and IT managers and Marketing, are eager to exploit the large amount of information to achieve better business decisions. They expect their Chief Technical Officer to provide end-to-end analytic solutions based on the data available in their IT and network infrastructure.
This presentation analyzes the complete value chain that can transform CSPs’ data to knowledge. It covers the sources of information, the data collection tools, the analytic platforms providing quick data access, and finally the business intelligence use cases with the presentation and visualization of the results and predictions.
Telcos are challenged in their business. Telephony becomes a commodity. How to leverage new business? Data use is key for the future business and analytics is the way to do it. This presentation shows a high-level picture on analytics.
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
The Briefing Room with Dr. Robin Bloor and HP Security Voltage
Live Webcast September 22, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=45ece7082b1d7c2cc8179bc7a1a69ea5
Hadoop is rapidly becoming a development platform and dominant server environment, and organizations are keen to take advantage of its massively scalable – and relatively inexpensive – resources. It is not, however, without its limitations, and it often requires a contingent of complementary components in order to behave within an information architecture. One area often overlooked is security, a factor that, if not considered from the onset, can insert great risk when putting sensitive data in Hadoop.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how security was never a design point for Hadoop and what organizations can do about it. He’ll be briefed by Sudeep Venkatesh of HP Security Voltage, who will explain the intricacies surrounding a secure Hadoop implementation. He will show how techniques like format-preserving and partial-field encryption can allow for analytics over protected data, with zero performance impact.
Visit InsideAnalysis.com for more information.
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
Презентация Виталия Никитина о возомжностях платформы HPE Idol для работы с BigData в современном кол-центре. Аналитика аудио и текстовой информации на базе платформы HPE IDOL
Enterprise Search: Addressing the First Problem of Big Data & Analytics - Sta...StampedeCon
This session addresses the first problems of Big Data & Analytics–Identifying, Indexing, Connecting and Gaining Insight of Existing Data to Drive Value. HPE’s Chief Field Technologist will give her perspectives on Enterprise Search as a Fundamental Cornerstone of Building a Data Driven Enterprise.
This webinar featuring Claudia Imhoff, President of Intelligent Solutions & Founder of the Boulder BI Brain Trust (BBBT), Matt Schumpert, Director of Product Management and Azita Martin, CMO at Datameer, will highlight the latest technology trends in extending BI with big data analytics and the top high impact use cases.
Attendees will hear about:
-- The extended architecture for today's modern analytics environment
-- The Internet of Things (IoT) and big data
-- The evolution of analytics – from descriptive to prescriptive
-- High impact use cases as a result of the changing analytics world
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...Usama Fayyad
BigData in financial services and banking - a view from the on-line advanced analytics with case studies from Yahoo! and others. This is a shortened presentation, and the longer version available. Includes commentary on Hadoop and Map-Reduce grid and where appropriate to use.
HUG Hadoop User Group du 29 Janvier 2015 chez HP.
Slidedeck des 3 talks ci-dessous:
#1: Traitement des données non structurées (Vidéos, images, …) avec Haven pour Hadoop,
#2: Apache Flink: Fast and Reliable Large-scale Data Processing,
#3: Etude de cas, projet Hadoop dans le domaine des RH avec Capgemini.
La vectorisation des documents : rendre comparables des informations non structurées, de nouvelles opportunités pour un acteur de l’emploi
"Industrializing Machine Learning – How to Integrate ML in Existing Businesse...Dataconomy Media
"Industrializing Machine Learning – How to Integrate ML in Existing Businesses", Erik Schmiegelow, CEO at Hivemind Technologies AG
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Since 1996, Erik Schmiegelow has worked as a software architecht and consultant, building large data processing platforms for companies such as NTT DoCoMo, Royal Mail, Siemens, E-Plus, Allianz and T-Mobile; and until 2001 he was CTO at the Cologne-based digital agency denkwerk.
In 2007 he founded the telecommunications consulting agency Itellity, followed by Hivemind Technologies in 2014. Hivemind Technologies is a solutions and services company, focussed on big data analytics and stream processing technologies for web, social data and industrial applications. Erik studied computer sciences in Hamburg.
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating their infrastructures more efficiently, to analyze the success of their services, and to create a better personal experience for their customers.
CSP Top executives, Network and IT managers and Marketing, are eager to exploit the large amount of information to achieve better business decisions. They expect their Chief Technical Officer to provide end-to-end analytic solutions based on the data available in their IT and network infrastructure.
This presentation analyzes the complete value chain that can transform CSPs’ data to knowledge. It covers the sources of information, the data collection tools, the analytic platforms providing quick data access, and finally the business intelligence use cases with the presentation and visualization of the results and predictions.
Telcos are challenged in their business. Telephony becomes a commodity. How to leverage new business? Data use is key for the future business and analytics is the way to do it. This presentation shows a high-level picture on analytics.
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
The Briefing Room with Dr. Robin Bloor and HP Security Voltage
Live Webcast September 22, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=45ece7082b1d7c2cc8179bc7a1a69ea5
Hadoop is rapidly becoming a development platform and dominant server environment, and organizations are keen to take advantage of its massively scalable – and relatively inexpensive – resources. It is not, however, without its limitations, and it often requires a contingent of complementary components in order to behave within an information architecture. One area often overlooked is security, a factor that, if not considered from the onset, can insert great risk when putting sensitive data in Hadoop.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how security was never a design point for Hadoop and what organizations can do about it. He’ll be briefed by Sudeep Venkatesh of HP Security Voltage, who will explain the intricacies surrounding a secure Hadoop implementation. He will show how techniques like format-preserving and partial-field encryption can allow for analytics over protected data, with zero performance impact.
Visit InsideAnalysis.com for more information.
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
Презентация Виталия Никитина о возомжностях платформы HPE Idol для работы с BigData в современном кол-центре. Аналитика аудио и текстовой информации на базе платформы HPE IDOL
Enterprise Search: Addressing the First Problem of Big Data & Analytics - Sta...StampedeCon
This session addresses the first problems of Big Data & Analytics–Identifying, Indexing, Connecting and Gaining Insight of Existing Data to Drive Value. HPE’s Chief Field Technologist will give her perspectives on Enterprise Search as a Fundamental Cornerstone of Building a Data Driven Enterprise.
The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere.
The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data.
Join Cloudera and Forrester to learn:
- What we mean by pervasive analytics, how it impacts your organization, and how to get started
- How leading organizations are using pervasive analytics for competitive advantage
- How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster
Social Listening and Intelligence is Predictive! Now What?Rob Key
new approaches to filtering and annotating social listening data, together with more advanced modeling, shows clearly that this data has predictive value -- if done correctly. This presentation reviews key data issues and was presented at the Advertising Research Foundation's 2015 Rethink Conference.
Intelligent Virtual Assistants, also known as Intelligent Digital Assistants, are capturing market share rapidly. As analytics and AI technologies scale, and as some standard models begin to emerge, business are starting to consider how to introduce these kinds of solutions into their customer experiences. This white paper, "Making Intelligent Virtual Assistants a Reality" attempts to demystify multiple aspects of the intelligent application ecosystem.
What kind of useful business problems can be solved by Virtual Assistants?
What are the technologies that are behind creating a Virtual Assistant, and how many new capabilities need to be integrated into the enterprise to build and deliver a Virtual Assistant?
What kind of content, knowledge representation, information architecture, assets and business processes are needed to deliver a Virtual Assistant experience?
What skills, techniques and expertise are needed of deliver a Virtual Assistant solution to the market?
Learn what is required to design and build an Intelligent Virtual Assistant, and how to deploy intelligent applications in your enterprise to achieve real business value.
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Supply Chain Intelligence and Analytics Executive Guidelines for SuccessHalo BI
Learn from industry experts about the future of supply chain analytics in 2016. Understand the main concerns of executives in the coming year and where the focus will be across the entire supply chain.
As more and more organizations move from recognizing that unstructured data exists, and remains untapped, the field of semantic technology and text analysis capabilities is
Creating a Data-Driven Organization, Data Day Texas, January 2016Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Overview of Data and Analytics Essentials and FoundationsNUS-ISS
As companies increasingly integrate data across functions, the boundaries between marketing, sales and operations have been blurring. This allows them to find new opportunities that arise by aligning and integrating the activities of supply and demand to improve commercial effectiveness. Instead of conducting post-hoc analyses that allow them to correct future actions, companies generate and analyze data in near real-time and adjust their operations processes dynamically. Transitioning from static analytics outputs to more dynamic contextualized insights means analytics can be delivered with increased relevance closer to the point of decision.
This talk will cover the analytics journey from descriptive, predictive and prescriptive analytics to derive actionable and timely insights to improve customer experience to drive marketing, salesforce and operations excellence.
Bio IT World 2019 - AI For Healthcare - Simon Taylor, LucidworksLucidworks
Presentation from Bio IT World, Boston | April 16-18, 2019
Track: AI for Healthcare: Practical Application of AI in Clinical Healthcare
Session Title: To AI or Not to AI, That Is the Question
Speaker: Simon Taylor, Lucidworks
A SMART Seminar conducted on 3 May 2013 by Ian Bertram.
Leveraging information for decision making, assessing its value and ensuring frictionless sharing of information within the enterprise and beyond is what will fuel success in the current and future economy. New use cases with insatiable demand for real-time access to socially mediated and context-aware insights make information management in the 21st century dramatically different.
For more information, see http://goo.gl/a6F2c
IW14 Session: Mike Gualtieri, Forrester ResearchSoftware AG
Session: Apama & Terracotta World; Big Data Streaming Analytics - Right Here, Right Now
Presentation Title: Streaming Analytics Is Icing On The Big Data Cake
Presentation given by Mike Gualtieri, Principal Analyst at Forrester Research, during the Apama & Terracotta World Session at Innovation World 2014 conference, Oct 13-15, 2014, at the Hyatt Regency New Orleans, produced by Software AG. Three days of vision, inspiration and insight. Innovation World is THE global event for digital leaders who are driven to leverage the Software AG Suite: Alfabet, Apama, ARIS, webMethods, Software AG Live, Terracotta and Adabas-Natural.
Similar to HPE IDOL Technical Overview - july 2016 (20)
Потребности:Надежное, экономически выгодное и простое в обращении решение для резервного копирования
Среда: VMware и Hyper-V, более, чем10 TБданных.
Предыдущее решение:
Очень дорогое и сложное решение для резервного копирования
Проблема: нехватка бюджета на нужды ИТ
Область применения:Социальные услуги.
Потребности:Сократить время и сложность бэкапа виртуальных машин.
Среда:45 ВМ, 1.5 TБданных, Windows Domain Controller, Lotus Notes, NAS
Область применения:ИТ услуги
Потребности:надежная система резервного копирования, нацеленная на сокращение времени простоя в случае отказа сервера.
Среда:множествоГипервизоров, WEBсайтов, ERP систем.
Hpe Data Protector Disaster Recovery GuideAndrey Karpov
This chapter provides a general overview of the disaster recovery process, explains the basic terms used in the Disaster Recovery guide and provides an overview of disaster recovery methods
Carefully follow the instructions below to prepare for disaster recovery and ensure a fast and efficient restore. The preparation procedure does not depend on the disaster recovery method, and includes developing a detailed disaster recovery plan, performing consistent and relevant backups, and updating the SRD file on Windows.
Assisted Manual Disaster Recovery (AMDR)
Manual Disaster Recovery (MDR)
This chapter contains descriptions of problems you might encounter while performing a disaster recovery. You can start with problems connected to a particular disaster recovery method and continue with general disaster recovery problems.
Example Preparation Tasks
Hpe Zero Downtime Administrator's GuideAndrey Karpov
Part 1: HPE P4000 SAN Solutions
This part describes how to configure the Data Protector HPE P4000 SAN Solutions integration. For information on how to perform zero downtime backup and instant recovery using the HPE P4000 SAN Solutions integration, see the HPE Data Protector Integration Guide for Microsoft Volume Shadow Copy Service.
Part 2: HPE P6000 EVA Disk Array Family
This part describes how to configure the Data Protector HPE P6000 EVA Disk Array Family integration, how to perform zero downtime backup and instant recovery using the HPE P6000 EVA Disk Array Family integration, and how to resolve the integration-specific Data Protector problems
Part 3: HPE P9000 XP Disk Array Family
This part describes how to configure the Data Protector HPE P9000 XP Disk Array Family integration, how to perform zero downtime backup and instant recovery using the HPE P9000 XP Disk Array Family integration, and how to resolve the integration-specific Data Protector problems.
Part 4: HPE 3PAR StoreServ Storage
This part describes how to configure the Data Protector HPE 3PAR StoreServ Storage integration, and how to perform zero downtime backup and instant recovery using the HPE 3PAR StoreServ Storage integration through native storage system support built-in in the Data Protector HPE P6000 / HPE 3PAR SMI-S Agent. For information on how to perform zero downtime backup and instant recovery using the HPE 3PAR StoreServ Storage integration through the Data Protector Microsoft Volume Shadow Copy Service integration, see the HPE Data Protector Integration Guide for Microsoft Volume Shadow Copy Service.
Part 5: EMC Symmetrix
This part describes how to configure the Data Protector EMC Symmetrix integration, how to perform zero downtime backup and instant recovery using the EMC Symmetrix integration, and how to resolve the integration-specific Data Protector problems.
Part 6: NetApp Storage
This part describes how to configure the Data Protector NetApp Storage integration, how to perform zero downtime backup using the NetApp Storage system, and how to resolve the integration-specific Data Protector problems.
Part 7: EMC VNX Family
This part describes how to configure the Data Protector EMC VNX Family integration, how to perform zero downtime backup using the EMC VNX storage system, and how to resolve the integration-specific Data Protector problems.
Part 8: EMC VMAX Family
This part describes how to configure the Data Protector EMC VMAX Family integration, how to perform zero downtime backup using the EMC VMAX storage system, and how to resolve the integration-specific Data Protector problems.
Introducing Backup to Disk devices and deduplication
This document describes how HPE Data Protector integrates with Backup to Disk devices and deduplication. By supporting deduplication, several new concepts are introduced to Data Protector, including a new device type, the Backup to Disk device, and four interface types: the HPE StoreOnce Software deduplication, the HPE StoreOnce Backup System, Smart Cache, and the EMC Data Domain Boost. Backup to Disk devices and deduplication are both discussed in detail in this document.
Backup to Disk devices are devices that back up data to a physical storage disk and support multi-host configurations. They support different backends such as the HP StoreOnce Software deduplication, the StoreOnce Backup system, Smart Cache, or the EMC Data Domain Boost. This document also describes the basic principles behind deduplication technology.
Data Protector supports the following deduplication backends:
HPE Data Protector Software deduplication provides the ability to deploy target-side deduplication on virtually any industry-standard hardware, offers greater flexibility than existing solutions as it can be deployed in a wider range of hardware set-ups, and provides enterprise-class scalability.
Because of the way Data Protector makes use of the extremely efficient HPE StoreOnce engine, Data Protector software deduplication uses memory very efficiently. As a result, you can deploy deduplication on application or backup servers without lowering application performance. Data Protector software deduplication can even be deployed on a virtual machine. In addition, Data Protector software deduplication delivers very high throughput. HPE StoreOnce Backup system devices are disk to disk (D2D) backup devices which support deduplication. Smart Cache devices are backup to disk devices that enable non-staged recovery from VMware backups. EMC Data Domain Boost devices are D2D backup devices which support deduplication.
Hpe Data Protector troubleshooting guideAndrey Karpov
How to troubleshoot
To solve problems quickly and efficiently:
1.Make yourself familiar with the general troubleshooting information.
2.Check if your problem is described in the HPE Data Protector Help file or the troubleshooting sections of applicable guides:
To troubleshoot installation and upgrade, see the HPE Data Protector Installation Guide.
To troubleshoot application integration sessions, see the HPE Data Protector Integration Guide.
To troubleshoot zero downtime backup and instant recovery, see the HPE Data Protector Zero Downtime Backup Administrator's Guide and HPE Data Protector Zero Downtime Backup Integration Guide.
To troubleshoot disaster recovery, see the HPE Data Protector Disaster Recovery Guide.
Overview of the installation procedure
Chapter 2: Installing Data Protector
This chapter contains detailed instructions about:
Installing the Data Protector Cell Manager and Installation Servers
Installing the Data Protector Single Server Edition
Installing the Data Protector web reporting
Chapter 3: Installing Data Protector clients
Chapter 4: Installing the Data Protector integration clients
Chapter 5: Installing Data Protector on Clusters
Chapter 6: Maintaining the installation
Chapter 7: Upgrading the Data Protector
Chapter 8: Data Protector Licensing
Chapter 9: Troubleshooting installation and upgrade
Part 1: IBM Applications
This part of the guide describes ways to back up and restore Informix Server database objects, DB2 databases, and Lotus Notes/Domino Server.
This part includes the following chapters:
Data Protector Informix Server integration
Data Protector DB2 UDB integration
lData Protector Lotus Notes/Domino Server integration
Part 2:
Microsoft Applications
This part of the guide describes ways to configure and use the following:
Data Protector Microsoft SQL Server integration
Data Protector Microsoft SQL Server 2007/2010/2013 integration
Data Protector Microsoft SharePoint Server VSS based solution
Data Protector Microsoft Exchange Server 2007 integration
Data Protector Microsoft Exchange Server 2010 integration
Data Protector Microsoft Exchange Single Mailbox integration
Part 3:
Oracle and SAP
This part of the guide describes ways to configure and use the following:
Data Protector Oracle Server integration
Data Protector MySQL integration
Data Protector SAP R/3 integration
Data Protector SAP MaxDB integration
Data Protector SAP HANA Appliance integration
Part 4:
Sybase and Network Data Management Protocol Server
This part of the guide describes ways to configure and use the following:
Sybase Server integration
Network Data Management Protocol Server integration
NetApp SnapManager solution
Part 5:
Virtualization
This part of the guide describes ways to back up VMware virtual machines and Microsoft Hyper-V data online.
This part includes the following chapters:
Data Protector Virtual Environment integration for VMware
Data Protector Virtual Environment integration for Microsoft Hyper-V
Part 6:
PostgreSQL
This part of the guide describes Data Protector integration.
This part includes the following chapter:
Data Protector PostgreSQL integration
VM Explorer® is a simple but powerful software to back up, replicate and restore your VMware ESX, ESXi and Microsoft Hyper-V Virtual Machines (VM).
The following documentation explains the main tasks required for configuration and daily use of VM Explorer®. All services hereinafter are brought to you by HPE.
The HPE services and materials presented for VM Explorer® hereinafter are protected by copyright, trademark, trade dress, unfair competition, and other intellectual property rights. The trademarks, logos and marks of HPE and VM Explorer® displayed on the services and products are the property of HPE or third parties. You are not permitted to use the Marks without the prior consent of HPE or the third party that may own the Marks.
Трансформация ИТ с помощью Hewlett Packard Enterprise
ИТ для экономики идей
Идеи всегда являлись залогом успеха в развития бизнеса. Однако одних лишь хороших идей мало. Успех определяется тем, насколько быстро компания может превращать идеи в прибыль. Сегодня путь от идеи до ее реализации радикально сократился. Именно поэтому, говоря об особенностях современного этапа развития экономики, эксперты рынка все чаще используют термин «экономика идей».
Building and managing secure private and hybrid clouds
HP Helion extends beyond just cloud to become the very fabric of your enterprise. Delivers an extensible and open portfolio to build and manage enterprise grade end-to-end orchestrated cloud services.
HPE Data Protector Administrator's GuideAndrey Karpov
About Data Protector
HPE Data Protector is a backup solution that provides reliable data protection and high accessibility for your
fast-growing business data. Data Protector offers comprehensive backup and restore functionality
specifically tailored for enterprise-wide and distributed environments.
Major Data Protector features
l Scalable and highly flexible architecture
l Mixed environment support
l Easy central administration
l High performance backup
l Easy restore
l Data and control communication security
l High availability support
l Automated or unattended operation
l Monitoring, reporting, and notification
l Service management
l Integration with online database applications
l Integration with other products
Data Protector Architecture
l Software Version number, which indicates the software version.
l Document Release Date, which changes each time the document is updated.
l Software Release Date, which indicates the release date of this version of the software.
To check for recent updates or to verify that you are using the most recent edition of a document, visit the
Knowledge Base on the HPE Big Data Customer Support Site.
Конференция по программным решениям HPE 2016Andrey Karpov
Конференция по программным решениям HPE 14 апреля 2016
Автоматизация процесса перевода транспортного сервиса между географически распределенными площадками
Система управления ТСЭР
Подсистема автоматизации процедуры перевода обработки
ЭС между центральными серверами ЦТУ ТСЭР (ПАПО)
Автоматизация процессов управления DevOps Новые реалии – новая скорость
March 2016 HPE Data Protector
Comprehensive data protection for the modern enterprise
If you pick up the latest datacenter trends reports from ESG, Gartner, and IDC, you will notice that improving backup and recovery appears among the top IT priorities for organizations. The reason for that is simple: as the velocity, variety and complexity of data continue to accelerate, so do the risks of not being able to speedily restore critical systems and applications in case of disaster or data loss.
HP Distributed R is a high-performance scalable platform for the R language. It enables R to
leverage multiple cores and multiple servers to perform Big Data Advanced Analytics. It consists of
new R language constructs to easily parallelize algorithms across multiple R processes.
HP Distributed R simplifies large-scale analysis by extending R. Because R is a single-threaded
environment, it has limited utility for Big Data analytics. HP Distributed R allows you to specify that
parts of programs be run in multiple single-threaded R-processes. This approach results in
significantly reduced execution times for Big Data analysis.
Become a data-driven organization with the Internet of Things
Executive summary
Personal health monitors tracking your fitness, trashcans monitoring their fullness, watches telling you more
than just the time, and agricultural soil monitors saying it’s time to water. It seems a day doesn’t go by that
we don’t hear about the latest “offline” thing, device, or equipment becoming “online,” moving from isolation
to being connected to the Internet of Things (IoT). It’s clear that integrating sensors, electronics, and
network connectivity into devices can enable innovation, enhancing and extending the way we work and
interact with each other and the world around us.
This document describes the functions performed by an HP Vertica database administrator (DBA).
Perform these tasks using only the dedicated database administrator account that was created
when you installed HP Vertica. The examples in this documentation set assume that the
administrative account name is dbadmin.
l To perform certain cluster configuration and administration tasks, the DBA (users of the
administrative account) must be able to supply the root password for those hosts. If this
requirement conflicts with your organization's security policies, these functions must be
performed by your IT staff.
l If you perform administrative functions using a different account from the account provided
during installation, HP Vertica encounters file ownership problems.
l If you share the administrative account password, make sure that only one user runs the
Administration Tools at any time. Otherwise, automatic configuration propagation does not
work correctly.
l The Administration Tools require that the calling user's shell be /bin/bash. Other shells give
unexpected results and are not supported.
HP Vertica Analytic Database
Creating flex tables is similar to creating other tables, except column definitions are optional. When
you create flex tables, with or without column definitions, HP Vertica implicitly adds a special
column to your table, called __raw__. This is the column that stores loaded data. The __raw__
column type is LONG VARBINARY, and its default maximum width is 130000 bytes (with an
absolute maximum of 32000000 bytes). You can change the width default with the
FlexTablesRawSize configuration parameter.
Loading data into a flex table encodes the record into a map type, and populates the __raw__
column. The map type is a standard dictionary type, pairing keys with string values as virtual
columns.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
2. Open Innovation is transforming everything
Closed technology
architecture design
“After-the fact” static analytics, e.g.
Monthly reporting
Analyze data at
“rest”
Real-time insight &
understanding via machine
learning
Put data science into your
processes – Next-gen apps
and services
Analyze and apply perishable
data
anywhere at any time
Premise-based
systems
Seamless blending of open
source, advanced technology,
deployment choices…
Contain Cost Create Outcomes
Traditional Data Analytics Open Innovation Data Analytics
Journey to the New Style of Business
3. Human data
Connected people, apps and things generating massive data in many forms
Machine data
Business data
faster growth
than
traditional
business data
10x
4. How do you bridge the gap between data and outcomes?
4
How do you consume
any data generated
or understood by
humans?
How do you identify
key aspects and
patterns to determine
outcomes?
How do you
automate to take
action?
Data sources Diverse Modern
Apps
Q1 Q2 Q3
5. Augmented Intelligence
power apps for competitive advantage
5
Augmented Intelligence
powered by HPE
Artificial intelligence, machine learning and natural
language processing using advanced analytics functions.
7. HPE Big Data Advanced Analytics Software Solutions
Vertica high-performance
advanced analytics
− Real-time performance at scale
− Premise, Cloud, and Hybrid
− Native optimized
Hadoop options
IDOL augmented intelligence for
human information
− Advanced enterprise search and rich media
analytics
− Analyze text, audio, image, and streaming
video
Haven OnDemand APIs and
Services
− Machine Learning as a Service
− Delivered on Microsoft Azure Cloud
− Accessible to any developer
Deep
Learning
Text
Analytics
Face
Detection
Neural
Network
Speech
Recognition
Categor-
ization
9. An analysis platform without data is like humans without senses
9
150 data sources
Index without relocation
10. Why is processing human data different?
Human Information is made up of ideas, is diverse and has context
– Ideas don’t exactly match like data does; they have distance.
– Human Information is not static – it’s dynamic and lives everywhere.
– Legacy techniques have all fallen short.
10
MobileTextsEmailAudioVideoSocial Media
Transactional Data Documents Search Engine Images IT/OT
16. Strong information and weak information
Key Words are small amounts of very strong information without contextLarger amounts of weaker information is what humans refer to as “context”
“Mercury”
Is it a planet?Is it an element?Is it a car?With high certainty; it’s an element!
“A heavy element and the only metal that is liquid at standard conditions for
temperature and pressure with the symbol Hg and atomic number 80,
commonly known as quicksilver”
17. Using Cognitive Analysis to form a human-like understanding of content
HPE IDOL: Natural Language Processing (NLP) engine
Fundamentally created to understand natural
human language using probabilistic modeling
and NLP algorithms
• Allows incoming data to dictate the model,
not pre-defined rules, dictionaries, or semantic webs
Self-Learning / Machine Learning
• Updates as more data is added or removed
• Adapts to changing definitions or meaning
Fundamentally language-independent
• Treats words as symbols
Optimized with language packs
• Eduction, sentiment analysis, speech analytics
Information Theory and
Bayesian Inference
18. IDOL’s Core Capabilities
18
Rich Media Analytics
Knowledge Discovery
Advanced
Enterprise Search
Data Enrichment
What is it?
Augment data with other relevant
data
Example
Extract company names from tweets and
make tweets searchable by company names
Context sensitive search across
internal and external sources
Search for Samsung and get results related
to Samsung, Apple iPhone, Huawei
Uncover trends, patterns &
relationships without explicit
queries
Uncover root causes of customer attrition
with social media and call center data
Recognize and analyze image,
video and audio
Logo/object/text recognition and speed-to-
text transcription in broadcast media
19. HPE IDOL - Market leadership
19
Gartner Magic Quadrant for Enterprise Search 2015
For the 2nd consecutive year, Gartner has
positioned HPE as a leader in its Enterprise
Search Magic Quadrant 2015 based on ability
to execute and completeness of vision.
Source: Gartner (August 2015)
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the
context of the entire document. The Gartner document is available upon request from HPE.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise
technology users to select only those vendors with the highest ratings or other designation. Gartner research
publications consist of opinions of Gartner’s research organization and should not be construed as statements of
fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of
merchantability or fitness for a particular purpose.
20. 20
HPE IDOL - Market leadership
The Forrester Wave™: Big Data Search And Knowledge Discovery Solutions, Q3 2015
The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester
Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical
representation of Forrester's call on a market and is plotted using a detailed spreadsheet
with exposed scores, weightings, and comments. Forrester does not endorse any vendor,
product, or service depicted in the Forrester Wave. Information is based on best available
resources. Opinions reflect judgment at the time and are subject to change.
• A leader in overall results based upon
strategy and current offering
• Top ranked in strategy
22. Over 500 IDOL functions to augment your intelligence
Automatic hyperlinking
Conceptual search
Keyword search
Fieldtext search
Phrase search
Phonetic search
Field modulation
Fuzzy matching
Implicit profiling
Explicit profiling
Community and expertise network
Agents
Intent-based ranking
Alerting
Social feedback
Eduction
Automatic clustering
Clustering 2D/3D
Autoclassification
Auto language detection
Sentiment analysis
Automatic taxonomy generation
Automatic Query Guidance
Highlighting
Parametric refinement
Summarization
Real-time predictive query
Metadata extraction
Automatic tagging
Faceted navigation
Inquire
Search your data
Investigate
Analyze your data
Interact
Personalize your data
Improve
Enhance your data
23. Language independence
–Free from linguistic restraints and
rules
–Automatically adapts to changing
definitions
–Over 150 languages
–Single,multibyte and Unicode
languages
–Optional language packs for
localization
24. Product performance issues
Clustering
Side letters
Off balance
sheet transactionsAutomatically
partition the data
so that similar
information is
clustered
together
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
25. Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
Add context to short queries by grouping results into concepts
Automatic Query Guidance
Query
”Madonna”
Results: Documents
containing ”Madonna”
Query
search
Documents about:
1.Singer
2. Italian Renaissance
3. Madonna Further
suggestions…
Most likely
meaning…
Result
documents
Conceptual
clustering
26. Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
Exploratory analytics that help you discover the “unknown unknowns”
Enhance your data
– Managed classification
– Create categories using business rules or training
– Automatic classification and clustering
– Automatically determine categories based on patterns and relationships in information
– Spot analysis of all themes and grouping
– Time sensitive analysis; What’s hot? What’s New?
– Eduction
– Apply structure to unstructured data by extracting key fields and entities
– Hundreds of entities supported, including names, addresses, credit card information, sentiment, intent, etc
– Audio analysis
– Speaker independent speech to text, speaker identification, audio events, language identification, etc
– Image and video analysis
– Next generation image classification (is this a car?/find more like “this”)
– On-screen OCR, logo detection, intelligent scene analysis, Color and texture analysis,
story segmentation, etc
27. Hundreds of conceptual entities
Eduction
– Quickly narrow search results with auto-identified facets and
conceptual entities such as employee names from documents
– Validate or customize entities
– Is this a valid credit card number?
– What are all docs that contain SSNs?
– If area code is 415, output as Home Office
– Pinpoint accuracy for multibyte languages such as CJK, Thai and some
European languages
Names
Places
IP addresses
Companies
Events
Relationships
Medicines
Airports
Cars
Social Security numbers
Phone numbers
Credit cards
Dates
Holidays
Job titles
Currencies
… many more
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
28. Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
Analyze your data
– Quickly evaluate the relevance of information
–Automatic Query Guidance (providing top themes from query results in real time)
–Concept navigation via advanced visualizations (node graphs, theme tracking, topic
maps, broadcast analysis)
–Intelligent summarization (simple, concept and context)
–Intelligent highlighting (search terms, phrases, concepts, context, fidelity to query
grammar)
–Concept streaming (Real-time summaries from audio that are contextual to queries and
intent)
–Intelligent de-duplication, including “near” de-duplication
– Use structure to navigate the data
–Structured, semi-structured and XML support
–Parametric search (unlimited nesting and association support)
–Directed navigation (create compelling navigation for users)
29. Personalize your data
We are what we… Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
30. Discover Relationships for Richer Insight
30
Knowledge Graph
Customer A is in
Customer B’s network
Customer C is linked
to Customer E via
Customer D
Customers F and G
purchased the same
model last year
Customer H is the
most influential in
Customer B’s network
31. Intent-based ranking
– Search results personalized and targeted based on user and context
– Profile developed through complete behavior
analysis… implicit or explicit profiling
– Gather data from content consumption,
– content contribution, interaction with
colleagues, etc.
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
32. Topical sentiment analysis
– Decomposition and classification within a sentence to pull out specific
topics
– “I stayed at the Marriott last week, and though the mattresses were
very nice, the service was awful.”
– Is this Positive? Negative? Neutral?
– How much Positive? How much Negative?
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
33. Search video as easily as text
Transform rich media into intelligent assets
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
Live video or playback
from archived footage
On-screen text
recognition
Face identification
Automatically generated
transcript using speech
recognition
Speaker identification
Timecode
synchronization
Automatic keyframe
generation
Automate
Automatically create metadata,
keyframes, transcriptions
Understand
Understand video footage and audio
streams in real time
Act
Apply advanced analytics such as
clustering and categorization, and link
with other file types
34. Image technology: 2D objects
Registered image Test image
Generic Logo recognition
Registered
Logos
Test image
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
35. Intuitive Knowledge Discovery for Self-Service Analytics
35
Visualization to simplify analytics workflow Topics Map
Sunburst
Result Comparison
Rich Contextual View
Business Intelligence for Human Information (BIFHI)
37. Customer care, turbocharged
Customer Self Service via IDOL Search
Key Differentiators
Automate more customer service with advanced
features such as contextual search, automatic
hyperlinking, implicit query, sentiment analysis,
alerting, and chat agents
Find and act on 100% of information - regardless
of language, source or information format
Scalably and securely access virtually all systems,
including cloud repositories with over 400 pre-built
enterprise-class connectors
How Customer Would Deploy
IDOL powered self service web-based support using all available knowledge sources: knowledge base, contact center, forums,
product reviews and more, with connectors to existing OSS, BSS, media solutions and network management as needed
How IDOL would drive competitive advantage for customer
• Reduced churn & improved CSAT due to enhanced automation of customer self-service and improved user experience;
• SG&A cost reduction with single systems for internal and external support
Solutions like HPE Service
Anywhere run IDOL Search
to improve service quality
and staff efficiency
38. Monitor social media to proactively address incidents and issues
Social Customer Service via IDOL
Key Differentiators
• Combine social and public data (Twitter, news, etc)
with customer data in the enterprise to gain insights
• Strong text analytics to synthesize and summarize
large volumes of data – sentiment analytics, concept
extraction, extract place names
• xxxxxHow Customer Would Deploy
Deploy IDOL with connectors to various data sources.
How Social Customer Service would drive competitive advantage for customer
Tap into other sources of customer feedback for proactive and reactive resolution of service issues.
Improve customer satisfaction, mitigate churn, identify upsell opportunities
39. Build a knowledge graph of your organization and automate customer interaction
Workforce Productivity via IDOL Knowledge Management
Key Differentiators
• Automates manual customer care processes &
actions
• Expertise location to team and deliver best response
• Proactively deliver and manage relevant & timely
data
How Customer Would Deploy
Deploy a comprehensive platform for customer
interaction to automate a time-consuming, labor-
intensive process.
How IDOL would drive competitive advantage for customer
• SG&A cost savings: Increased customer satisfaction (decrease churn rate), decreased call center load, understand your
organization better to align resources and eliminate inefficiency
• Increased revenue: Re-deploy resources to high value customer add services
Unstructured
Structured
Collaboration
Expertise
Location
Categorization
Eduction
Taxonomy
40. A Smarter Data Lake Needs…
Automatically analyse rich media
Connectors & Policies
IDOL Features
Integration points with Hadoop
Understand myriad file formats and types
Breakdown information silos across enterprise
Improved, intuitive visibility to contents
KeyView
IDOL Server (incl HDFS Sync)
Image Server & Video Server
Using IDOL to enhance Hadoop (Beta for Evaluation)
• Any Source − Build, enrich, and clean up your data lake
• Data Clarity and Mapped Security – Data dictionary and information security within your data lake
• Advanced Analytics - Provide contextual search and text, image, video, speech machine learning
41. On Screen
Text Recog.
Analyze video, audio, images to support & drive the next wave of experience and monetization
Multimedia Analytics via IDOL Multimedia
Key Differentiators
• Automate - create metadata, key frames, transcriptions
• Understand - video and audio streams in real time
• Act – apply advanced analytics (cluster, categorize, link)
How Customer Would Deploy
In line with strategic next wave value added services, rich content,
and services strategy
How IDOL Multimedia Analytics would drive competitive advantage for Customer
Drive next wave content, publishing, and advertising/monetization (revenue enhancement)
- Value Added Services to complete against OTT
- Content screening, moderation
- Ad verification
- Compliance
New Age On-Demand Internet Video, Audio
Multi
Language
Video
Analytics
Face
detection
Sentiment
extraction
Advanced
IDOL
Analytics
Speech-to-
Text
Speaker
Identify
42. IDOL powered Healthcare Analytics for 360 degree clinical intelligence
Core Capabilities:
• Integrated modular platform for variety of use cases
• Hundreds of data connectors and data types
• Rapid identification of concepts, patterns and relationships
• Conceptual search on all data
• Advanced security for compliance
Healthcare specific capabilities:
• SNOMED CT taxonomy with 344K+ concepts and 2M terms
• Integrated ICD codes
Reconciliation
ID discrepancies between
diagnostic code and clinical notes
Monitor KPIs and Metrics
Reporting
Abstraction
Rapid Chart Access
43. IDOL powered Smart City Solution
Integration Analytics Data Fusion
Integrate data feeds
from across the city into
a common command
center for investigation
and event monitoring
Add video, audio, and
event analytics to the
feeds to enable real
time monitoring for
security trends and
incidents
Complete the puzzle
with additional
information sources like
social media, broadcast
media monitoring,
employee databases,
etc.
Built-in Scalability
Unlimited expansion
and connectivity already
included at all levels by
design.
Automation
Streamlined workflow
and automated process
for triggers and alerts
45. – Challenge
• Create airline passenger registration system and compare
information against existing police databases, to protect the
country against crime.
• Been able to intercept suspect passengers before they take a
flight, during transit or at their destination point.
– Solution
– HPE IDOL + Vertica
– Benefits
– Extract meaning from virtually all forms of information associated with any airline
booking, including unstructured data such as audio, video, images, social media,
email and web content, as well as structured data such as customer transaction
logs
– Perform language-independent analysis and flag potential targets.
“2016 will see 3.6 billion passengers
worldwide making journeys by air, so we need
to employ every possible way to protect our
borders against crime”
Spanish Ministry of Interior Improves Safety With Big Data Analytics
46. China Mobile
Communications service provider industry
Challenge
– Allow users to access information on thousands of public services
directly from their mobile phones – success of the Wireless City
platform depends on the users’ ability to quickly find information
Solution
– HPE IDOL
Result
– Over 740 million subscribers can search through more than 8,000
applications for public service information, including public
transportation schedules, public health records, traffic offenses and
more
– Users receive more accurate search results than ever before
– China Mobile customers get the most relevant and useful information
regardless of the terms they use in the search
Private | Confidential | Internal Use Only 46
47. Leading American multinational telecom
Paying careful attention to every aspect of customer-facing processes and applications
Challenge
– Provide support desk staff with fast access to precise information
required to address customer’s problem
– Improve knowledge management system search capabilities
Solution
– HPE IDOL
– HPE Big Data Professional Services
Result
– Reduced time-to-resolution with fast queries that ensure support
experts can resolve customer issues quickly
– Relevant results as query functionality makes sure that results deliver
information most likely to resolve customer issues
Private | Confidential | Internal Use Only 47
48. Leading financial software, data and media company
Subscribers require up-to-the-second information on market conditions and trends
Challenge
– Deliver search performance at the scale required by the size of its data
repository, 200 million messages, 15-20 million chats daily
– Provide robust, cost-efficient solution with scalability for large and
growing volume of data, supported by small IT headcount
Solution
– HPE IDOL
– HPE Big Data Professional Services
Result
– Detects trends in real-time messaging and chats for subscribers
– Accommodates 10+ billion of document entries without compromising
performance today
– Ensures scalability delivers ROI in the future
Private | Confidential | Internal Use Only 48
49. NANA Management Services
HPE IDOL brings higher security, lower cost, better business data
Challenge
– Businesses are challenged to manage high expense of traditional
guard services, false alarm rate and security equipment failure
Solution
– HPE IDOL engine embedded in OEM security solution
– NMS Virtual Guard with Milestone video surveillance platform
Result
– Increased visibility with limited number of cameras versus human
security guards. NMS Virtual Guard records every event
– Better value from assets, system alerts users when a camera goes
down (often recording only black)
– Efficient cost structure, NMS Virtual Guard and HPE IDOL can reduce
security costs by 80% over traditional guard staffing
Private | Confidential | Internal Use Only 49
50. Free State of Saxony
HPE IDOL offers government powerful, centralized search
Challenge
– State government needed a future-proof, easy-to-administer search
solution for all administrative departments in Saxony, Germany
Solution
– HPE IDOL
Result
– A reliable, cost-effective search solution with simple administration and
high-speed indexing of web services, files, cartography, more
– 150 different internet portals indexed each night, with changes to
100,000+ documents
– System manages 110 km of documents, covering 1,100 years of
Saxon, German, and European history across five locations
Private | Confidential | Internal Use Only 50
51. HPE’s Resume Search solution
Finding talent using Big Data Platform
Challenge
– Provide a fast, reliable means for finding the right talent for contracted
service engagements n HPE’s client base
Solution
– HPE IDOL
– HPE Project Portfolio Management
Result
– Meaning-based search of unstructured data across thousands of
resumes helps locate in-house talent quickly
– Resource Brokers identify key attributes, skills and experience
required by Enterprise Services projects
Private | Confidential | Internal Use Only 51
52. HPE on HPE CX Analytics
Answering critical customer satisfaction issues better and faster
Challenge
– Pull all customer-related data into a centralized repository
– Create a set of analytics services its business units can use to
improve the company’s Net Promoter Score® (NPS)
Solution
– HPE IDOL Information Analytics and HPE Vertica Analytics Platform
– Tableau
– Hadoop
Result
– Maximize value of customer experience data to improve customer
satisfaction
– Provide snapshot of customer experience metrics that is current and
comprehensive; answer complex questions in 5-10 minutes
– Generate a 360-degree view of the HPE customer experience
Private | Confidential | Internal Use Only 52
53. Dept. of State Development, Business and Innovation
Public Sector – Victoria, Australia
Challenge
– Provide a single, secure, enterprise-wide search platform across
multiple information sources inside and outside the organization
– Locate information from different information sources such as HPE
TRIM, the DSDBI Intranet, shared network drives, Salesforce and
external sites such as Hansard, Australian Bureau of Statistics,
Victoria online and other websites
Solution
– HPE IDOL, Microsearch Portlet, Microsearch consulting services
Result
– Easily and quickly find relevant information with near real-time search
across millions of documents, 9 enterprise and Internet content
sources, leading to significant time savings
– Single sign-on allows filtered results, preventing the inadvertent
disclosure of sensitive information
Private | Confidential | Internal Use Only 53
54. Dubai Police
Accelerating law enforcement for Smart Cities
Challenge
– Use automation to locate wanted vehicles efficiently
– Read a range of number plate styles in both English and Arabic
lettering and different color codes
Solution
– HPE IDOL
– HPE Media Management and Analytics Platform
Result
– System helped Dubai Police capture 2,739 people locally and
internationally, over 18 month period
– Success led to version 2, incorporating improved cameras with ability
to read across six lanes of high-speed traffic
Private | Confidential | Internal Use Only 54
55. Auckland Transport
Driving groundbreaking Futures Cities initiative
Challenge
– Enable Safe City Solution to be predictive instead of reactive
– Deploy video analytics to support safety and well-being of citizens with
Future Cities initiative
Solution
– HPE IDOL
– HPE Media Management and Analytics Platform
Result
– Optimizing video analytics with more than 2000 video feeds recorded,
200 video analytics running in real time
– Detect red light jumps, congestion, clearway violation & much more
– Utilize data from more than 2,000 cameras monitor traffic patterns for
more than 1.4M citizens
– Implement license plate recognition for accurate identification and
scene analysis
Private | Confidential | Internal Use Only 55
56. High risk environment
Base protection
Challenge
– Detect threats anytime, anywhere by correlating
intelligence from multiple sources in various forms –
audio, video, reports and 3rd party sensors
Solution
– HPE IDOL
– HPE Media Management and Analytics Platform
Result
– Automatically flag anomalies by analyzing feeds from
aerostat, UAV, towers, and correlating with other events
– Use biometric databases to relay real-time recognition of
facial features and license plates
Private | Confidential | Internal Use Only 56
57. Stanford Children’s Health
Research for healthcare provider ranking study
Challenges
– Quality and clinical effectiveness research on ~115K patients, ~390K
encounters, ~3M documents
– Diverse data types (structured and unstructured) across data silos
involved
– Time constraints vs extensive search scope
Solution
– HPE IDOL
– Ontology Tagger and Analytics User Interface
Results
– Cross patient search for cohort identification
– Intuitive UI for simple query construction
– Easy clinical note review with highlights, navigation and related
concepts
– Portable queries and results
– Fast indexing
Private | Confidential | Internal Use Only 57
58. Global health services
Robust search technology supports health services needs of 80 million customers worldwide
Challenge
– Detect meaning of data even if data didn’t conform to specific standard
e.g. physician, MD, doctor, or Dr
– Fast query results to support positive customer experience
Solution
– HPE IDOL
Result
– Customers can quickly identify providers that meet their needs for
specialty, location and other important criteria
– Solution supports business and fiscal objectives with lower cost-in-
network providers
– Scalability maximizes ROI over time
Private | Confidential | Internal Use Only 58
59. Fortune 500 global diversified healthcare company
Private | Confidential | Internal Use Only 59
Claims data
– Provider information
– FWA recovery data
– Call center data
– Treatment/Service data
– Social media
Population and community health
Care management/Care coordination
Surveillance, Analysis, Product development innovation
Consumer cctivation/Engagement/Education
Reputation management/Outreach
Innovation focus
Lines of business
– Innovation
– Brand
– Care delivery
– Product development
– Payment integrity
– Provider
– Consumer activation
60. Fortune 500 global diversified healthcare company
Accelerate and increase cost savings
Challenges
– Drive to find savings by improving payment integrity
– Address evolving patterns of FWA
– Disparate payment systems , no single view
– Skill gaps limit access to analysis
– Long turn around time for BI analysis reports
Solution
– HPE IDOL
Results
– 24X Improvement in analysis turnaround
– Multi $M savings in weeks
– Self-service analysis for business analysts
– Single point of access covering multiple systems
– Dynamic rule-engine tests against new and historical claims to identify
potential recoveries
– Scale out on Hadoop Architecture
– Flexible platform supporting continual additions of new data and use-cases
60Private | Confidential | Internal Use Only
61. Beijing Future Advertising
Next generation sports marketing
Challenge
– Deliver high value-add services for advertising clients
Solution
– HPE Media Management and Analytics Platform
– HPE IDOL
Result
– Integrated: Bring broadcast and social media analytics together
– Efficient: Automate monitoring & analysis of broadcasts & audience
reactions. Reduced data classification and processing time from 10
tens to minutes
– Effective: Tap into insights from audience/consumer engagements
– Impactful: Provide guidance for strategy and resource investment
Private | Confidential | Internal Use Only 61
62. NASCAR
Fan and Media Engagement Center
Challenge
– Economic conditions
– Rapidly changing media landscape (social media growth)
– Rev pressures from sponsors
– Industry leadership expectation
Solution
– HPE IDOL
Results
– Live monitoring and analysis of broadcast, news and social media
– Sponsors’ brand and fan sentiment analyses
– Analytics to support race team sponsorship renewals
– Crisis management
– Build fan base with active engagement
View customer testimonial on YouTube
Private | Confidential | Internal Use Only 62
63. Summary
• Holistic – Integrate data silos & unlock hidden insights
• Proven – Sustained market leadership
• Versatile – One platform for diverse use cases
63
For more information, please visit:
www.hpe.com/software/IDOL
67. IDOL Data Ingestion pipeline
LUA scripting engine is
available within
connectors
KeyView file format process,
Eduction and LUA scripting
engine are available within CFS
OCR
Audio/Video
Category
APA Agents
Repository
Connector
Connector
framework server
Content
Repository
Connector
Repository
Connector
DIH
IDOL Proxy
Index tasks
72. Retrieval methods
– Conceptual
–Natural language
–Conceptual matching
–Unstructured refinement
– Business rules
–Boolean
–Keyword
– Parametric
–Structured refinement
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
73. Over 100 operators for Boolean search
AND
OR
NOT
NEAR
NEARn
DNEAR
DNEARn
WNEAR
WNEARn
BEFORE
AFTER
EOR
WHEN
WHENn
vAND
vSUBSTRING
vMATCHES
NEAR
NEAR/n
SENTENCE
PARAGRAPH
BEFORE
AFTER
ORDER
SOUNDEX
MANY
[n] WORD
CASE
PHRASE
. >
. >=
. <
. <=
. !=
. =
LANG/x
TODAY
YESTERDAY
NOW
NOW+n
NOW-n
term
term*
term?
vOR
vNOT
vACCRUE
vANY
vALL
vIN
vWHEN
vCONTAINS
vENDS
vSTARTS
vSUBSTRING
vCONTAINS
vENDS
vSTARTS
FREETEXT
STEM
TYPO
TYPO/n
YES-NO
PRODUCT
SUM
COMPLEMENT
LOGSUM
LOGSUM/n
MULT
MULT/n
FREQ
term~
term[100]
term[*1.5]
"term"
"term phrase"
term:field
"term
phrase":field
~term
FUZZY()
FUZZYnn()
SOUNDEX()
APCMMOD[]
term[~]
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
74. Conceptual search
– High recall and precision
–Return documents that do not contain query
terms but are conceptually related
– Input sentences or entire document as query
–Extracts main concepts in the query to
deliver the most relevant results
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
75. IDOL powers the largest systems in the world:
Scalability
– Millions of users
–Government Agency : 2.5 million users
– Billions of documents
–Major financial services firm : Over 1 bn
emails
–Major pharma: 50 terabytes of data in
discovery repository alone
– High throughput
–Major information services providers:
Alert on 46m emails per day
76. Mapped security
–Fully integrated Kerberos authentication together with
Secure Socket Layer (SSL) encryption across all transactions
–Compliance with all major Security Standards, including
US DoD5015.2, UK TNA2002, Australia’s VERS, ISO 15489
–Full-range of customizable security functionality:
– Discretionary access control (ACL based)
– Mandatory access control (Based on metadata)
– Kerberized access to IDOL
– SSO authentication using Windows Active Directory
77. Search your data
• Conceptual, Keyword or Object
• Extensive Field combinations
• Full Meta Search
• Linearly Scalable
• Fault Tolerant
• Disaster Recovery Friendly
• All Information
• Real-Time Data
• Audio and Video
• Mapped Security
• Fully Extendable
• Leverages Existing Security
Accuracy
Robust Architecture
Reach
Security
Inquire
“Search your
data”
Investiga
te
“Analyze your
data”
Interact
“Personalize
your data”
Improve
“Enhance your
data”
78. Personalize your data
Explicit profiling (Agent):
user-defined
• Define your interest using:
- Natural language descriptions
- Keyword/boolean rules
- Refine by example
• Automatically monitor information
• Customizable
• Share interests with knowledge community
Implicit profiling: capture
behavior data
• Fully automatic
• Ongoing monitoring of data consumption
and contribution
• Multi-faceted profiles
• Always up-to-date
Expertise
CommunitiesAgents
Profiles
Dynamic communities of interest
• Expert identification
• Define business rules to guide relationships
• Automatically form and
manage community
• Collaboration networks
• Document rating
• Consumer groups
Expertise Expertise
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
79. Understanding the customer at the level of a dialog
Contextual Segmentation
Geo + Demo + Psychographic
segments Behavioral segments
Functions
Performance
Feature Driven
Reviews
News
Adverts
Social media
Buzz driven
18-35 yrs
35-65
Seniors
Have Kids
Male
Female
Semantic
segments
Large Screen
Lots of storage
High Res
Display
Would give it 5
stars
Great Value for
Price
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
80. Automatic hyperlinking
–Automatically retrieves conceptually related content
–Searches automatically done for the user
–Increase productivity and reduce duplicate work
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
81. Visualization of main topics Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
82. Summarization
Quick summary
(N+ lines)
Context summary
(What is this doc about with relation
to query terms?)
Concept Summary
(What do I look for with
regards to interest rates?)
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
Information Theory and
Bayesian Inference
83. Directed navigation Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
Narrow search with facets
84. Foster collaboration byautomatically matching and connecting employees with similar needs
Connect with your colleagues
Experts
Communities
Files
Social
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
85. Eduction
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
<Organization>
• National Security Agency
<Names>
• President Obama
• Vladimir Putin
• Edward Snowden
<Places>
• Moscow
• St. Petersburg
• Washington
• Syria
• Russia
86. Automatically redact sensitive or offensive
entities
–Profanity
–Personally Identifiable Information (PII)
–Payment Card Industry Data Security
Standard (PCI-DSS)
–…and hundreds of entities
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
87. In-Document structured analytics: dynamically
create fields at query time
• Define new “computed” fields on the fly
• Define a new Total_Price field based on the indexed list price and a
run-time tax rate
• Define parametric ranges on the fly
• Add new typed fields on the fly
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
88. IDOL Speech to Text adopts next-gen algorithms
• Deep Learning Speech to Text Algorithms
– Deep Neural Networks (DNNs) used for Acoustic Modeling
– Provide deep learning of the features of Speech
– Better at approximating Speech than statistical-based algorithms
• Language Packs trained on Speech Corpora containing many hours of
Speech
89. Most Advanced Speech Technology
–Deep Neural Networks (DNNs) used for acoustic modeling
– convert spoken words to text
– Acoustic + Language Model
– Speech-to-Text and IDOL’s conceptual understanding
– Eliminate manually adding metadata to A/V clips
– Superior to phonetic and statistical-based approaches
– Model of language disambiguates similar terms
– U.S. President “Bush”
– “bush” as in a large plant
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
90. Limitations of Phonetic Search
– Phonetic sounds do not have a unique match
– Only capable of keyword matching
– “Cambridge University”
– /k ey m b r ih jh y uw n ih v er s ax t iy/
– The University of Cambridge
– Cambridge colleges
– Kings College
– Trinity Hall
/k/ /ae/ /t/
“cat”
“category”
“scatty”
“catalogue”
?
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
91. IDOL Speech is supported by powerful algorithms
Acoustic model, language model and lexicon for each language
• 30+ languages supported
• Real-time operation
• Speaker Independent
• Ability to customize language
• Telephony and broadcast
models
Models of fundamental sound
patterns – different for low
quality telephone models
(8kHz) and higher quality
broadcast models (16Khz+)
Base language
models and
customized models
that include
common phrases
and word
sequences
Trained pronunciation
dictionary with
vocabulary
TextFront End
Processing
Recognition
Algorithm
Language
Model
Lexicon
Acoustic
Model
Speec
h
92. Image technology: Text
Document field extraction
Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
<item>
<price>$6.23</pric
e>
<date>10/2/2012</
date>
<purpose>Lunch</
purpose>
…
</item>
OCR: Read text from images
1D and 2D barcode reading
ISBN
(“9870140189865”)
PDF-417 (“LASTNAME,
FIRSTNAME,…”)
Data Matrix
(“The Future of Ticketing…”)
Many more (about 20
barcode types)
Image artifacts such as wrinkled paper
Avoid non-text parts of the image
Column understanding
93. Image technology: human analysis Inquire
“Search your data”
Investigate
“Analyze your
data”
Interact
“Personalize your
data”
Improve
“Enhance your
data”
Primary clothing color =
white
Not nude
Primary clothing color =
white
Not nude
Primary clothing color =
black
Not nude
Face detection
Face analysis
Found “President Obama” face