Co-ranking Mul7ple En77es in Online Enterprise Social Network -- … Wang-Hitachi... · 2017-05-02 · Ranking documents in online enterprise social network 21 • Social media applications

AlexWang,Ph.D.BigDataLab@Hitachi

Co-rankingMul7pleEn77esinOnlineEnterpriseSocialNetwork--ABigDataApproach

© Hitachi America, Ltd. 2016. All rights reserved.

1. Reorganization of the R&D Group

Reorganize the global R&D organization along 3 innovation strategy axes

Co-create services and solutions with customers

Generate innovative products by focusing on strong technology platforms & their deployment

Pioneer new frontiers through vision-driven exploratory basic research

approx. 500

approx. 2,000

approx. 100

Customer-driven 顧客協創

Technology-driven 技術革新

Vision-driven 基礎探索


2. CSI-NA Organization & Location

CSI-NA 100 HC

Silicon Valley RC: Big Data Analytics/Service PFIT/IoT/UXD, Detroit RC: Automotive, Brazil: Resources

Silicon Valley RC, USCustomer Co-creation

Big Data Analytics

· Energy · Social Media ·  ITCS · Healthcare · Financial

· Predictive/Prescriptive Analytics · Common Analytics Platform

Focus Verticals

Digital Service Platform (ITPF, IoT and Network) User Experience Design

Detroit RC, USCustomer Co-creation

· Automotive Focus Verticals

Sao Paulo, Brazil Customer Co-creation

· Resources · Agriculture

Focus Verticals

ITCS: Information and Telecommunication System, UXD: User Experience Design

Co-create advanced solutions with customers on common Big Data Analytics Platform


3. Global Center for Social Innovation (CSI)

Center for Technology Innovation

・Service design ・Ethnography

Co-design method Utilize technology platforms

Identify issues together with customers & provide solutions

� Share vision with

customer

� Generate new

concepts, Develop

prototype & demos

� Develop solution

Same sector

↓

Different sector

deployment

� Proof-of-concept

at customer site

CSI activity CommercializeC

o-cr

eatio

n ap

proa

ch


4. Evolution of Big Data

Enterprise Data

Human-Generated Data

Machine Data

“50 ZB of data created”

1.3T Tags and sensors

144.8 billion Email messages per day 340 million tweets per day

684,000 bits of content shared on Facebook per day 11 million instant messages per day

72 hours of video uploaded on YouTube per minute

4B people online 31B Devices

“By 2020, there will be more information coming from everywhere”


5. Evolution of Analytics Sophistication

Sophistication

Va

lue

Information

Knowledge in Advance

Optimization

What happened and Why?

What will happen and When?

What is the best action to take?

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics


6. Analytics: From Data to Actionable Insights

Big Data are high volume, high velocity, high variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight & decision making “at the speed of business”

Data Types & Sources

Machine Data (Realtime

Data)

User Generated

Data

Documentation

Newsfeeds /public

sources

Transaction Data

Big Data Analytics

Descrip(ve

Predic(ve

Prescrip(ve

Better Insights, Better Decisions

Business Outcome

IT X OT powering Big Data Analytics powering Big Decisions

Feedback Loop


7. Big Data Lab Mission and Strategy

Repeatable solutions delivered as applications or services by HCC, HDS, and other Hitachi companies.

Create innovative social innovation solutions that deliver business value to customers via Hitachi business divisions

Strategies • Big Data Technologies

Key analytics solution IP • Solutions Framework

for developing, deploying, and executing repeatable, high-value analytic applications in multiple industries

• Big Data Solutions PoCs tha t lead to h igh ly leveraged solutions in selected domains (collaboration with HGC-IA, customers/partners)

• Solutions Showcase

Delivery Model

HCC: Hitachi Consulting Company, HDS: Hitachi Data Systems, HGC-IA: Hitachi Global Center for Innovative Analytics, IP: Intellectual Property, PoC: Proof of Concept


8. Analytics Solutions and Platform & Technologies

Ana

lytic

s fo

r So

cial

Med

ia

Ana

lytic

s fo

r Fi

nanc

ial/R

etai

l

Ana

lytic

s fo

r N

atur

al R

esou

rces

Ana

lytic

s fo

r H

ealth

care

Ana

lytic

s fo

r Po

wer

/Ene

rgy

Ana

lytic

s fo

r Pu

blic

Saf

ety

Ana

lytic

s fo

r In

form

atio

n an

d

Tele

com

mun

icat

ion

ANALYTICS SOLUTIONS

ANALYTICS PLATFORM

and TECHNOLOGIES

•  descriptive, predictive, prescriptive analytics, machine learning, deep learning and AI algorithms •  simulation •  mathematical optimization•  information visualization and visual analytics•  real-time and streaming analytics

•  analytics over unstructured and semi-structured data (text, images, video) •  analytics framework for rapid development and deployment •  IoT platform architectures•  edge/core analytics


Automotive Power/Energy Power T&D BEMS Connected car

Predictive analytics with occupancy data for building performance efficiency

PMU data analytics for detecting and predicting disturbances on the power grid

Social Media Social Network

Real-time network monitoring, analytics and optimization

Telecom

Risk Stratification, Disease Progression, Triage

Population health

Healthcare

Public Safety Video surveillance

Video surveillance with advanced video analytics (multi-perspective search)

Telematics data analytics for improving life with cars

Oil & Gas Mining

Shale Oil/Gas well production optimization

Overall Operational Efficiency, Dynamic dispatch, predictive maintenance

Natural Resource

Predictive analytics for fleets with telematics data

Fleet management

For fleet management, Mining, Chillers, …

Predictive Maintenance Financial

New financial business models with Fintechs (Blockchain etc.)

9. Customer-driven Big Data Projects

BEMS: Building Energy Management System, PMU: Phasor Measurement Unit, T&D: Transmission and Distribution

Fintech

All Rights Reserved, Copyright（C） 2003, Hitachi, Ltd. －10－ Hitachi Confidential

Sentiment Analysis by Hitachi America n  Primary Research Goal

u  Establishing “PSM (Public Sector Marketing) Process” u  Introducing “Voice of the Crowd” to the existing and / or future infrastructures u  Developing technologies required for its process

n  Case Study – Sentiment Analysis of Public Transportation System Services using Twitter Data u  A real data set is collected using Twitter Streaming API u  “public transportation” is used as the key word u  Classifying data into 3 classes: Positive; Negative; Neutral

Austin, TX 8%

Boston, MA 17%

Chicago, IL 16%

Los Angeles, CA 1%

New York, NY 30%

San Francisco, CA 7%

Washington, D.C. 21%

Major City - Positive User Austin, TX

1%

Boston, MA 13%

Chicago, IL 15%

Los Angeles, CA

11%

New York, NY 49%

San Francisco, CA 5%

Washington, D.C. 6%

Major City - Negative User

All Rights Reserved, Copyright（C） 2003, Hitachi, Ltd. －11－ Hitachi Confidential

Social Analytics by Hitachi America

n  Primary Research Goal u  Analyzing and modeling the social

graphs in enterprise social networks

u  Studying the impact of user attributes (eg. geographic locations and his/her ranks) on user interactions

n  Case Study – Social Graph Analysis of a large enterprise social network u  A real data set is collected using

Jive Software REST API u  A directed social graph is built

based user’s “following" relationship

Node Size: In-Degree (Number of Followers)

© Hitachi America, Ltd. 2016. All rights reserved. © Hitachi America, Ltd. 2014. All rights reserved. 12

•  Social media applications such as Jive, Salesforce.com, and Slack have been widely adopted by enterprises.

Enterprise Social Network


•  The importance of a document posted in the Enterprise Social Network (ESN) is different from traditional document collections or Web pages.

•  More importantly, it is different from public social networks such as Facebook and Twitter, which view the entire social network as a “flat” graph.

•  In most enterprise social media applications, the documents are organized in a hierarchical structure, which may include “group”, “space”, “project”, etc.

•  Also, the author of a document plays a critical role in the importance of that document.

The importance of a Document in ESN


•  “who posted it” –  A document posted by an influential user may get a better chance to be viewed by others

•  “where it posted” –  A document that is posted in a popular space may get a better chance to be viewed by others

•  The document importance also affects the influences of the users and popularities of the spaces

•  We formulate a system of equations that describes the joint relationship among the ranks of documents, users, and spaces, and use its solution to solve the document ranking problem.

Evaluating the importance of a Document in ESN


•  Relationships among Author, Document, and Space

Reinforcing relationship


•  The ESN is modeled as a directed graph based on the “following” relationships between users –  The graph G = (V,A) consists of a set V of nodes (vertices) representing user accounts and a set

A of arcs (directed edges) that connect nodes. –  The in-degree dI(i) of a node vi, which is the number of arcs into i, is equal to the number of

followers of user i.

Social graph


•  Initial ranking of the users and spaces in the ESN •  Initial influence of user i is defined as yi

0 = dI(i) / Nu •  dI(i) is the in-degree of a node vi •  Nu is the total number of arcs in this graph

•  Initial popularity of space s is zs0 = Ns / Nd.

•  Nd denote the total number of documents posted in the ESN and Ns the number of documents posted in space s

Initial Ranking


•  document importance xd, user influence yi, and space popularity zs

rewrite the above equations in a compact manner By substituting the last two equations into the first, we get where The document importance is then given by the solution to the above linear equation, namely

Calculating the importance



20. Common Analytics Framework & Environment

Leveraging Hitachi’s unique experience and IP for customer PoCs

Data Source Billing Supplier Customer Operations Production Maintenance Supply Chain

Dashboard Designer Flow Builder

Application Designer

OT Domain Expert

OT X IT Solution Component

OT X IT Dashboard(s)

All Rights Reserved, Copyright（C） 2003, Hitachi, Ltd. －21－ Hitachi Confidential © Hitachi America, Ltd. 2014. All rights reserved.

Ranking documents in online enterprise social network

21

•  Social media applications such as Jive, Salesforce.com, and Yammer have been widely adopted by enterprises.

•  The importance of a document posted in the Enterprise Social Network (ESN) is different from traditional document collections or Web pages.

•  More importantly, it is different from public social networks such as Facebook and Twitter •  The entire social network is viewed as a “flat” graph.

•  In most enterprise social media applications •  The documents are organized in a hierarchical structure

•  “group”, “space”, “project”, and etc •  The author of a document plays a critical role in the importance of that

document. •  Proposing a novel method to rate the importance of every document posted in an

ESN objectively.


Summary

22

•  To rate the importance of a document posted in an ESN, we consider two major factors:

•  “who posted it” •  A document posted by an influential user may get

a better chance to be viewed by others •  “where it posted”

•  A document that is posted in a popular space may get a better chance to be viewed by others

•  The document importance also affect the influences of the users and popularities of the spaces.

•  We formulate a system of equations that describes the joint relationship among the ranks of documents, users, and spaces, and use its solution to solve the document ranking problem.

S1

S2

D1

D2

D3 D4 D5 D6

A

B

C

Author Document Space


Document Ranking Calculation

23

•  Initial ranking of the users and spaces in the ESN •  Initial influence of user i is defined as yi

0 = dI(i) / Nu •  dI(i) is the in-degree of a node vi •  Nu is the total number of arcs in this graph

•  Initial popularity of space s is zs0 = Ns / Nd.

•  Nd denote the total number of documents posted in the ESN and Ns the number of documents posted in space s

A B

C

E

D

•  We define the document importance xd, user influence yi, and space popularity zs as the solution to the following system of equations:

Documents

Co-ranking Mul7ple En77es in Online Enterprise Social Network -- … Wang-Hitachi... · 2017-05-02 · Ranking documents in online enterprise social network 21 • Social media applications