Serving Information Needs of Knowledge Workers

Debdoot Mukherjee, IBM Research-India

Knowledge Worker is one who develops or applies knowledge in the workplace -- Peter Drucker

Who can I reach out for help?

How did we handle such a case before?

What best practices

apply?

Information Needs of a Knowledge

Worker

Engg. Design

Customer Support

Sales / Pre-Sales

R&D

Spend 15% - 35% of their time

searching.Successful only 50% of timeSo

urc

e: I

DC

Potential Productivity Gain

20 – 25%

Sou

rce:

Mc-

Kin

sey

Huge Cost of NOTfinding the RIGHT

information at the RIGHT time

Sample Case Information created by Sales Teams

Web Portals,Wikis, Forums

100’s of structured fields in Notes databases

Dense Documents in Team Rooms

Going beyond keyword search. Users expect deeper

insights or analyses.

Complicated Access Control

Handling a mix of structured and unstructured data

Understanding results from past cases is difficult

Challenges

Deal with each case domain separately

Enumerate information needs in that domain

Extract information entities and drive semantic search

Leverage context of the casebeing worked upon

Opportunities

Information Retrieval

Information Interaction

Information Extraction Understanding the case

domain – artifacts created, different roles and their

information needs

Information Retrieval

Information Interaction

Information Extraction

Knowledge workers need to

read multiple dense

documents and distill insights thereof to arrive at a decision

Aggregate insights are often

more important than knowing about a particular case

Need to facilitate information

exploration - guess what

would one want to know next360 degree views of information entities

pureflex

IBM PureFlex™ System is an infrastructure system with expertise for sensing and anticipating resource needs to optimize your infrastructure. Key attributes include: Factory integrated and optimized system infrastructure Management integration across physical and virtual resourcesAutomation and optimization expertiseBuilt for cloud, as a foundation for Infrastructure as a Service offering

Definition from SO Village Offering page Related Links: IBM PureFlex System (ibm.com)

Switch to Search

Customers

ACME Inc.

2 Opportunities (1 Win)

Processes / Lessons

PureFlex and PureAS Solution Process

Source: CES Handbook

XYZ Ltd

2 Opportunities (1 Win)

ABCD Inc.

5 Opportunities

Apply FiltersApply Filters

Version 1 - PureFlex Solution Checklist

Source: CES Handbook

PureSystems

Source: SO Village

Value Proposition

Apply Filters

People

Apply Filters

Add a Explorer View

Select Explorer Views

Add

Leave A Comment

Each exploration view targeted toward an

unique information need

Decide which views to show based on

query and role

pureflex

IBM PureFlex™ System is an infrastructure system with expertise for sensing and anticipating resource needs to optimize your infrastructure. Key attributes include: Factory integrated and optimized system infrastructure Management integration across physical and virtual resourcesAutomation and optimization expertiseBuilt for cloud, as a foundation for Infrastructure as a Service offering

Definition from SO Village Offering page Related Links: IBM PureFlex System (ibm.com)

Switch to Search

Customers

ACME Inc.

2 Opportunities (1 Win)

Processes / Lessons

PureFlex and PureAS Solution Process

Source: CES Handbook

XYZ Ltd

2 Opportunities (1 Win)

ABCD Inc.

5 Opportunities

Apply FiltersApply Filters

Version 1 - PureFlex Solution Checklist

Source: CES Handbook

PureSystems

Source: SO Village

Value Proposition

Apply Filters

People

Apply Filters

Add a Explorer View

Leave A Comment

Client: ACME Inc

Client Background

Sector : Distribution

Industry : Travel & Transportation

Contacts : John Will (CSE), Ray Harris (TSM)

Past Opportunities (Win, Loss, Unknown) :

2Y-337WW2 1Y-43HYFD 12KZ-52XZQQU 4D-DFREE

See 5 results from IBM Connections

Similar Clients: World Tour-Co Cosmos, Globus

Comments (1)

It seems like ACME Inc. is an early adopter of Pureflex. Is it willing to act as a reference. @John Will: Any idea?

4/10/2013

Entity Profiles aggregated from information in multiple documents across multiple sources

Add Comment

Recommendation View: Change

Client

Solution

Competition

Scope

Win Themes

Value Proposition

Delivery Model

Offerings & Asset

Architecture

Financials

RAID

Engagement

HR Solution

Transition & Trans

Section Selector Section View

Current Topics

Upload Documents

Standardisation

Recommendations

Apply Filters

Recommended Topics

Faster Provisioning

Improve Speed To Market

Reduce IT Operating Costs

Pay per useScalability Flexibility

Standardization of Images Enable

collaboration with partners

ACME Inc12Y-6774YContact:Steve Toll

Govt of XYZ12Y-4YFFFTContact:Mike Chang

Case Field being worked upon (say, Value Proposition)

Recommended Value Props. from similar past cases

Visually analyze relevance of topics

Software Maintenance

Software Development

Information Retrieval

Information Interaction

Information Extraction

Summarize

Topic ModelingMMR based 2-3 line segment summary

Segment & Annotate

Dictionary, Regex-based

Leverage Formatting - Paragraphs, Tables

Crawl & Parse

Multiple platforms, technologies

Parse formatting, not just text. Export

thumbnails

Co

mm

on

Am

big

uit

ies

Diagram Parsing

• Parse information about diagram shapes

• Attributes such as coordinates, dimensions, text, geometry

Structure Inference

• Precisely determine the underlying flow graph

• Deal with structural ambiguities

Semantic Interpretation

• Classify the semantic of every node or edge based on their structural, textual or geometric features

• Unsupervised training of such a classifier performs as well as supervised

Extracting Formal Models From Informal Diagrams

Information Retrieval

Information Interaction

Information Extraction

Create ER network

Compute pair-wise Personalized

Page Rank

Leverage case context to supplement user queries

CasePeopleTechnologyRiskCustomer

Create ER network

Pair- wise Personalized

Page RankHow do we set the edge

weights? Equally?

Suppose, we want recommendations for field - Xk for this case

Let’s initiate a random walk here and try to

hit nodes of Xk

Which case fields provide meaningful context for Xk?

Across cases, if similarity in Xi leads to similarity in Xk, then Xi should be

used as context for generating recommendations for Xk

Correspondence Analysis

1. Select a pair of cases, Ri = (Ci1, Ci2) from the case repository

2. For each case field, Xk (k = 1, 2,…n), compute similarity of contents of Xk in Ci1, Ci2 Sik

3. Repeat steps 1 and 2 for all pairs of cases in repository to populate matrix S.

4. To compute Corr(Xi, Xk) for all i = 1,2…n, regress column k with the other columns in S

5. The coefficients obtained from a linear regression model obtained above for each column i gives Corr(Xi, Xk)

17

X1 X2 …. Xk ….. Xn

S11 S12 …. S1k … S1n

….

….

….

….

….

….

Sm1 Sm2 …. Smk … Smn

R1

Rm

Case Repository

Ci2Ci1

Ri

Sik = Similarity(Ci1.Xk , Ci1.Xk)

S

Correspondence, Corr(Xi , Xk) is calculated as the degree to which similarity in field Xi

corresponds to similarity in field Xk across pairs of cases.

An incoming edge to a node of type Xi is weighted by Corr(Xi , Xk) whenever a

node of type Xk is a target for any Personalized Page Rank calculation.

Semi-structured Contents

Read

Index Creator

Personalized Page Rank Calc.

PPR DBSearch &

Rank

CrawlersCrawlers

Web UI/API Query

Generator

Thumbnails

Rich texts

CrawlersParsers

Document DB

ReadCrawlersAnnotators

Graph DB

Primary

Secondary

Entity Profile Creator

Graph Builder

Front-end App

Document Processing & Analytics Pipeline

Provisioning

Project Context

SIM Architecture

Usage Tracking

Access Level Provisioning

Access Control

Search Indices

Related Publications

• Rohan Padhye, Debdoot Mukherjee, Vibha Sinha, API as a Social Glue, ICSE 2014 (In Submission)

• Mu Qiao, Debdoot Mukherjee et. al., Unleashing The Power of Expert Knowledge for IT Services Sales with Graph Search, INFORMS 2013

• Debdoot Mukherjee, Jeanette Blomberg, Rama Akkiraju, Dinesh Raghu, Monika Gupta, Sugata Ghosal, Mu Qiao, Taiga Nakamura: A Case Based Approach to Serve Information Needs in Knowledge Intensive Processes. ICSOC 2013: 541-549

• Richard Goodwin, SweeFen Goh, Pietro Mazzoleni, Vibha Sinha, Debdoot Mukherjee, SenthilMani: Effective Content Reuse for Business Consulting Practices. SRII Global Conference 2012: 682-690

• Monika Gupta, Debdoot Mukherjee, Senthil Mani, Vibha Singhal Sinha, Saurabh Sinha: Serving Information Needs in Business Process Consulting. BPM 2011: 231-247

• Debdoot Mukherjee, Senthil Mani, Vibha Singhal Sinha, Rema Ananthanarayanan, BiplavSrivastava, Pankaj Dhoolia, Prahlad Chowdhury: AHA: Asset Harvester Assistant. IEEE SCC 2010: 425-432

• Debdoot Mukherjee, Pankaj Dhoolia, Saurabh Sinha, Aubrey J. Rembert, Mangala GowriNanda: From Informal Process Diagrams to Formal Process Models. BPM 2010: 145-161

• Biplav Srivastava, Debdoot Mukherjee, Rema Ananthanarayanan, Vibha Sinha: From Model Extraction to Model-based Reuse of Enterprise Documents. COMAD 2010: 171

• Pietro Mazzoleni Debdoot Mukherjee, et. Al.: Consultant assistant: a tool for collaborative requirements gathering and business process documentation. OOPSLA Companion 2009

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