Dataspaces: Progress and Prospects - Peoplefranklin/Talks/BNCOD09.pdf · Dataspaces: Progress and...

M. Franklin BNCOD 2009 7 July 2009

Dataspaces: Progress and Prospects

Michael J. Franklin UC Berkeley & Truviso

BNCOD July 7, 2009


Dataspaces: Progress and Prospects

Michael J. Franklin UC Berkeley & Truviso

BNCOD July 7, 2009

Dataspace: The Final Frontier?


Outline

•  Dataspaces – some history •  Dataspaces – what are they, really? •  Some emerging examples •  Example technologies •  What’s missing? •  What’s next?


The SIGMOD Credo

Codd made relations, all else is the work of man.

Leopold Kronecker (paraphrased by Raghu Ramakrishnan?)


The Politics of Dataspaces •  Roots: CIDR 2005 Conference

–  “Gloom and Doom” panel –  David Dewitt’s call for a unifying goal – Juxtaposed with lots of great work across the

web, new devices, scalable computing, …


An Aside: The cycle of DB Angst

Did we “miss the boat” on something cool?

Are we polishing a “round ball”?


Dataspaces: Timeline •  CIDR 2005 (January) •  A small group started looking for

commonality and a “grand challenge” •  We put a name on it. •  Ran an early draft by an impromptu group

of advisors at SIGMOD 2005 (June 05). •  Wrote it up for SIGMOD Record (Dec 05)

[Franklin, Halevy, Maier] •  Kept working on pretty much what we

were already doing!


What’s in a name?


Dataspaces – what are they?


Dataspaces Inclusive

Deal with all the data of interest – in whatever form

Co-existence not Integration No integrated schema, no single warehouse,

no ownership required Pay-as-you-go

– Keyword search is bare minimum. – More function and increased consistency

as you add work.


Compare to Data Integration

A quintessential schema-first approach.

wrapper wrapper wrapper wrapper wrapper

Mediated Schema

Semantic mappings

Courtesy of Alon Halevy


Structured Data Management


A “Modern” View of Data Management


The Structure Spectrum

Structured (schema-first)

Relational Database

Formatted Messages

Semi-Structured (schema-later)

XML Tagged

Text/Media

Unstructured (schema-never)

Plain Text Media


Whither Structured Data? •  Conventional

Wisdom: only 20% of data

is structured.

•  Decreasing due to: – Consumer

applications – Enterprise search – Media applications


But Structure Matters! Functionality

Time (and cost)

Structured (schema-first)

Unstructured (schema-less)

Dataspaces (pay-as-you-go)

Structure enables computers to help users manipulate and maintain the data.


An Alternative View

Strong Weak

Strong

Weak

Desktop Search

Web Search Virtual

Organization

Federated DBMS

DBMS

Semantic Integration

Administrative Control


Some Interesting Points on the Structure Spectrum


Web-scale Structured Data�

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The Future of Analytics •  Analytics traditionally a

key DB use case – Need to understand

data to manipulate it •  “Barbarians at the Gate”

– Procedural cloud-based approaches gaining interest

– Scalability for massive data sets – But, we’ve seen this movie before!


The View From the Clouds •  “Pig Latin” [Olston et al. SIGMOD 08]

– Why have a schema? 1) Transactional (referential?) Consistency 2) Fast point look ups through indexes 3) Curation for future (other) users

–  Flexible, optional, nested data model –  Data remains in files (no admin)

•  “Column Family” models of BigTable, Hbase, Cassandra, CouchDB, …

•  “Schema on Read”? == Errors on Read?


Other Examples Personal Information Management(iMemex),

Question answering, Scientific Collaboration


Outline

•  Dataspaces – some history •  Dataspaces – what are they, really? •  Some emerging examples •  Example technologies •  What’s missing? •  What’s next?


DataSpace Technology

•  Probabilistic Databases •  Schema Matching •  Judicious use of User Input •  Approx. Query Answering •  Uncertainty Management •  Data Model Learning •  Provenance and Annotation •  Structured + Unstructured Search


Roomba: Soliciting User Feedback* •  A “web 2.0” spin on Reference

Reconciliation. –  Inspired by “ESP Game” for image labeling by

Von Ahn & Dabbish; “MOBS” architecture by Doan et al.

•  Use automated techniques to generate candidate matches.

•  Ask users to confirm. •  Problem: which matches are most important?

* “Soliciting User Feedback in a Dataspace System”, Shawn Jeffery, Michael Franklin, Alon Halevy; SIGMOD 2008.


Roomba Overview

•  Based on Value of Perfect Information (VPI) (see Russell and Norvig)

•  Choose matches that provide largest increase in dataspace utility.

•  Must consider: Query Workload, # Records per Term, and Confidence of Matches.


Roomba: Sample Result Perfect Knowledge

VPI-Based Ordering


Data Integration at Web-scale�•  A typical data integration solution is impractical for

web-scale data –  Too many domains of interest (Web Data is about

everything) –  Huge number of sources for each domain –  Designing Mediated Schema is infeasible –  Data sources are dirty, incomplete and lack of meta-data

•  Solution: A Data Integration Solution that is –  Automated –  Best Effort –  Pay-as-you-go

“Functional Dependency Generation and Applications in Pay-as-you-go Data Integration Systems” WebDB 2009 Wang, Dong, Das Sarma, Franklin, Halevy


Probabilistic Functional Dependencies (pFDs)�

•  Idea - use probabilistic Functional Dependencies to guide automated approaches –  Normalize mediated schemas –  Identify low quality data sources

•  Definition of a probabilistic FD (pFD) X p A, p is the likelihood of FD holds in general

•  “Learn” pFDs by counting data and schema instances –  Note: this will get you a bad grade in your database course.

•  Related work –  TANE, CORDS –  Conditional Functional Dependences


Results for pFDs Generation Algorithms on “Web Tables”�

Fidelity of generated FDs with confidence 0.8 with “golden standard” FDs


Normalizing a Mediated Schema�•  Generating the minimal pFD-set

–  Prune low-probability pFDs –  Prune pFDs that can be generated by transitivity

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Results for Schema Normalization�


PayGo Quality Metrics�

•  Measuring quality of data sources •  Measuring and Improving quality of a integration

(e.g. mediated schema, schema mapping, etc.)

•  FD-based Quality measuring framework is an example: –  Identify Dirty Data sources –  Improving Mediated Schema


What’s Missing? •  Metrics!!!!

–  Key idea: you pay more to get better data. Must define “better”!

–  Application-, user-, context-dependent –  Relation to Data Quality work

•  Benchmarks –  Key to progress

•  Support for collaboration/data-sharing/visualization –  Particularly with uncertainty in base data and inferences

•  More data/media types •  Focus on “serious” analytics workloads •  …Your ideas here…


Metcalf’s (not Moore’s) Law will drive future DBMS inovation

Data Center

EDGE

Data Warehouse

Inventory

PoS ERP

• More connectivity means more data to integrate.

• Dataspace-style techniques will play an ever-larger role.


Conclusions •  More connectivity means more data. •  Many would simply throw away the benefits

of structure due to “schema-first” problems. •  Dataspaces provide a framework for

intelligent use of structural information. •  Could also meet the goal of a “grand

challenge” for the DB Community.

As an inherently unsolvable problem…

Dataspace may, in fact, be the final frontier.

Dataspaces: Progress and Prospects - Peoplefranklin/Talks/BNCOD09.pdf · Dataspaces: Progress and...

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