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Ad hoc data integration for mobile GIS applications

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Title: PhD Colloquium Subject: Adhoc Data Integration for Mobile GIS Application Keywords: Information Extraction, Data Integration, Retrieval, Mobile GIS – PowerPoint PPT presentation

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Title: Ad hoc data integration for mobile GIS applications


1
Ad hoc data integration for mobile GIS
applications
  • Ramya Venkateswaran
  • (ramya_at_geo.uzh.ch)

2
Contents
  1. Scenario
  2. Research Objective
  3. Introduction Overview of the GenW2 project
  4. Motivation Why is Ad hoc Data Integration
    needed?
  5. State of the Art
  6. Research Questions Discuss 3 research questions
  7. Methods TourGuide and friends
  8. Next Steps Data Enrichment and Quality control

3
Scenario
1
4
Scenario of Usage
I will be vacationing in Paris and I want to
visit some of the famous palaces, History related
places and other tourist locations in Paris
Tourist Travel Websites
?
Other Sources
Recommendations from
Albums Images
People
Tourist Guides
5
Scenario of Usage
Id still like to go to Paris..
Tourguide
?
Other Sources
Recommendations from
Tourist Travel Websites
Albums Images
People
Tourist Guides
6
Research Objective
2
7
Objective of my research
Ad hoc Data Integration
  • Data quality control
  • Completeness
  • Correctness
  • Credibility
  • User feedback
  • Data Integration
  • Flavour Based integration
  • Ad hoc DI vs. Traditional DI
  • TourGuide
  • Data enrichment
  • POI Enrichment
  • Website credibility

8
Overview and Introduction
3
9
Overview of the GenW2 Project
  • Short for Generalization for portrayal in Web
    and Wireless mapping
  • Develop new methods for web and wireless mapping
  • Focus on
  • ad hoc integration of heterogeneous information
  • on-the-fly map generalization in a mobile context.

10
The GenW2 Framework
11
The GenW2 Framework
12
The GenW2 Framework
13
Types of Data sources
MRDB Facts DB
Image metadata
  • Web
  • services

Web pages
Static datasets
14
Motivation - Why is Ad hoc Data Integration
needed?
4
15
Motivation
  • So many data sources and so little structure
  • Web as a database Too much information to
    ignore!
  • Ad hoc integration Need based according to
    scenario and flavour, unlike search engines.
  • Importance of recording certain facts that can
    enrich the MRDB and the integration process.

16
State of the art
5
17
Relevant Domains
Recommendation Systems
Information Filtering
Information Retrieval
Collaborative Filtering
Ad hoc Data Integration
18
State of Art
Ad hoc Data Integration
  • Data quality control
  • Completeness
  • Correctness
  • Credibility
  • User feedback
  • Data Integration
  • Flavour Based integration
  • Ad hoc DI vs. Traditional DI
  • TourGuide
  • Data enrichment
  • POI Enrichment
  • Website credibility

19
Integration, IR and decision systems
  • Different concepts and methods in Data
    Integration
  • Data Integration from multiple sources
  • Geospatial data mining and integration. (Knoblock
    et al. 2001, Michalowski et al., 2004)
  • Mashup web data for overall importance of
    landmarks. (Grabler et al., 2008)
  • SPIRIT Design, techniques and implementation
    (Purves et al., 2007, Jones et al., 2002, Bucher
    et al., 2005)
  • Geo parsing, geo coding and IR techniques (Clough
    et al., 2005)

20
Integration, IR and decision systems
  • Methods for marking tourist locations and a guide
    that is 'context aware'. (Abowd et al., 2004)
  • Activity based model of decisions that are
    affected based on activity-travel behavior and
    also predict the activities. (Arentze and
    Timmermans, 2004)
  • Voluntary information from a community,
    collaborative semantics, recommendation systems
    (Schlieder , 2007)

21
Data Enrichment
  • Methods and algorithms for the provision of
    auxiliary data and its use for controlling an
    automated adaptive generalization process (Neun,
    2007)

22
Data quality and assessment
  • Framework for efficient and accurate integration
    of geospatial data from a large number of sources
  • Positional accuracy, completeness (Thakker et
    al., 2007)
  • VGI (Volunteered Geographic Information) Trust
    models for Gazetteers (Keßler et al., 2009)

23
Observations from literature
  • Considerable work and methods for traditional
    data integration, variety of methods in IR and
    GIR
  • Lesser work and methods for data integration from
    multiple and dynamic sources (Focus on semantics
    rather than data and context) and recording
    reusable facts.
  • Considerable work on user modeling, activities
    and activity recommendation
  • Data enrichment work for improving generalization

24
Challenges
  • Datasets are not static and are dynamic and
    heterogeneous
  • Auxiliary data
  • Determining parameters (user categories,
    activities habits etc, not a single user or set
    of preferences)
  • Point of complete integration
  • Methods to test and evaluate the effectiveness

25
Research Questions
?
6
26
RQ1 Flavour Based Integration
  • Given an activity and unrelated data that is
    heterogeneous and dynamic, what is an effective
    method of data integration, so that the results
    are streamlined towards information about events
    and places for a set of users?
  • Flavour based data integration from various
    sources
  • Ad hoc DI vs. Traditional DI
  • Tour guide An example of web data integration

27
RQ2 Data Enrichment
  • How can the Generalization for portrayal in Web
    and Wireless mapping (GenW2) framework record and
    exploit valuable reusable information, obtained
    from the preceding data integration?
  • Facts DB
  • Activity-Location pairs
  • Data source credibility (Keßler et al., 2009)
  • User feedback

28
RQ3 Quality of data
  • What are the different metrics that can be used
    to control and/or assess the quality of the
    integrated data?
  • Measurement of Quality?
  • Quality of data by completeness (Thakkar et al.,
    2007)
  • Quality of data by correctness (Thakkar et al.,
    2007)
  • Another metric for Quality Assessment
  • Quality of data by collective user feedback
  • Credibility rank of information sources (Keßler
    et al., 2009)
  • Evaluation Methodology

29
Methods
7
30
Flavour Based Data Integration
Recommendation Systems
Information Filtering
Information Retrieval
Collaborative Filtering
31
Definition - Flavour Based Data Integration
Recommendation Systems
Information Filtering
Information Retrieval
Collaborative Filtering
a field of study designed for creating a
systematic approach to extracting information
that a particular person finds important from a
larger stream of information (Canavese, 1994).
the goal of an information retrieval system is
for the user to obtain information from the
knowledge resource which helps her/him in problem
management (Belkin, 1984)
use the opinions of a community of users to help
individuals in that community more effectively
identify content of interest from a potentially
overwhelming set of choices (Resnick and Varian
1997).
The central idea here is to base personalized
recommendations for users on information obtained
from other, ideally likeminded, users. (Billsus
and Pazzani, 1998).
32
Definition - Flavour Based Data Integration
Recommendation Systems
Information Filtering
Information Retrieval
Collaborative Filtering
a field of study designed for creating a
systematic approach to extracting information
that a particular person finds important from a
larger stream of information (Canavese, 1994).
the goal of an information retrieval system is
for the user to obtain information from the
knowledge resource which helps her/him in problem
management (Belkin, 1984)
use the opinions of a community of users to help
individuals in that community more effectively
identify content of interest from a potentially
overwhelming set of choices (Resnick and Varian
1997).
The central idea here is to base personalized
recommendations for users on information obtained
from other, ideally likeminded, users. (Billsus
and Pazzani, 1998).
33
Definition - Flavour Based Data Integration
Recommendation Systems
Information Filtering
Information Retrieval
Collaborative Filtering
a field of study designed for creating a
systematic approach to extracting information
that a particular person finds important from a
larger stream of information (Canavese, 1994).
the goal of an information retrieval system is
for the user to obtain information from the
knowledge resource which helps her/him in problem
management (Belkin, 1984)
use the opinions of a community of users to help
individuals in that community more effectively
identify content of interest from a potentially
overwhelming set of choices (Resnick and Varian
1997).
The central idea here is to base personalized
recommendations for users on information obtained
from other, ideally likeminded, users. (Billsus
and Pazzani, 1998).
34
Definition - Flavour Based Data Integration
Recommendation Systems
Information Filtering
Information Retrieval
Collaborative Filtering
a field of study designed for creating a
systematic approach to extracting information
that a particular person finds important from a
larger stream of information (Canavese, 1994).
the goal of an information retrieval system is
for the user to obtain information from the
knowledge resource which helps her/him in problem
management (Belkin, 1984)
use the opinions of a community of users to help
individuals in that community more effectively
identify content of interest from a potentially
overwhelming set of choices (Resnick and Varian
1997).
The central idea here is to base personalized
recommendations for users on information obtained
from other, ideally likeminded, users. (Billsus
and Pazzani, 1998).
35
Definition - Flavour Based Data Integration
Recommendation Systems
Information Filtering
Information Retrieval
Collaborative Filtering
a field of study designed for creating a
systematic approach to extracting information
that a particular person finds important from a
larger stream of information (Canavese, 1994).
the goal of an information retrieval system is
for the user to obtain information from the
knowledge resource which helps her/him in problem
management (Belkin, 1984)
use the opinions of a community of users to help
individuals in that community more effectively
identify content of interest from a potentially
overwhelming set of choices (Resnick and Varian
1997).
The central idea here is to base personalized
recommendations for users on information obtained
from other, ideally likeminded, users. (Billsus
and Pazzani, 1998).
36
Flavour Based Data Integration
Recommendation Systems
Information Filtering
Information Retrieval
Collaborative Filtering
a field of study designed for creating a
systematic approach to extracting information
that a particular person finds important from a
larger stream of information (Canavese, 1994).
the goal of an information retrieval system is
for the user to obtain information from the
knowledge resource which helps her/him in problem
management (Belkin, 1984)
use the opinions of a community of users to help
individuals in that community more effectively
identify content of interest from a potentially
overwhelming set of choices (Resnick and Varian
1997).
The central idea here is to base personalized
recommendations for users on information obtained
from other, ideally likeminded, users. (Billsus
and Pazzani, 1998).
37
Keyphrases in FBDI
  • Systematic approach to extracting information
  • Obtain information from one or many knowledge
    resource/s
  • Recommendations for user groups or user
    categories
  • Opinions of a community of users
  • Keyword, flavour or activity such as tourism,
    history, sport, culture, shopping etc

38
Definition of FBDI
  • FBDI is an activity based, systematic approach to
    extract and integrate information from multiple
    knowledge sources depending on habits of certain
    user groups or user categories, capable of
    learning over time.
  • Flavour typical activities of a certain user
    group
  • Examples Tourism, Shopping, Sports, Historical
    excursions, Cultural excursions etc

39
Demo
Click me!
40
The GenW2 Framework
41
The GenW2 Framework
42
Adaptive tour guide for Paris
  • Flavour Based Integration with web as datasource
  • Only web as the database (Grabler et al.,2008 )
  • Integration of data on
  • Tourism
  • Transport
  • User feedback
  • User Rating
  • Facebook profile
  • Dopplr profile
  • Scheduler

43
Data Integrator
  • Example of web data integration
  • Functional components (Baumgartner et al., 2009)
  • Web interaction component
  • Lonelyplanet, wikitravel, virtualtourist,
    tripadvisor and official tourist website
  • Wrapper generator
  • OpenKapow Robomaker
  • Data transformer
  • DOM parser for RSS and XML formats

44
The GenW2 Framework
45
Data Integrator
  • Example of web data integration
  • Functional components (Baumgartner et al., 2009)
  • Web interaction component
  • Lonelyplanet, wikitravel, virtualtourist,
    tripadvisor and official tourist website
  • Wrapper generator
  • OpenKapow Robomaker
  • Data transformer
  • DOM parser for RSS and XML formats

46
The GenW2 Framework
47
Web data Extraction
  • Semi automatic wrappers
  • Automatic wrapper Induction
  • WIEN (Kushmerick et al., 1997)
  • Stalker (Muslea et al., 2001)
  • DEBye (Laender et al., 2000)
  • Commercial
  • RoboMaker (Kapow Technologies)
  • WebQL(QL2 Software Inc.)
  • Academic
  • XWARP (Liu et al., 2000)
  • Lixto (Baumgartner et al., 2001)
  • Wargo (Pan et al., 2002)

48
Data Integrator
  • Example of web data integration
  • Functional components (Baumgartner et al., 2009)
  • Web interaction component
  • Lonelyplanet, wikitravel, virtualtourist,
    tripadvisor and official tourist website
  • Wrapper generator
  • OpenKapow Robomaker
  • Data transformer
  • DOM parser for RSS and XML formats

49
The GenW2 Framework
50
Data Integrator
  • Example of web data integration
  • Google as a first part of integration
  • Second Part - Functional components (Baumgartner
    et al., 2009)
  • Web interaction component
  • lonelyplanet, wikitravel, virtualtourist,
    tripadvisor and official tourist website
  • Wrapper generator
  • OpenKapow Robomaker
  • Data transformer
  • DOM parser for RSS and XML formats

51
The GenW2 Framework
52
Intelligent Ranker and Scheduler
  • Third step of integration.
  • Applies different profiles to the data, like
    Facebook and Dopplr.
  • Arranges the data in a ranked form depending on
    matches from user interests and activities.
  • Brute force cumulative ranking algorithm
  • 3 Explicitly mentioned
  • 2 Description match
  • 1 Suggested by other users
  • Merges data from public transport

53
The GenW2 Framework
54
Facts DB
  • Location information from the MRDB and map LOD
    with place
  • Activity Location pairs
  • Fact DB structure

55
Facts DB Structure
  • High Level Structure
  • Lower level structure Database Object maps to
    more locations
  • Limit to two levels
  • Inverse Page Lookup

Database Object
Activity LocationFrom LocationTo Name Rank User Feedback
Shopping 4722'40?N, 832'25?E 47.3671N , 8.5409E Bahnhofstrasse 3 Shop for watches, jewelry, clothes
56
Data Quality
  • Evaluation through completeness and correctness
  • Example Shopping stores in Bahnofstrasse
  • Extract lat-lng
  • Shop name, website, details and contact details
  • Shop opening and closing times
  • Evaluate against manually collected data for
    completeness and correctness.

57
Next steps
8
58
Next Steps
  • Formalizing parameters and methods for
    integration (Link)
  • Improve scoring algorithm for places
  • Structure of Facts DB for efficient storage and
    retrieval
  • Develop on quality control methods like
    considering user feedback and credibility

59
Open Questions
  • At what point is the data integrated?
  • When is it complete? Qualitative vs.
    Quantitative
  • Error recovery and correction mechanism in
    FactsDB?
  • Mapping of places score to LOD?

60
Milestones
61
Summary Expected contributions
  • Working system and framework for ad hoc data
    integration, that will work for certain flavours
  • Methodology of Flavour based data integration
    (RQ1)
  • Structure
  • Algorithm for efficient data source selection
    depending on flavour
  • Algorithm for scoring different places depending
    on number of parameters.
  • Concept and structure of FactsDB that will work
    with data from the MRDB for enrichment (RQ2)
  • Improved and adapted parameters and a mechanism
    for checking the quality of the integrated data
    and some test cases (RQ3)

62
The GenW2 Framework
63
Thank you!
Ramya Venkateswaran (ramya_at_geo.uzh.ch)
Demo and slides at http//www.geo.uzh.ch/ramya/ko
lloquium/
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