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Data Cloud. Yury Lifshits Yahoo! Research http://yury.name. My Beliefs. The key challenge in web search is structured search Part 1: What is structured search? The key challenge in structured search is collecting data Part 2: Data distribution & idea of Data Cloud

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Data cloud l.jpg

Data Cloud

Yury Lifshits

Yahoo! Research

http://yury.name


My beliefs l.jpg
My Beliefs

The key challenge in web search is structured search

Part 1: What is structured search?

The key challenge in structured search is collecting data

Part 2: Data distribution & idea of Data Cloud

Part 3: Demo: numeric data distribution

The key challenge in collecting data is incentive design

Part 4: Economics of data distribution


Slide3 l.jpg

Structured

Search


Slide11 l.jpg

Data = data of entities + data of content

Data

Structured data

Entity unit:

  • Identifier

  • Metadata:

    • Explicit key-value pairs

    • Relational properties

    • Evaluation

Semi-structured data

Content unit:

  • Body: text, video, audio, or image

  • Metadata:

    • Explicit key-value pairs

    • Relational properties

    • Evaluation


Structured search l.jpg
Structured Search

Factoid search

“what's the value of property X of object Y“

Entity hubs

  • Domain hubs

    Structured object search

    "all concerts this weekend in SF under 20$ sorted by popularity"

  • Time focus

  • Ranking focus

  • Relations focus

    Structured content search

    "all videos with Tom Brady"

    “all comments and blog posts about Bing"


Yury s wishlist l.jpg
Yury’s Wishlist

Business-generated data

  • Products, services, news, wishlists, contact data

    Reality stream, sensors

  • Where what have happened

    Expert knowledge

  • Glossary, issues, typical solutions, object databases, related objects graph

    Events

  • Sport, concerts, education, corporate, community, private

    Market graph & signals

  • Like, interested, use, following, want to buy; votes and ratings


Search as a platform l.jpg

Query analysis

Post analysis

App 3

App 1

Classic search

App 2

Structured Data

Web index

Search as a Platform

App 4


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Data Cloud

How to collect all structured data in one place?


Data producers l.jpg
Data Producers

  • People: forums, wiki, mail groups, blogs, social networks

  • Enterprizes: product profiles, corporate news, professional content

  • Sensors: GPS modules, web cameras, traffic sensors, RFID

  • Transactional data


Data distributors l.jpg

Data distributor is any technical solution to accumulate, organize and provide access to structured and semi-structured data

Data publisher: the original distributor of some data

Data retailer: a consumer-facing distributor of some data

Data Distributors


Data consumers l.jpg
Data Consumers

  • Humans

    • Email

    • Aggregators: news, friend feeds, RSS readers

    • Search

    • Browsing / random walks

  • Intelligence projects

    • Recommendation systems

    • Trend mining


Data cloud19 l.jpg
Data Cloud

Data Cloud is a centralized fully-functional data distribution service

Success metric for data cloud strategy = the total “value” of data on the cloud


To cloud solutions l.jpg
To-Cloud Solutions

  • Extraction

    • DBpedia.org, “web tables”

  • Semantic markup, data APIs

    • Yahoo! SearchMonkey

  • Feeds

    • Yahoo! Shopping

    • Disqus.com, js-kit.com, Facebook Connect

  • Direct publishing


On cloud solutions l.jpg
On-Cloud Solutions

  • Ontology maintenance

    • Freebase

  • Normalization, de-duplication, antispam

  • Named entity recognition, metadata inference, ranking

  • Data recycling (cross-references)

    • Amazon Public Data Sets

    • Viral license

  • Hosted search

    • Yahoo! BOSS


From cloud solutions l.jpg
From-Cloud Solutions

  • Search, audience

    • Y! SearchMonkey, Google Base

  • Data API, dump access, update stream

  • Custom notifications

    • Gnip.com

  • Data cloud as a primary backend

  • Access control

    • Ad distribution. (AT&T and Yahoo! Local deal)


Slide23 l.jpg

Demo:

webNumbr.com

Joint work with Paul Tarjan


Webnumbr com import l.jpg
webNumbr.com: Import

  • Crawl numbers from the web

    URL + XPath + regex

  • Create “numbr pages”

  • Update their values every hour

  • Keep the history

    Anyone can create a numbr

    http://webnumbr.com/create


Webnumbr com export l.jpg
webNumbr.com: Export

  • Embed code

  • Graphs

  • Search & browse

  • RSS


Slide27 l.jpg

Economics of Data Distribution

Joint work with Ravi Kumar and Andrew Tomkins


Network effect in two sided markets l.jpg

Two sided market = every product serves consumers of two types A and B

Cross-side network effect: the more type-A users product X has, the more attractive it is for type-B consumers and vice versa

Examples: operating systems, credit cards, e-commerce marketplaces

Two-sided network effects: A theory of information product design

G. Parker, M.W. Van Alstyne, N. Bulkley, M. Van Alstyne

Network Effect in Two-Sided Markets


Basic model l.jpg
Basic model types A and B

  • Distributors D1, … Dk

  • Producer/consumer joins only one distributor

  • Initial shares (p1,c1) … (pk,ck)

  • New consumer selects a distributor with a probability proportional to pi

  • New producer selects a distributor with probability proportional to ci


Basic model30 l.jpg
Basic model types A and B

a2

a4

a3

a1

a1

a3

a4

a2


Market shares dynamics l.jpg
Market Shares Dynamics types A and B

Theorem 1

Market shares will stabilize

Theorem 2

With super-liner preference rule

one of distributors will tip

Theorem 3

With sub-liner preference rule

market shares will flatten


External factor l.jpg
External Factor types A and B

Preference rule with external factor:

ei+ci/(c1+…+ck)

  • Theorem 4

    • Market shares will stabilize on

    • e1 : e2 : … : ek


Coalition l.jpg
Coalition types A and B

Data Cloud


Coalitions l.jpg
Coalitions types A and B

Theorem 5

If all market shares are below 1/sqrt(k)

coalition (sharing data) is profitable for

all distributors

Corollary

Coalitions are not monotone

Example: 5 : 4 : 1 : 1


Model variations l.jpg
Model Variations types A and B

  • Same-side network effect

  • Different p-to-c and c-to-p rules

  • Multi-homing (overlapping audiences)

  • n^2 vs. nlog n revenue models

  • Mature market: newcomer rate = departing rate

  • Diverse market (many types of producers and consumers)

  • Newcoming and departing distributors

  • Directed coalitions


Slide36 l.jpg

Challenges types A and B


Marketing l.jpg
Marketing types A and B

  • Data demand?

  • Data offerings?

  • Requirements for distribution technology?


Incentive design l.jpg
Incentive design types A and B

  • Incentives for data sharing?

  • Centralized or distributed?

    • For profit or non-profit?

  • Data licensing and ownership?

  • Monetizing data cloud?


  • More challenges l.jpg
    More Challenges types A and B

    Prototyping:

    • Data marketplace: open data & data demand

    • Search plugins: related objects, glossaries, object timelines

    • Publishing tools for structured data

    • Data client: structured news, bookmarking, notifications

      Tech design:

    • Access management

    • Namespace design

      User interface:

    • Structured search UI

    • Discovery UI


    Slide40 l.jpg

    Thanks! types A and B

    Follow my research:

    http://twitter.com/yurylifshits

    http://yury.name/blog


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