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

• Cellular Architecture for LDIS

Introduction

• Provide local or nonlocal news, weather, traffic reports, navigation maps and directory services in wireless environment.

Introducton

• Mobile environment constraints,

• Spatial data,

• User movement.

Introduction

Related Concepts

• Location Models

• Query types

• Valid scope

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)

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”.

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}).

Related Concepts –Valid Scope Example

Data Access Models

• On-demand access

• Broadcasting

• Hybrid of the two.

On-demand Access

• Data placement,

• Data replication,

• Query scheduling,

• Indexing.

On-demand Access-Data Placement

• 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

• 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

On-demand Access-Query Scheduling

• 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

On-demand Access-Indexing

On-demand Access-Indexing

On-demand Access-Indexing

Broadcast

• Broadcast lets an arbitrary number of users simultaneously access data.

• Good for simple queries.

• Hard for general queries.

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!

Data Caching

• Data may be cached at the mobile clients for better performance.

• Data consistency: – Location dependent cache invalidation.– Time dependent cache invalidation.

• LRU

• P/X

• Distance based algorithm

• Valid scope

Data Caching-Data Replacement

• Feasible for simple queries.

• May be hard for general queries.

• Not much work on this issue.

Data Caching-Data Prefetching

Summary

• LDIS is a developing technology.

• Many research opportunities remains.

• SPOT (Smart Personal Objects Technology ) announced by Microsoft in 2003