Smart Content Navigation with the

SAP HANA Platform Georg Nold (Springer Science+Business Media) and Philipp Scholl (SAP AG)

May 15, 2013

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Springer Science – overview

Springer Science+Business Media is one of the leading publishers of

scientific publications

2,700+ journals and 7,000 new book titles annually

Our customers are accessing content via SpringerLink (link.springer.com)

Subscriptions cover e.g. discipline, business branches or collections of specific journals

Large archive of publications (from 1842 to today) – most of it digitized

~7.6 million documents available via SpringerLink

Continual migration from print to electronic publishing

Goal: identify new business models for journal and book content

Grow e-book revenues

Target corporate and individual customers

Simplify functional areas around the content database to be more efficient

© 2013 SAP AG or an SAP affiliate company. All rights reserved. 3

Springer Brands and Imprints

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Springer Science – current challenges

Most accessed through search engines (like Google)

One stop and leave – bad for cross-selling

Content is stored in eight blade servers for storing metadata (A++ XML format) and

indexing and searching documents (MarkLogic)

Heavy use of preaggregated indices, split per discipline

Frequent reprocessing of XML data

Approx. 400 million accesses per month (results in ~86GB raw server log data)

200+ million full-text downloads per year (as of 2012)

Structured data available only in server log data

Generation of usage reports takes up to several days – live reporting not easily possible

Springer is going through a transformation towards digital business

© 2013 SAP AG or an SAP affiliate company. All rights reserved. 5

What is smart content navigation?

Support for users in navigating large volumes of unstructured content

Enable users to find relevant documents efficiently

Enable explorative navigation as well as direct access

Refine search goal by current context

Enable automatic content recommendations

Offer helpful workflow-supporting features

© 2013 SAP AG or an SAP affiliate company. All rights reserved. 6

Text analytics overview in SAP HANA SPS05

SAP HANA

Text processor

Linguistic

processing

Entity and

fact

extraction

Column store

SQL engine

XS engine

Domain-specific

Terminology

Documents

Web application

Structured

Data

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The numbers

One box for SAP HANA

Intel Xeon CPU E7 – 8870 2.5GHz (8 CPUs à 10 cores)

1 TB of RAM

The data

50% of all Springer content (~ 3.8 million PDF documents)

– 3.57 TB stored as disk-based column

– 280 GB full-text and index (compressed and in memory)

– Text engine indexes ~ 50,000 documents per hour

– 98 million PDF metadata entries

2.8 billion entities extracted from text

– 39 GB (compressed and in-memory)

14 months of Web server access logs

– 160 GB (compressed and in memory)

Total: 482 GB of data in memory

© 2013 SAP AG or an SAP affiliate company. All rights reserved.

Thank you

Contact information:

Georg Nold Philipp Scholl

Director Platform Integration SAP Strategic Projects Lab

Georg.Nold@springer.com P.Scholl@sap.com

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