Data 2.0|

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Published on January 13, 2009

Author: npch

Source: slideshare.net

Description

Presentation given at Supercomputing 2007 on the progress of data sharing models, specifically highlighting the collision of data grid / data service and Web 2.0 worlds.

Data 2.0 a new way of integrating data? Neil Chue Hong SC07, Reno

Summary From Data Grids To Data Services The Rise of Web 2.0 Towards Data 2.0

From Data Grids

To Data Services

The Rise of Web 2.0

Towards Data 2.0

Grid versus Users Grid is about: sharing resources interoperable middleware allowing bigger problems integrating communities improving security bringing together data Users want to: access more resources ignore middleware solve bigger problems form communities have simple security bring together data Grid and Users want very similar things and yet there is still a “want-got-gap” between them how can this be bridged?

Grid is about:

sharing resources

interoperable middleware

allowing bigger problems

integrating communities

improving security

bringing together data

Users want to:

access more resources

ignore middleware

solve bigger problems

form communities

have simple security

bring together data

Grid and Users want very similar things

and yet there is still a “want-got-gap” between them

how can this be bridged?

Data Grids The first generation of Grids concentrated on Compute Grids harnessing capacity to improve capability Then came the first Data Grids mechanisms for dealing with the large amounts of data generated by sensors and simulations

The first generation of Grids concentrated on Compute Grids

harnessing capacity to improve capability

Then came the first Data Grids

mechanisms for dealing with the large amounts of data generated by sensors and simulations

Data Challenges Diversity Scale Ownership Security of data resource types, vendors, middleware, schema, metadata of collections, formats, geographical, political and social distance on individual, group, and organisation levels; intersecting yet independent for client, service and data owner; at many levels, with many tradeoffs

Move towards data services Defined interface to stored collection of data e.g. Google and Amazon But the data could be: replicated shared federated virtual incomplete Improve the ability to discover, reference, annotate, search, and provide provenance Make access transparent Make integration easy Make management simple

Defined interface to stored collection of data

e.g. Google and Amazon

But the data could be:

replicated

shared

federated

virtual

incomplete

Improve the ability to discover, reference,

annotate, search, and provide provenance

Grid Data Services Data middleware provides a way of publishing data in a uniform way accessible discoverable searchable Provide tools such as registries replica catalogs mediators

Data middleware provides a way of publishing data in a uniform way

accessible

discoverable

searchable

Provide tools such as

registries

replica catalogs

mediators

Grid versus User: Round 2 Grids provide: data discovery services distributed queries basic provenance workflows to represent analysis process Users want: information to find the right data cross-database searches sophisticated annotation to explore the information space Data 2.0 must go beyond simple data access domain-specific vs generic data services composability, interoperability and ease of use

Grids provide:

data

discovery services

distributed queries

basic provenance

workflows to represent analysis process

Users want:

information

to find the right data

cross-database searches

sophisticated annotation

to explore the information space

Data 2.0 must go beyond simple data access

domain-specific vs generic data services

composability, interoperability and ease of use

The Rise of Web 2.0 New sites allow non-technical users to share information and interact in programmable environments Social Networking: MySpace, Bebo, Facebook GIS: Google Maps, Google Earth Preference Matching: Amazon Meta-clustering: digg, del.icio.us Information Publishing: Flickr

New sites allow non-technical users to share information and interact in programmable environments

Social Networking: MySpace, Bebo, Facebook

GIS: Google Maps, Google Earth

Preference Matching: Amazon

Meta-clustering: digg, del.icio.us

Information Publishing: Flickr

The Rise of Web 2.0 New sites allow non-technical users to share information and interact in programmable environments Social Networking: MySpace, Bebo, Facebook GIS: Google Maps, Google Earth Preference Matching: Amazon Meta-clustering: digg, del.icio.us Information Publishing: Flickr An army of curators, a world of information

New sites allow non-technical users to share information and interact in programmable environments

Social Networking: MySpace, Bebo, Facebook

GIS: Google Maps, Google Earth

Preference Matching: Amazon

Meta-clustering: digg, del.icio.us

Information Publishing: Flickr

An army of curators, a world of information

The Four Levels of e-Science Enlightenment 1) Resources: Providing access to a larger and wider diversity of resources 2) Automation: Increasing the automation and repeatability of experimentation 3) Collaboration: Allowing intra and cross disciplinary collaboration through enabling networks 4) Participation: Increasing access to a wider set of users and increasing knowledge in a domain by bringing new people to the subject

1) Resources: Providing access to a larger and wider diversity of resources

2) Automation: Increasing the automation and repeatability of experimentation

3) Collaboration: Allowing intra and cross disciplinary collaboration through enabling networks

4) Participation: Increasing access to a wider set of users and increasing knowledge in a domain by bringing new people to the subject

From DSs to VREs Virtual Research Environments bridge gap between middleware and users integrate functionality and facilities Harness interest in communities and make it easy to contribute and easy to benefit infrastructure annotation tools graphical environment

Virtual Research Environments

bridge gap between middleware and users

integrate functionality and facilities

Harness interest in communities and make it easy to contribute and easy to benefit

infrastructure

annotation tools

graphical environment

SEE-GEO: Geolinking Census DB Borders DB WFS GDAS OGSA-DAI getData getFeature geoLink Feature Portrayal GLS Portal Map Server Receive ticket for results Retrieve annotated image Store image on server Send parameterised query FPS Call out to existing FP service Cache attributes Stream polygons Request attributes Request features Run algorithm Stream relevant annotated polygons Concentrate on algorithm Access domain-specific data sets Utilise existing services Efficient delivery methods

Virtual Workspace for the Study of Ancient Documents An interface allowing browsing and searching of multiple image collections, including tools to compare and annotate the researcher’s personal collection

An interface allowing browsing and searching of multiple image collections, including tools to compare and annotate the researcher’s personal collection

Data 2.0: From Silos to Sharing Choose data based on stored metadata bring together for each user Build a community by providing tools to contribute back Manc Data Soton Data OD OD Choose Dataset Dataset Annotation VRE Portal Amy Annot. Add Annotation Edin Data OD Bob Annot. Central Annot.

Choose data based on stored metadata

bring together for each user

Build a community by providing tools to contribute back

Data 2.0: a new way of integrating data? Many diverse data sources independently owned and curated Many diverse users each sharing and utilising multiple datasets A personalised, virtual data warehouse bring together many sources to appear as one Allow shared, distributed, centralised, replicated annotation to build a community

Many diverse data sources

independently owned and curated

Many diverse users

each sharing and utilising multiple datasets

A personalised, virtual data warehouse

bring together many sources to appear as one

Allow shared, distributed, centralised, replicated annotation to build a community

What is the future of data? Data must be available to all to be useful Individuals must be able to harness the data to make it important to them The work you have seen today will help this happen Data 2.0 is not as far away as you think!

Data must be available to all to be useful

Individuals must be able to harness the data to make it important to them

The work you have seen today will help this happen

Data 2.0 is not as far away as you think!

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