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APRIL 2012

The Cloud: Searching for Meaning Finding Relevant Data in the Cloud for Actionable Decisions Š CGI GROUP INC. All rights reserved _experience the commitment TM


Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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Information Retrieval is beyond databases Information Retrieval*, aka Search, is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers).

Search > SELECT * FROM Enterprise Data

DBMS

Go

“An Information Need* is the topic about which the user desires to know more, and is differentiated from a query, which is what the user conveys to the computer in an attempt to communicate the information need.�

* Maning, C. D., Raghavan, P. and Schutze, H. An Introduction to Information Retrieval. 2009

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Volume, variety and velocity… Big Data Information Retrieval*, aka Search, is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers).

The “Cloud”

Search > SELECT * FROM Enterprise Data

Go

Big Data refers to fast growing, large data sets that cannot be managed with “traditional” Database Management Systems.

DBMS * Maning, C. D., Raghavan, P. and Schutze, H. An Introduction to Information Retrieval. 2009

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Consumer market is there and Organizations can learn from it

Siri: Searching for… Personal Data

The “Cloud”

iPhone

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Analytics is enabling these capabilities Information Retrieval applies analytic techniques such as clustering and classification to support users in browsing or filtering document collections or further processing a set of retrieved documents.*

The “Cloud�

Search

Go

> SELECT * FROM Enterprise Data

Big Data

DBMS * Maning, C. D., Raghavan, P. and Schutze, H. An Introduction to Information Retrieval. 2009

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Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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The “ABC” Formula

The “Cloud”

Analytics

Search

Go

> SELECT * FROM Enterprise Data

Big Data

DBMS Analytics + Big Data + The “Cloud” = Enhanced Business Operations 9 Confidential


Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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Some of the Challenges Finding relevant data Large-scale data sets Quality of search results •

“A document is relevant* if it is one that the user perceives as containing information of value with respect to their personal information need.” “Something (A) is relevant** to a task (T) if it increases the likelihood of accomplishing the goal (G), which is implied by T.”

* Maning, C. D., Raghavan, P. and Schutze, H. An Introduction to Information Retrieval. 2009 ** Hjorland, B. and Christensen, F. S. Work tasks and socio-cognitive relevance: A specific example. 2002

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Some of the Challenges Finding relevant data Large-scale data sets Quality of search results • •

Personal Information Retrieval: The system searches operating systems, e-mail, and other device applications. Enterprise, Institutional, and domain-specific search: Documents are typically stored on centralized file systems and/or dedicated servers. Web Search: The system has to provide search over billions of documents stored on millions of computers. 12 Confidential


Some of the Challenges Finding relevant data Large-scale data sets Quality of search results •

To assess effectiveness of an Information Retrieval system (i.e., the quality of its search results), a user will usually want to know two key statistics about the system’s returned results for a query or search: Precision: What fraction of the returned results are relevant to the information need? • Recall: What fraction of the relevant documents in the collection were returned by the system? •

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Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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Example 1: The Right Profile

Analytics: Text Mining

The “Cloud”

Search > SELECT * FROM Pipeline Data

Go

Big Data: LinkedIn 150 million professionals

DBMS Analytics + Big Data + The “Cloud” = Enhanced Recruitment Process 15 Confidential


Example 1: The Right Profile

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Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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Example 2: Like it 

Sentiment Analysis

The “Cloud”

Search > SELECT * FROM Pipeline Data

Go

Big Data: Twitter 340 million tweets/day

DBMS Analytics + Big Data + The “Cloud” = Enhanced Customer Satisfaction 18 Confidential


Example 2: Like it 

Public Relations using “Twitter Earth” Case: Tracking tweets and displaying them by location 19 Confidential


Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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Example 3: Promote it

The “Cloud”

“Wisdom”

Search > SELECT * FROM Pipeline Data

Go

Big Data: Facebook 800 million users

DBMS Analytics + Big Data + The “Cloud” = Enhanced Marketing Effectiveness 21 Confidential


Example 3: Promote it

Social Intelligence using “Wisdom� Case: Analyzing 10 million Facebook users to promote Engineering 22 Confidential


Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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Conclusions •

Analytics add capabilities to information retrieval systems that facilitate finding relevant data in the “cloud”.

Analytics enables information retrieval systems to deal with large-scale data sets and therefore is recommendable for working with Big Data.

Analytics provides advanced techniques for more effective browsing and filtering of Big data. How are you driving business value with the data assets accessible in by your organization? Consider the “ABC” formula 24 Confidential


Agenda • • • • • • • •

Information Retrieval The “ABC” Formula Some of the Challenges Example 1: The Right Profile Example 2: Like it  Example 3: Promote it Conclusions Q&A

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Andres Dorado -Finding Relevant Data in the Cloud  
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