data access models in location dependent information services
DESCRIPTION
Data Access Models in Location Dependent Information Services. Yu Meng May 1, 2004. Outline. Introduction Related concepts Location models Query types Valid scopes Access models On-demand Access Broadcasting Summary. Introduction. What is LDIS What are the challenges - PowerPoint PPT PresentationTRANSCRIPT
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Data Access Models in Location Dependent Information Services
Yu Meng
May 1, 2004
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Outline
• Introduction• Related concepts
– Location models– Query types– Valid scopes
• Access models– On-demand Access– Broadcasting
• Summary
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Introduction
• What is LDIS
• What are the challenges
• Cellular Architecture for LDIS
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Introduction
• Provide local or nonlocal news, weather, traffic reports, navigation maps and directory services in wireless environment.
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Introducton
• Mobile environment constraints,
• Spatial data,
• User movement.
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Introduction
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Related Concepts
• Location Models
• Query types
• Valid scope
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Related Concepts -Location Models
• Geometric model.– Latitude-longitude pair returned by GPS.
– Advantage: good for heterogeneous system,
– Disadvantage: costly in terms of data volume
• Symbolic model.– Real-world entities.
– Logical entities
– Advantage: easy to manage data with well organized structures.
– Disadvantage: hard to convert among heterogeneous systems.(good topic for RFC)
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Related Concepts -Query types
• Local vs. non-local queries.– “Tell the local weather”,– “Find the weather in New York City”.
• Simple vs. general queries.– “Download the local traffic report”,– “List the hotels within 30 miles”,– “List the hotels with a room rate below $100”.
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Related Concepts -Valid Scope
• The area or areas within which the query result is valid.
• Data object returned: (query, result, vs)– (nearest-hotel, A, vs), – (nearby-restaurant, {A,B}, {1,2}).
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Related Concepts –Valid Scope Example
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Data Access Models
• On-demand access
• Broadcasting
• Hybrid of the two.
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On-demand Access
• Data placement,
• Data replication,
• Query scheduling,
• Indexing.
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On-demand Access-Data Placement
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• The system creates certain copies of the data and places them at different locations in the network.
• Work done are based on network topology and access patterns.
• Problem: Access patterns may be time dependent periodically or temporally. Is EMM a solution?
On-demand Access-Data replication
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• Query scheduling determines query processing order.
• Work has been seen in improving average queuing delay.
• What happens if client moves?
• Is prediction a solution?
On-demand Access-Query scheduling
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On-demand Access-Query Scheduling
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• Disk indexing• Geometric location model: MBR based indexing.
May be inefficient caused by overlapping.• Symbolic location model: mapping to valid data
object is needed.• Several R tree based algorithms are proposed but
none works superior to others in all cases.
On-demand Access-Indexing
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On-demand Access-Indexing
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On-demand Access-Indexing
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On-demand Access-Indexing
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Broadcast
• Broadcast lets an arbitrary number of users simultaneously access data.
• Good for simple queries.
• Hard for general queries.
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Broadcast-Air indexing
• Client can download a indexing info to predict availability of queried data.
• Indexing size and latency.
• Broadcasting strategy: how to divide bandwidth? Based on the statistics.
• Not adaptive!
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Data Caching
• Data may be cached at the mobile clients for better performance.
• Data consistency: – Location dependent cache invalidation.– Time dependent cache invalidation.
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• LRU
• P/X
• Distance based algorithm
• Valid scope
Data Caching-Data Replacement
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• Feasible for simple queries.
• May be hard for general queries.
• Not much work on this issue.
Data Caching-Data Prefetching
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Summary
• LDIS is a developing technology.
• Many research opportunities remains.
• SPOT (Smart Personal Objects Technology ) announced by Microsoft in 2003