An IEEE ICDE 2000 Tutorial on. Mobile and Wireless Database Access for Pervasive Computing. Panos K. Chrysanthis University of Pittsburgh & Carnegie Mellon University Evaggelia Pitoura University of Ioannina firstname.lastname@example.org email@example.com. Outline. Motivating Example
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An IEEE ICDE 2000 Tutorial on
Mobile and Wireless Database Access for Pervasive Computing
Panos K. Chrysanthis
University of Pittsburgh & Carnegie Mellon University
University of Ioannina
that your next appointment is in 1 hour.
you get home in the evening.
palmtops, wristwatch PC/Phone, walkstations
Example: Fidelity (degree to which a copy of data matches thereference copy at the server)
Where should support for mobility and adaptability be placed?
(-) applications must be re-written which may be very complicated
(-) no focal point of control to resolve potentially incompatible application demands or to enforce limits on resource usage
(+) existing applications continue to work unchanged
(-) too general, cannot take advantage application semantics
(-) may not be attainable (e.g., during a long disconnection)
at all sites
At selective sites (e.g., at frequent callers)
the whole network
Always update (at each movement)
Maintain a hierarchy of location registers (databases)
A location database at a higher level contains location information for all objects below it
(+)No pre-assigned HLR
(-)Increased number of operations (database operations and communication messages)
(-)Increased load and storage requirements at the higher-levels
(large Call to Mobility Ratio)
Most current research assumes a centralized location database
How to model the location of moving objects?
Dynamic attribute (its value change with time without an explicit update) [e.g., in MOST]
For example, dynamic attribute A with three sub-attributes: A.value, A.updatetime and A.function
(function of a single variable t that has value 0 at time t=0)
How to represent and index moving objects?
Goal : Maximize query capacity of servers, minimize energy per query at the client.
Focus: Read-only transactions (queries).
1. Pull: PDAs demand, servers respond.
2. Push: Network servers broadcast data, PDA's listen.
3. Combinations Push and Pull (Sharing the channel).
A B C
A A B C
A B A C
(+) "listening/tuning time" decreases
(-) "access time" increases
Trade-Off: Size means Miss factor
Trade-Off: Size means Miss factor
Probability of access (P)/Broadcast frequency (X).
- pt: Access Probability (P) * period (T) before page appears next
- A broadcast page b replaces the cached page c with lowest pt value
abort T, if x є RS(T) appears in the invalidation report
abort T, if ts(x) > tso(T)
A signature is a compressed checksum similar to the one used for file comparison.
... several consistency models
begin (first read)
(Earliest Deadline First) algorithm + batching
E.g., clustering of queries [Ren 99]
a technique to reduce the cost of cache misses during disconnection.
That is, load before disconnect and be ready.
E.g., tree-based in file systems, access paths (joins) in DBs
E.g., Create View with Update-On clause [Lauzac 98]
OO query to describe hoarding profiles [Gruber 94]
reformulates C so that M accepts a local constraint C’ instead
Let x and y be two data items at two nodes.
C = J.x + K.y > D is a global constraint.
Localization yields two local constraints:
x > L1 and y > L2
where L1 and L2 are constants chosen such that J.L1 + K.L2 > D
Re-localization: L1, L2 can be changed: node y increases L2 before node x decreases L1
Goals: sharing of partial results while in execution,
maintaining computation state on a fixed host,
moving transactions on/off a mobile host and across fixed hosts.
A pessimistic approach: Exotica
and two-tier replication.
Mobile and wireless computing attempts to deliver today’s and tomorrow’s applications on yesterday’s hardware and communication infrastructure!