From Personal Desktops to Personal Dataspaces: A Report on Building the iMeMex Personal Dataspace Ma

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From Personal Desktops to Personal Dataspaces A
Report on Building the iMeMex Personal Dataspace
Management System

• Jens Dittrich Lukas Blunschi
  Markus Färber Olivier Girard Shant
  Karakashian Marcos Vaz Salles
• BTW 2007

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A World of Data Silos

• gt 80 of data outside of relational databases
• Documents, spreadsheets, presentations
• Web pages
• Email, instant messages, news feeds
• Images, audio, video
• Specialized systems for many of the data types
  (filesystems, web/email servers, DBMSs)
• Lack of unified services over ALL the data

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Dataspace

• The complete set of information (documents,
  emails, images, etc) belonging to one
  organization or task
• Examples
• Personal dataspaces ? your messages, your family
  photos
• Enterprise dataspaces ? all information about a
  key customer
• Scientific dataspaces ? all information about one
  given research project
• Includes a set of data sources and relationships
  among pieces of information in the sources

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Dataspace Management System

• New system abstraction
• A hybrid of
• Search Engine
• Database Management System
• Information Integration System
• Data Sharing System
• Offers services on ALL the data
• Keyword and structural search to start with
  (baseline)
• Provides pay-as-you-go information integration
• Model data relationships and their evolution
• However, does not acquire full control of data
• System does not own the data

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Projects on Dataspaces

• Vision Paper on Dataspaces
• Mike Franklin (UC Berkeley), Alon Halevy (U Wash
  / Google), David Maier (U Portland).
• From Databases to Dataspaces A New Abstraction
  for Information Management. SIGMOD Record,
  December 2005.
• ETH Zürich iMeMex
• UC Berkeley (Shawn Jeffrey) and Google (Alon
  Halevy)
• U Portland (David Maier)
• Purdue U (Nehme, Elke Rundensteiner, et. al.)

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Our Focus Personal Dataspaces
Great applications, but information
integration is done by the user
User
Applications
PDSMS
Data Sources
Email Server
Web Server
PC
iPod
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So far...

• Vision Dataspaces (VLDB 2005, SIGIR PIM 2006)
• To come...
• Data model single framework for different types
  of data (VLDB 2006)
• System Architecture Mediation / Warehousing
  (CIDR 2007, BTW 2007)
• Pay-as-you-go information integration (ongoing
  work)

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Characteristics of Personal Data

• Non-schematic
• Heterogeneous collections, no formally defined
  schema
• Several possible serializations
• Hundreds of file formats, different encodings
• Contains arbitrary graphs
• References within documents (LaTeX/Word),
  filesystem links
• Distributed among different data sources
• Filesystem, email servers, web servers,
  databases, iPod
• Infinite
• RSS, ATOM, email streams

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Data Model Options
Extension XLink/ XPointer
Specific schema
View mechanism
Extension ActiveXML
Extension Document streams
Extension Relational streams
Extension XML streams
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Data Models for Personal Information
Abstraction Level
lower
higher
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iDM iMeMex Data Model

• Our approach get the data model closer to
  personal information not the other way around
• Supports
• Unstructured, semi-structured and structured
  data, e.g., filesfolders, XML, relations
• Clearly separation of logical and physical
  representation of data
• Arbitrary directed graph structures, e.g.,
  section references in LaTeX documents, links in
  filesystems, etc
• Lazily computed data, e.g., ActiveXML (Abiteboul
  et. al.)
• Infinite data, e.g., media and data streams

See VLDB 2006
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iDM Lazily Computed Graph

• Nodes and edges are lazily computed
• Each node is a Resource View

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iDM Lazily Computed Graph

• Behind the scenes, obtaining the content may
• Read a file on the filesystem
• Access a page on the web
• Fetch the data from an index structure
• Behind the scenes, obtaining the group may
• Get the children of a folder in the filesystem
• Look up an edge replica
• Obtain the sections of a document

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How to implement iDM Architectural Perspective
Complex operators (query algebra)
IndexesReplicas access (warehousing)

• Data source access (mediation)

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Further Research Challenges in Dataspace
Management Systems

• Pay-as-you-go information integration
• Model relationships in the dataspace
• Examples semantic equivalences, lineage
  relationships
• Distributed Dataspaces
• Query language specification (iQL)

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iMeMex Prototype Implementation

• iMeMex Prototype
• 780 classes
• 70,900 LOC
• Java-based supported on Linux, Mac and Windows
• OSGi-based Everything is a Plug-in ( 52
  bundles)
• Open-source (Apache 2.0) http//www.imemex.org
• Team
• Advisor
• Two Ph.D. students
• Three M.Sc. students
• Thirteen Semester Project students

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Conclusions

• Dataspace Management Systems are a new system
  abstraction
• iMeMex is among the first implementations of this
  new breed of systems our focus Personal
  Dataspaces
• Dataspace Management Systems call for
• New data model
• New system architecture
• New capabilities for pay-as-you-go information
  integration
• More information http//www.imemex.org

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Questions? Thanks in Advance for your Feedback! ?
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Backup Slides
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Personal Dataspaces Literature

• Dittrich, Vaz Salles, Kossmann, Blunschi. iMeMex
  Escapes from the Personal Information Jungle
  (Demo Paper). VLDB, September 2005.
• Dittrich, Vaz Salles. iDM A Unified and
  Versatile Data Model for Personal Dataspace
  Management. VLDB, September 2006
• Dittrich. iMeMex A Platform for Personal
  Dataspace Management. SIGIR PIM, August 2006.
• Blunschi, Dittrich, Girard, Karakashian, Vaz
  Salles. A Dataspace Odyssey The iMeMex Personal
  Dataspace Management System (Demo Paper). CIDR,
  January 2007.
• Dittrich, Blunschi, Färber, Girard, Karakashian,
  Vaz Salles. From Personal Desktops to Personal
  Dataspaces A Report on Building the iMeMex
  Personal Dataspace Management System. BTW, March
  2007