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Mobile Data Management Ken Rimey

Explore PDIS and µXMLDB for next-gen synchronization and efficient XML database solution on mobile devices. Study issues and methodology for managing files across multiple devices.

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Mobile Data Management Ken Rimey

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  1. Mobile Data ManagementKen Rimey 2003-2004: • Personal Distributed Information Store (PDIS) 2005-: • Too Many Boxes - end-user digital asset management for networked consumer electronic devices. • µXMLDB - an XML database for mobile devices.

  2. PDIS (2003-2004) • Next-generation data synchronization: Enable people to store copies of their data on several devices, and several computers, and to keep these copies in sync. • State-transfer, update-anywhere replicated XML database. • PIM data. • Digital media metadata. • Ported to Nokia’s Python for Series 60.

  3. TMB: Too Many Boxes • Ken Rimey, HIIT • How will consumers manage their files on multiple handheld devices, computers, and home/car entertainment boxes? • How to combine control with simplicity, rejecting both complete automation and complex graphical user interfaces? • Utilize PDIS/PDOS results to build a home network testbed. • 2005 volume: 4-5 person-years

  4. Hypothesized Issues • WHERE to store copies of files? • WHEN to synchronize them over wireless networks? • WHOse personal devices should have access?

  5. TMB Methodology • Build and study three prototypes: • Start with raw sharing of files among several phones and several computers, presumed to belong to the same person (1H2005). • Then add in multiple people (family members, etc.), end-user configurability, a focus on specific media types, a mockup of a home entertainment system, and some experimentation with gestural or multimodal interaction techniques (-2006). • Finally, capitalize on this infrastructure by integrating additional applications or devices, or by adapting it to some other setting besides the home (2007).

  6. µXMLDB: An XML Database for Mobile Devices • Kenneth Oksanen, HUT and HIIT • XML is of increasing interest as a vendor-neutral exchange and storage format in handheld devices. • Existing XML database solutions target large, administered servers and mostly text-oriented XML data. • Needed: A self-administering XML database with small memory and flash consumption, a simple vendor-neutral index creation API, and extensions for querying data-oriented XML. • 2005 volume: 3 person-years

  7. Research Topics • Index management • Query language • Representation of stored XML

  8. Index Management • Indexing of XML is not well understood. • Some systems build large generic indices. • Others allow specifying more accurate indices, but no standard API exists for this. • Specify intuitively with query templates: /todo[date="%s"] • Or automatically based on queries: /todo[date="20040922"] /todo[date="20040923"] • The latter leads to an opportunity to construct very concise and selective indices.

  9. Query Language • Based on XPath and possibly XQuery. • Extended for, among other things, categories: • The query /todo[isa(@category, "work")] matches the document <todo category="PDIS">...</todo>,if “PDIS” is defined as being work. • Categories change, and we don't want to reindex the whole database when they do. • Handling RDF and OWL is a possible further research issue.

  10. Representation of Stored XML • Allow use of compression. • Store big CDATA blobs separately.

  11. µXMLDB Deliverables • An open-source C implementation for Unix and Symbian OS. • SWIG interface supporting other languages. • Use existing system software (Berkeley DB, SQL, …) for persistence, storage and indexing.

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