sones company presentation

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Create and Uncover Relationships.

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Page 1: sones company presentation

Create and Uncover Relationships.

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About sones

(*)e.g.:  Seman-c  Web  data,  workflows,  pictures,  personal  documents,  loca-on,  sensor  data,  eCommerce  items,  Facebook,  TwiAer,  blogs,  mobile  apps,  configura-on  data,  your  email  inbox,  CRM  data  

sones GraphDB is the first database for cloud computing that makes associations between complex data just like the human brain.

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Company history

T-Venture invests First customer: T-Online(prototypes)

sones GmbH founded The basic concept of the DB structure is developed Financing with seed capital 3 employees

GraphDB 1.0 Initial proof of concept Customer saves on 100 servers with version 1.0 Start of OEM and partner sales strategy

GraphDB as an open source version à OSE 1.1 – 5,000 downloads during the first month GraphDB Cloud Edition on Azure

Series A financing round with TGFS Talend data integration Enterprise Edition license for telcos, web, data analysis New CEO and expanded management

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§  How people access information today: •  using the Web (no boundaries, unstructured) or •  using databases (structured, boundaries)

§  How people will access information in the future: Sones GraphDB •  using the Semantic Web, ontologies

(no boundaries, structured, automated)

Information - the capital of today and tomorrow

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The current market

90% of data traffic today is

unstructured (worldwide)

Videos, photos, articles, user profiles,

news, groups, events...

In 2011, this digital universe will be 10 times bigger than it was in 2006 (IDC

prediction)

§  Cloud  compu-ng  data  management’s  unsolved  issues  (Cloud  Compu-ng,  Hype  Cycle,  Gartner):  §  Data  security,  data  portability,  user  controls,  reliability,  concurrency  and  dynamic  connec4ons  

between  data  records  (#)  (data  has  to  be  shi:ed  from  one  data  center  to  another  to  process  the  informa4on)  

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Database Evolution

Graph-based concepts - latest innovation

Hierarch. Database Nearly “died out”

���

Today 70s

Search, Cloud Computing 60s 80s 90s Since 2000

Relational Database ERP, CRM, …

Dominating the market���

Object. Database

Niche-products,

Developers

���

Olap - and other concepts for

real time analytics ���

Key value based concepts for

search and Web ���

Graph based Content /

Application / Analytics / Search

���

Coldplay Band

Palo Alto City

Jane Person

IBM Company

Dave Person

Bob Person

Design Team Group

Stanford Alumnae Group

IBM.com Web Site

123.JPG Photo Dave.com

Weblog

Sue Person

Joe Person

Dave.com RSS Feed

Lives in Publisher of

Friend of

Depiction of

Depiction of

Member of

Married to Member

of

Member of

Member of

Fan of

Lives in

Subscriber to

Source of

Author of

Member of

Employee of Fan of

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The database world

The innovation:

Coldplay Band

Palo Alto City

Jane Person

IBM Company

Dave Person

Bob Person

Design Team Group

Stanford Alumnae

Group

IBM.com Website

123.JPG Photo Dave.com

Web log

Sue Person

Joe Person

Dave.com RSS feed

Lives in

Publisher of

Friend of

Depiction of

Depiction of

Member of

Married to Member

of

Member of

Member of

Fan of

Lives in

Subscriber to

Source of

Author of

Member of

Employee of

Fan of

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What is sones GraphDB?

sones GraphDB: § A new type of object-oriented, graph-based database management system § Enables efficient storage, management and evaluation of complex, highly connected data records § Combines the advantages of file storage with the possibilities of a database management system § Unstructured data and information (e.g., video files), semi-structured data (metadata, e.g., log files) and structured data (similar to SQL) can be linked to each other, which makes it possible for users to manage this data themselves and evaluate when necessary

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What makes us different?

Persistence: Storage on a non-volatile storage medium

Flexible data modeling while the system is

running

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§  Information and data are saved in object networks instead of tables.

§  The original data structure is maintained.

§  New paradigm: §  Linking logic and data. Improved efficiency->real-time.

§  New functions for large numbers of queries on highly complex, distributed, dynamic data.

•Fewer processing steps required. •Cost advantages, competitive advantages

We do it differently

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Universal data access

New database applications Social CRM

Scaling at the push of a button

Personalized recommendations

Targeting

Universal data access

Public profile data Can be linked with corporate data on Facebook Increased information density

Image data Type Dimensions Width Height Resolution Bit depth

Compression Camera Photographer Price …

Metadata

Develop your own solutions using a

flexible data structure

Your data remains consistent even when

modeled while the system running

Universal access no matter where your data

is stored

Links to your corporate data

Consolidation and links to other information

REST WebDAV SOAP

Automatically generates metadata from images, videos, music and documents

Relational data silos

GraphDB

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Easy to manage

MySQL query SELECT w.word AS wort, k.sig AS sig FROM co_s k,

words w WHERE k.w1_id=(SELECT w_id FROM words w WHERE word = “Laptop”) AND k.w2_id=w.w_id ORDER BY k.sig DESC LIMIT

10;

GQL query

FROM Word SELECT Cooccurrences.TOP(10) WHERE Content = ‘Laptop’;

Easy-to-learn GQL query language

Can be scaled as you like – our

solution easily grows with your demands

Index

No. Subject … … … … … … … … … …

Index-based storage, simplifies storage and search processes

SEARCH

Rela-onal  Database  

Increasing  amount  of  connected  data  

BeAer  Performance  

=  Cost  savings  

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Real-time analytics

Analysis and prioritization Relevant information

Recognizing and evaluating multidimensional relationships

Universal analytics

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Low TCO

Highly scalable

Optimized processing power, up to 300% greater performance when

handling semi-structured data

€ $ JPEG …

No double data storage for data processing and

evaluation

Complex queries as in-depth as desired on the GraphDB call for less processing power due to their graph

structure

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Solution approaches

Cross-system duplicate recognition

Point of sale Real-time recommendations

Analyses of customer behavior

e.g.: churn detection

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Locations

sones GmbH R&D Lab Eugen-Richter-Straße 44 99085 Erfurt Germany Mail: [email protected] Tel.: +49 (0)361/ 3026 250 Fax: +49 (0)361/ 2445 008

sones GmbH Headquarters Schillerstraße 5 04109 Leipzig Germany Mail: [email protected] Tel.: +49 (0) 341/ 3929 680 Fax: +49 (0) 361/ 2445 008

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Appendix Application examples

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§  Image portal - Increases sales of images since the right image can be found much more quickly or is automatically recommended

§  AB testing - Fast and easy evaluation of marketing campaigns Real-time analysis also possible during implementation

§  Click-path analysis - e.g., via which paths do customers access the portal

Web

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§  Link building – Automatically links relevant pages/content, checks completeness of references, makes automatic recommendations of links to appropriate pages (according to topic or other criteria).

§  SEO – Optimized search results (e.g., with Google).

The system does not directly link pages but generates “link chains” that provide the desired depth (e.g., 4 plus x).

§  Content management - Providing the right content to

the right user in the right context at the right time

Web / content

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§  Enterprise Search/Enterprise Storage - Access to all data present internally regardless of their data silo. With the option of saving changes in that same location. Supplements internal data with external information from the Web (e.g. blogs/web portals/social networks).

§  Central metadata repository - Universal data access layer, centrally manage corporate data. Link data from diverse editorial sources (images, articles, etc.)

Universal data access

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§  Analysis of user behavior - How do visitors/customers behave on the corporate website?

§  Customer/user group evaluation §  SRM (social CRM) – Supplementing existing customer

data with customer data from sources such as social networks, e.g., Facebook. Intention: to develop a holistic picture of the customer. When customer X calls, sales agents/customer agents can access both the internal customer status as well as information on the customer that they have posted on blogs, social networks, etc.

Social graph

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§  Campaign management - Addressing campaigns to the right customers at the right time.

§  Automatic categorization (e.g., job profiles for job

portals) - Semantic categorization in order to increase the quality of job ads, etc., on the portal.

§  Social networks - Real-time friend-of-a-friend calculation. Who do I know through WHOM? Customizable path query with desired depth possible ad-hoc.

Social net

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§  eCommerce - Recommendations regarding the right products made to the right customers at the right time (customer-specific advertising), regional targeting. Goal: To increase the number of items sold.

§  eCommerce - Optimizing costs by reducing the

number of items returned – Automatic recognition of “safe” returns, conducting pre-defined processes, e.g., recommending suitable products, increasing costs for shipping, etc.

eCommerce

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§  Adding social commerce, i.e., recommendations from/to friends in the friendship graph (i.e., also multi-hop!) or

§  product graphs (shared shopping possible) §  for members of a group or similar shopping behaviors

§  e.g., same brand regarding individual products §  e.g., same interests/groups/rated products

Social commerce

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§  Affiliation management – Who is affiliated with which companies? Direct storage of related information such as minutes of meetings, company agreements, etc.

§  Visualization – Simple, interactive depiction of relationship networks/connections/relationships. Intuitive use (e.g.,. via Silverlight)

§  Geomapping - Linking the data mentioned above with

geoinformation Where are customers/subscribers located? (and why?)

Visualization

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§  Recalls, e.g., for cars: Ad-hoc report of all the people who purchased a car in which the defective part is installed.

§  Parts tracking – Who installed which part when? Which supplier can deliver a specific product at a certain time for the lowest price?

§  Semantic Web – social tagging, processing user generated content, crowd sourcing, social media monitoring

Miscellaneous

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§  Configuration management database

•  Definition according to Wikipedia In the IT Infrastructure Library (ITIL) context, a CMDB is a database that is used to access and manage configuration items. All IT resources are classified as configuration items (CI) in the context of IT management. […] In this context, this refers to the existing pool and the interdependencies of the objects being managed.

•  Specification: federation (metadata management) / reconciliation (target/current state comparisons) / mapping & visualization / synchronization

sones graphDB can be described as the only real CMDB

CMDB

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Disclaimer

General Disclaimer This document is not to be construed as a promise by any participating company to develop, deliver, or market a product. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. sones GmbH makes no representations or warranties with respect to the contents of this document, and specifically disclaims any express or implied warranties of merchantability or fitness for any particular purpose. The development, release, and timing of features or functionality described for sones products remains at the sole discretion of sones. Further, sones GmbH reserves the right to revise this document and to make changes to its content, at any time, without obligation to notify any person or entity of such revisions or changes. All sones marks referenced in this presentation are trademarks or registered trademarks of sones GmbH and other countries. All third-party trademarks are the property of their respective owners.