re-imagine the future in an age of connection and insight · re-imagine the future in an age of...

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Re-Imagine the Future in an Age of Connection and Insight

Prof. Dr. Christoph MeinelPresident & CEO

Hasso Plattner Institute

University Institute with 10 departments funded by Hasso Plattner Foundation

■ ≈ 150 lecturers, assistant professors and scientific colleagues

■ ≈ 450 bachelor- and master students

■ ≈ 120 PhD students, ≈ 20 external

■ ≈ 240 „HPI Design Thinking Program“ students

USPs: HPI Future SOC Lab | D-School | Research School | openHPI …

Hasso Plattner Institute – Brief Introduction

Prof. Dr. Chr. Meinel Director & CEO

Re-Imaginingthe Future

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openHPI – The MOOC Platform of HPIopenSAP – Powered by openHPI


MOOCS – Most important Innovation in E-Learning

■ Massive ■ Open■ Online ■ Courses


■ Digital transformation refers to the fundamental changes in all branches of human society that are associated and pushed by digital technologies

■ Digital transformation affects

□ our individual life in all its facets as well as

□ all segments of social life

Digital Transformation Changes our Individual and Social Live

DigitalTransformation

Business

Government

Commnication

MedicineTraffic

Science

Art

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Digital revolution, also known as forth industrial revolution is the change from

analog technology,

mechanical technology,

electronical technology to

digital technology

Digital Transformation is Driven by the Digital Revolution

020406080

100120

Change of Technologies

digital

electronicalmechanicalanalog


■ Digital technologies allow to digitally wrap each entity in the world, whether it is a human or any kind of subject or object

■ This digital wrap, opens a powerful second channel for interactions:

– beside of the traditional physical channel

– a new digital channel is available over the Internet

■ It become possible to interact with any entity remotely via its digital wrap, no direct physical touches are needed. But contrary to physical touches digital remote interactions are possible

– over any distance, and

– almost with speed of light

Each Entity May Become Smart …


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Internet of Things Allows us to Dream of a Smart World …

Source: http://blogs-images.forbes.com/jacobmorgan/files/2014/05/libelium_smart_world_infographic_big.png

Prof. Dr. Ch. Meinel Director | CEO


The Internet of Things:Mirroring the Physical World into the Digital World

Source: http://blog.atlasrfidstore.com/wp-content/uploads/2013/07/beecham_research_internet_of_things.jpg



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The Internet of Things:E.g. Smart Home, Smart City, …

Source:http://www.districtoffuture.eu/uploads/imagenes/imagenes_meetinpoint_smart-city_2b637ab6.jpg



The Internet of Things:E.g. Smart Factory / Industry 4.0

Source: http://www.moxa.com/Event/DAC/2013/Factory_Automation_IO/images/Factory_Automation.jpg



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■ Big Data …

■ Cloud …

■ Multicore …

■ In-Memory …

This talk tries to

■ connect these buzzwords and

■ introduces some of our HPI research projects …

Current Developments in Cyberspace are Technically driven by …



What Does HPI Do?

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HPI - Top Ranked in Teaching and Research in IT- Systems Engineering

Since 2008, top place among German speaking computer science faculties ...



HPI Concern and Focus:IT-Research with Practical Relevance

Progress in hardware

development

Complex Enterprise Applications

Our focus

Advances indata processing

(Software)



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■ DBMS architecture had not changed over decades

■ New developments make changes possible

□ Hardware trends (CPU/cache/memory)

□ Changed workloads

□ Data characteristics

□ Data amount

■ Meanwhile various database manufacturers have incorporated our ideas and presented new products, e.g. SAP HANA

Recent Developments Allow New Approach to Data Processing

Buffer pool

Query engine

Traditional DBMS Architecture



…during recent years, further progress in software for processing data has been achieved

■ Column-oriented data organization (“column-store”)

□ Sequential scans allow best bandwidth utilization betweenCPU cores and memory

□ Independence of tuples within columns allows easy partitioningand therefore parallel processing

■ Lightweight Compression

□ Reducing data amount, while…

□ Increasing processing speed through late materialization

■ and more, e.g., parallel scan / join / aggregation

Recent Progress in Academic Research in Data Processing



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HPI Scientists and Students Have Developedthe In-Memory Database “Sanssouci” SAP HANA

… for enterprise applications

Developed at the chair ofProf. Hasso Plattner

Main idea:

■ Data permanently residesin main memory

■ Main Memory is the primary“persistence”

■ Only one optimization objective: main memory access

■ Cache- optimized algorithms anddata structures

17

Main Memoryat Blade i

Log

SnapshotsPassive Data (History)

Non-VolatileMemory

RecoveryLoggingTime travel

Data aging

Query Execution Metadata TA Manager

Interface Services and Session Management

Distribution Layerat Blade i

Main Store DifferentialStore

Active Data

Merg

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Col

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Col

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Indexes

Inverted

ObjectData Guide

■ Allow data-centered architecture

■ Serve as the single source for all relevant data

■ Enable real-time analytics of big data

■ Allows informed management decisions on the most current data

■ and many other things …

In-Memory Databases …



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HPI Future SOC Lab: Industry Partners Provide Latest High Computing Systems for Research …



HPI Future SOC Lab:Unique Academic IT Infrastructure



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Potentials of In-Memory: Example 1: Personalized Medicine

Personalized Medicine –Multicore und In-Memory Technologies

Personalized medicine

DNAsequencing

Analysis of genomic data

• Quantity: 3.2 million base pairs

• Data size: 1-20 GB

• Quantity:• Known mutations: 80M• Different genes: 20k-25k• Proteins: 50k-300k

• Data size: • Orientation: 5-10 GB• Variants: 10-100 GB

0 10 20 30 40

Supported by HPIToday

Duration (days)

Treatment decisionPatient has cancer Conventional therapy

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HPI In-Memory Genome ProjectChallenge of Gene Analysis

Analysis of gene data

Orientation and Variants Comment analysis inglobal DB

Dependenton CPU performance Storage capacity

Duration Hours – days WeeksHPI Minutes Real time

In-MemoryTechnologie

Multi-Core Partitioning & Compression

HPI In-Memory Genome ProjectReal-time Analysis of Genome Data



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Potentials of In-Memory: Example 2: Cyber Security Analytics - REAMS

■ Cyberattacks exploit (known) vulnerabilities in hardware, OSs and applications

■ The continuous real-time analysis of the various security sensor data makes it possible to detect cyberattacks and to react in real-time time

□ Log files (OS/App), scanning reports, virus firewall warnings, IDS alerts, monitoring logs from different sources, …

□ Post-processing (filtering, compressing…), aggregation, clustering, correlation, visualization

□ Through correlation detection of complex attack scenarios is possible

■ Due to a continuous live analysis immediate responses are possible

Based on In-Memory Technology: Real-Time Security Analysis and Monitoring



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REAMS: Real-Time Event Analysis and Monitoring System

■ Combination of IDS and SIEM

■ Based on SAP HANA Platform

□ fast in processing huge amount of log data

□ sub-second, simple and complex queries

□ HANA analytics capabilities - Predictive Analysis Library (PAL) and R integration

■ Integration of other complex analytics algorithms

HPI-REAMS:HANA-based Architecture



Based on In-Memory Technology: HPI REAMSReal-Time Security Analysis and Monitoring System



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Potentials of In-Memory: Example 3: Social Media Analytics

Why do we need to analyze Social Media?

What happens within an internet minute?

■ 100 000 tweets1

■ 300 000 Facebook updates2

■ 80 000 blog posts3

■ people spend nearly 9 hours a day online4

■ 29% of global population are active social media users

□ ca. 60% of North America, 50% of China, 35% of Germany

30

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How Complex is Social Media?

Unstructured Data is everywhere

■ Only personal information like name, birthday, likes and interests are structured

■ Main information is unstructured and buried in

□ Huge amount of posts and interactions

□ Friendship networks

□ Pictures and videos

■ Content-related analysis:

□ Content filtering

□ Opinion detection (opinion shaping)

□ Trend analysis ( “buzz“, “hot topics“)

■ Network-related analysis:

□ Information diffusion analysis / infection tree

□ Communities / blog rings / clusters

□ Rankings

Analysis of Big Data from Social Media by Means of In-Memory Technology



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Based on These Principals we have designed a Blog Search Engine: blog-intelligence.com



■ Leverage unknown information on demands in Social Media

■ Become a game changer for communication and sales

■ Identify potential customers and match them across internal and external data

■ Spend more time dedicated for real customers

■ Leverage sales tremendously

Industry Use CaseneXenio Lead Generation

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Relevance based on unstructured marketing material Flyers Presentations Mails …

Industry Use CaseLead Categorization by Machine Learning

Monitoring of several sources

New sources can be added

Internal and external sources

Industry Use CaseReal-time Monitoring of Social Media Sources

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Listen to Sources

Product1

Product2

Product3

Product4

SocialMediaSuite

Knowledgebase for finding discussions

Industry Use CaseneXenio Lead Generation Flow

Industry Use CaseneXenio Lead Inbox

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Future Research

39

■ Deep Understanding – Deep Learning

■ Advanced Forecasting / Mining

■ Artificial Intelligence for Simulated Interactions

Many Thanks of Your Attention

Contact:

Prof. Dr. Christoph MeinelHasso-Plattner-InstitutPotsdam, Germany

0331 5509-222

[email protected]

www.hpi.de/meinel

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