Location-aware Query Process and

Location-based Services

Yang Gang HU

yghu1@student.monash.edu.au

Contents

• Motivation and Definition of LBS

• Technologies used in LBS

• Characteristics of LBS

• Location-aware Processing−Snapshot Query Processing−Continuous Query Processing

• Case Study: CareDB

Motivation

Application: Restaurant Finder

• Peter is driving from Melbourne to Sydney and he wants to know the nearest restaurant around his current position.

• So he sends a query request to the Restaurant Finder using his mobile phone,

• And the service returns the list of the nearest restaurant according to his current position via Global Positioning System (GPS).

Definition of LBS

• Supported by location-aware query processing (LAQP), location-based services (LBS) are information services accessible with mobile devices through the mobile network and utilizing the ability to make use of the location of the mobile device.

LBS Technologies

Five Components of LBS

• Mobile Device is used by end-users to communicate with service providers.

• Positioning Platform collects the position data from either the communication network or from GPS.

• Communication Network is responsible for two-way data transferring between end-users and service providers.

Five Components of LBS (Cont.)

• Service and Application Provider offers a number of location-based services to users.

• Data and Content Provider. In some cases, the application provider itself does not store and maintain the data, such as map and restaurant yellow pages.

Characteristics

• Mobile: provide services from different kinds of mobile devices.

• Context-aware: include preference- and context-aware like weather, road conditions and etc.

• Adaptive: the location-aware system is able to change its behaviour automatically according to different contexts of the objects.

Characteristics (Cont.)

• Secure: take good care of privacy.

• Current: support the dynamic and real-time query processing. Nobody wants to know the price of air tickets last year!

• High performance

Location-aware Processing

• Location-aware Snapshot Query Processing

• Location-aware Continuous Query Processing

Location-aware Snapshot Query Processing

• Snapshot past queries: “Find out all the moving objects were close to each other from 7:00 PM to 8:00 PM yesterday.” is a typical spatio-temporal query, which contains both the spatial dimension and the temporal dimension.

• Snapshot present queries require real-time query support and always accessed by continuous quires. “Find out the current location of a specific shuttle bus” is an example of this kind of query.

Location-aware Snapshot Query Processing (Cont.)

• Snapshot future quires. “Find out all the nearest restaurants after half an hour”. This kind of query is achieved by predicting the movement of the current objects and should only be valid for a limited period of time

Implementation

• Wang and Zimmermann [6] proposed a model for the location-aware snapshot query, which makes use of dual-index design. The model uses an on-disk R-tree index structure to store the necessary network connectivity information and utilizes an in-memory grid structure to efficiently maintain the position updates of the moving objects.

Implementation

• Wang and Zimmermann [6] proposed a model for the location-aware snapshot query, which makes use of dual-index design. The model uses an on-disk R-tree index structure to store the necessary network connectivity information and utilizes an in-memory grid structure to efficiently maintain the position updates of the moving objects.

Location-aware Continuous Query Processing

• requires spatial-temporal techniques to handle continuous moving objects

• keep refreshing the objects’ status within the monitoring ranges of mobile queries.

• An example of this kind of query is “keep tracking all the vehicles within 1 miles of a police vehicle”.

Implementation (4 methods)

• Result Validation.

• Result Caching

• Result Prediction

• Incremental Evaluation

Method One: Result Validation

• used to associate a validation condition with each query answer

• Two types:− valid time: how long the query answer is

valid for the next specific period of time units

− valid region: in which region the query answer is valid

Method Two: Result caching

• Cache the similar query answers

• Retrieve more data and cache them for later use

Method Three: Result Prediction

• The query answer can be predicted in advance since the future trajectory movement is in a regular pattern

• Once the trajectory changes, the query should be re-evaluated.

Method Four: Incremental Evaluation

• Incremental evaluation means that the continuous query is only evaluated once and only the updates of the answer need to be evaluated.

• requires continuously listen on the notifications that some objects are out of the boundary.

Case Study: CareDB

• A preference- and context-aware database server

• redefine the query answers of the current existing location-aware query processing

• only the expected answers are returned

Capability

• Existing stored data: a traditional relational database.

• Collecting the preferences and current context of an object.

• Collecting the surrounding global context, such as location, weather, and time.

Architecture

Three types of Context

• User preferences and context. It allows valid users to specify their preferences.

• DB-specific context. E.g. restaurant information database, hotel database and golf club information database

• Environmental context. Mainly third-party context. E.g. weather, time, road traffic and etc.

Two Main Modules

• Query Rewriting Module−Receive snapshot or continuous query−Check which context should be considered

• Preference- and Context-Aware Query Processing and Optimization Module−Construct the operator and process the

query−Optimize

References• Virrantaus, K. et al. Developing GIS-Supported Location-Based Services,

Second International Conference on Web Information Systems Engineering (Wise’01) Volume 2, December 3 – 6, 2001.

• H. G. Elmongui. Query optimization for spatio-temporal data stream management systems. The SIGSPATIAL Special, Volume 1, Number 1, March 2009.

• Shiode, et al. The impact and penetration of Location Based Services.2004.

• J. Nord, et al. An Architecture for Location Aware Applications.2002.

• M. F. Mokbel and W. G. Aref. Location-aware Query Processing and Optimization.2007.

• H. Wang and R. Zimmermann. Snapshot Location-based Query Processing on Moving Objects in Road Networks. ACM GIS ’08, November 5-7, 2008.

References(Cont.)• M. F. Mokbel and J. J. Levandoski. Toward Context and Preference-

Aware Location-based Services. MobiDE’09, June 29, 2009.

• N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. In SIGMOD Conference, 1990.

Thank you!