CSLI 5350G - Pervasive and Mobile ComputingWeek 2 - Paper Presentation“Multi-scale query processing in vehicular networks”

Name: Dinesh BilimoriaDate: Sept 25th, 2013

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Research Paper

Bibliography:

T. Delot, S. Ilarri, M. Thilliez, G. Vargas-Solar, and S. Lecomte. {2011) “Multi-scale query processing in vehicular networks”. Journal of Ambient Intelligence and Humanized Computing, 2:213–226.

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Background

What is a Mobile Query? A request for information from a mobile device which can be either a push-based

or pull-based query.

What are some of the types of Mobile Query? Location Dependent Queries (LDQ) - queries whose answers depend on the

locations of certain moving objects. Spatiotemporal Queries (STQ) - include all queries that combine space and time

and generally deal with moving objects. Continuous Queries (CQ) - whose answer is automatically refreshed as needed,

in order to support the frequent changes/updates in the query results.

What is Multi-Scale Query Processing? Multi-scale query processing is any query processing that may need to access

data sources of different types to obtain the answer to a query.

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Multi-scale query processing in vehicular networks

Objective The goal is to provide query processing in a high dynamic vehicular

networks. Previous research relied on push model. Exploit different access modes (e.g., push, pull) and various data sources

(e.g., data cached locally, data stored by vehicles nearby, remote Web services, etc.) to provide the users with results for their queries.

Proposal Focus on the specific case of vehicular networks where vehicles can

exchange data among themselves to inform drivers about interesting events (e.g., available parking spaces, accidents, traffic congestions, etc.)

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Multi-scale query processing in vehicular networks

Example Retrieve the list of petrol stations located in a radius of 10 km around me

where fuel prices are less than 1$ (and update the result every 5 min).

radius of 10 km

Fuel Price = 92c

Fuel Price = 90c

Fuel Price = 1.01c

Data is coming from different sources

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Multi-scale query processing in vehicular networks

Contributions Problems are seen in V2V communications due to the high mobility of the

vehicles and the unreliability of the wireless communications in such a dynamic environment.

Not to focus on one particular access model but rather to consider multi-scale query processing, which implies exploiting the available data sources whatever the access mode (e.g., push or pull).

Assumptions Open world assumption, as opposed to closed world. Availability of multiple data sources Retrieve the maximum number of results interesting for the user with respect to one or

more criteria (e.g., result computation time, energy spent, financial cost, etc.)

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Multi-scale query processing in vehicular networks

Representation of Data Sources Represent the properties of each data source by storing information about

the different data sources accessible by the mobile device in an XML file Necessary to have precise information about the I/O parameters for each

data source Decompose the query into several local queries to execute on the data

sources The different data sources can be local or remote data sources For eg A local positioning service which provides to the user her/his location.

Two different data sources providing the location of the mobile device ie (1) a GPS interface and (2) a WiFi based positioning service An XML file stored locally on the mobile device The XML element connector allows to define how the data source can be

used.

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Multi-scale query processing in vehicular networks

Script Description of Data Sources

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Multi-scale query processing in vehicular networks

Optimization of Multi-Scale Mobile QueriesGeneration of all Possible Candidate Queries

1. Accuracy2. Average Availability3. Cost – based on time, money and energy4. Data Production Rate5. Response Time6. Update Frequency

Cost of Query for each dimension

Global Cost of Query where the goal is to minimize C(Q) for Optimization

Attributes for Optimization

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Multi-scale query processing in vehicular networks

Evidence Cost of a query can be based on time, money and energy which was proved

using a multi-scale query processor prototype.

Evaluation of Prototype Developed a prototype using Microsoft Language-Integrated Query (LINQ)

API to evaluate multi-scale query processing Benefits of using LINQ is the possibility to query different types of data

sources (a data structure, a Web service, a file system, or a database) Developed two types of external LINQ providers; (1) translate a LINQ query

into an equivalent HTTP GET request and (2) transform a LINQ query to a specific Web service using SOAP

Cost query optimizer selects Candidate Query 2 (CQ2) since cost is minimized

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Multi-scale query processing in vehicular networks

Shoulder of Giants This research was build on previous

study using VESPA – Vehicular Event Sharing with a mobile P2P Architecture.

Impact Cited by a new research paper

on context-aware routing vital for intelligent inter-vehicular communication (2012)

VESPA Prototype

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Multi-scale query processing in vehicular networks

Open problems New data management, dissemination and query processing techniques are

required to analyze the efficiency of context-based collaboration in VANETs. Also to resolve query and result routing problems

Discussion points What is a Mobile Query?

A request for information from a mobile device. What is Multi-Scale Query Processing?

Any query processing that may need to access data sources of different types.

What are some of the different types of data sources? A data structure, a Web service, a file system, or a database.