IBM -Dublin ResearchLab City Search · Smarter Cities share data …Open Urban Data is at the center of a ... RDF Index Download Search Query ... Spatio-temporal topic detection for

IBM - Dublin Research Lab

City SearchAn evolution of search to incorporate city data

Veli Bicer

IBM Research


Outline

• A Planet of Smarter Cities

• City Data and Information

• Making City Search Smarter

• Managing City Data

• Searching City Data

• Conclusion


A Planet of Smarter Cities

“Cities have the capability of providing something for everybody, only because, and only when, they

are created by everybody.”Jane Jacobs


A planet of smarter cities: In 2007, for the first time in history, the majority of the world’s population—3.3 billion people—lived in cities. By 2050, city dwellers are expected to make up 70% of

Earth’s total population, or 6.4 billion people.


Instru

men

ted

Inte

rcon

ne

cte

dIn

tellig

en

t

Dublin

Test B

ed

Energy Movement Water

Seed ProjectsReal World Insight | Data Sets | Devices

Optimization

Predictive Modelling

Forecasting

Simulation

Solu

tions th

at S

usta

in E

conom

ic D

evelo

pm

ent

Driving New Economic Models

Significant Collaborative R&D

Skills Development & Growth

Competitive Advantage

Collaboration and Access to Local, Regional & Worldwide NetworkSME’s | MNC’s | Universities | Public Sector | VC Community

Intelligent Urban and Environmental Analytics and Systems

Sm

art C

ity Solu

tions

Integrated Cross Domain Solutions

City Fabric

Smarter Cities Technology Centre


City Data and Information

“The country places and the trees don’t teach me anything, but the people in the

city do”

Socrates



Transportation Social MediaEnergy Management City Management

RegionSupply Chain Food System HealthCare

• Large, open and continuous data environment from heterogeneous domains:

and even more�

City of Data and Information: Many Areas

Water

Management


Some Traffic-related Data Sets

from Dublin

� Big data

� Heterogeneous data

� Static, Continuous data

� Not all open yet,

� Not linked yet

� Noisy data (inconsistent, imprecise)


POWERED by

Open Innovation Portalwww.dublinked.ie


Dublinked - outcomes

• Publish and put into context (100’s datasets, 1000’s of files)

• Create innovation ecosystem

Waste Collection

Property management

Environment

Demographics

Business & Retail

Commercial valuations

and rates

Tourism

Transport & Access

Crime

Heritage

Mapping

Housing

WaterFault Reporting

Events

Health

Planning

Pool resources Share results


Making City Search Smarter

“We cannot afford merely to sit down and deplore the evils of city life as inevitable, when cities are constantly growing,

both absolutely and relatively. We must set ourselves vigorously about the task of improving them; and this task

is now well begun.”

Theodore Roosevelt


City SearchNot a revolution, but evolution

City SearchConcerns all type of (complex) queries encountered in everyday city life, e.g. city events.

City SearchRelevance is highly dimensional, context-dependent and leverage more city-specific information than Web information.

Genetic D

rift

Web SearchOrdinary Web users want to locate information (e.g. documents) on the Web

Genes

Information Need Search Relevance Information Source

Local SearchMainly targeting queries to locate businesses within a geographic area

Local SearchExtending Web-based relevance with spatiotemporal relevance.

City SearchCity data provides a unique source of information to understand city context

Web SearchModels relevance using IR models based on content, Web popularity, clickthrough data etc.

Local SearchEnhanced with location and time information collected from mobile sensors, IP address etc.

Web SearchUtilizing Web-based information such as document content, Web graph, search engine logs, etc.


What do people search for?Top 8 categories according to user scores [Kukka, PUC, 2013]


Relevance

• Coffee? ☺

• Costa? Starbucks?

– Does the distance really matter?


Relevance

• Local vs. Web popularity

• What else?

• More information�

More relevance to the

user!!

Source: Foursquare, July 2013


Relevance

• Need to buy new “furniture”?


Relevance

• Dublin TRIPS data:


Relevance

• Dublin Trips Data:

– Journey times throughout the city

– Real-time data with updates in every minute

– Historical data is available for every day since 9/7/2012

– Mined from SCATS-based (Sydney Coordinated Adaptive Traffic

System) intelligent transportation system for 500+ sites around Dublin

• Accessible from:

– http://dublinked.ie/datastore/datasets/dataset-215.php

• Visualization

– http://www.dublinked.ie/traffic/


Relevance

• More transportation data

– Public Transport Route Networks

• http://dublinked.ie/datastore/datasets/dataset-258.php

– Dublin Bus GPS Data• http://dublinked.com/datastore/datasets/dataset-304.php

– Dublin Bus GTFS data • http://dublinked.ie/datastore/datasets/dataset-254.php

– Accessible Parking Places • http://dublinked.com/datastore/datasets/dataset-049.php

– Roads and Streets in Dublin City • http://dublinked.com/datastore/datasets/dataset-123.php


RelevanceBuying your dream house

Finding the houses?

Is the price reasonable?How is the neighborhood?

Perfect match!!


Relevance

• Property Register Index : ~52000 property sales

Available at http://kdeg.cs.tcd.ie/propertyPriceMap/


Relevance

• More city data:

– Amenities & Recreation

• http://dublinked.ie/datastore/by-category/amenities-

recreation.php

– Schools

• http://dublinked.com/datastore/datasets/dataset-099.php

– Key developing areas


– Air pollution monitoring data



We actually

have this

information.

But we require

novel search

and

exploration

methods


Business case: traffic diagnosisProblem: diagnosis and reasoningHow can we provide City decision makers with explanations and diagnoses for events by applying machine reasoning techniques to a fusion of massive, rich, complex and dynamic data? How can we move from explanation to prediction?

Challenges

• Identifying relevant data and information• Capturing and representing anomalies

• Correlating time-evolving knowledge on heterogeneous data sources• Advanced fusion of data

Anomaly Detected:

Delayed buses, congested

roads

Anomaly Detected:

Delayed buses, congested

roads

Detection to

Diagnosis?

Detection to

Diagnosis?

Diagnosis:

A music concert next to

Canal Road at 3PM

Diagnosis:

A music concert next to

Canal Road at 3PM


Information Sources

Web Context City ContextWeb documents Structured City Data

User Queries City-specific Web Documents

Clickthrough Data Sensor Data

Hyperlinks Social Media, Check-ins

Road Network

Transportation Data

City Events

Regional Information

Municipality

Crime and safety and much more…


Search Indexes

Search IndexesSearch

Indexes

Search IndexesSearch

Indexes

Search Indexes

Semantic Virtual Views on Data

Semantic Virtual Views on Data

Stream Data

Stream Data

Search Logs

Search Logs

Retrieval and RankingRetrieval and Ranking

Structured Data

Structured Data

Textual Data

Textual Data

Multiple Indexes needed to support different lookup

patterns, e.g. text, structured, real-time and

spatial indexes

Queries, ClickthroughData

Social, Web, Businesses,Events, M

Search Interface(Traditional)

Search Interface(Traditional)

Search Interface(Map)

Search Interface(Map)

Understanding the query characteristics via

classification, rewriting, expansion and semantic

translation

Contextual Access for Users and

Applications

Contextual Access for Users and

Applications

Model LearningModel Learning

IndexingIndexing

IRIR

DMDM

Legend

OtherOtherGeospatial Data

Geospatial Data

Sensors, Transportation,

Weather, GPS,M

Municipality, Public Services,

M

Road Network, POI,M

Data Access and Management LayerData Access and Management Layer

Query ProcessingQuery Processing

Search Interface(Mobile)

Search Interface(Mobile)

Overview of Components in City Search

Retrieval and RankingRetrieval and Ranking

Multiple aspects of relevance for various ranking

signals and aggregation

[Russell-Rose, SIGIR 2013]

[Liu, SIGIR 2008]

[Zhang & Jin, SIGIR 2008]

[Mika & Tran, SemTech 2013]

Geographic Crawling

Geographic Crawling


Managing City Data

“True genius resides in the capacity for

evaluation of uncertain, hazardous, and

conflicting information.”

Winston Churchill


Smarter Cities share data … Open Urban Data is at the center of a

new wave of opportunity (*)

(*) “Driving Innovation with Open Data”,Jeanne Holm, Data.gov,

Feb. 9th, 2012 (Presentation to Ontology 2012)

• More than 150 city agencies and authorities, worldwide, have already made over 1M datasets available through open data portals.

• Open data are generating new business: McKinsey & Associates estimate the economic value of big, open health data, at approximately $350B annually.


Systems view

(Not expressive)

(models)


Big city data4

V’s

of

Big

Da

ta


Technical challenges: outline

• Creating good quality Linked Data at enterprise scale from silos of raw city data with minimal entry cost

• Next-generation analytics for diagnosis, based on Semantic Reasoning

• Understanding context and human interaction with the system and data and aggregating relevant information into views

• Discovering relevant connections across data


Approach – Graph based model[QuerioCity – Lopez et. al, ISWC’12]

Pay-as-you-go –

Scale in terms of gain for the effort spent

• Publish, discovery and search of structured metadata -> Queries over metadata

• Files into a standard representation and entity extraction > Queries over data

• Link and partially integrate schemata -> Queries across datasets

• Integrate globally -> Queries across Web data

• Create meaningful views -> merging data views by users

Documents +

MetadataStructure Entities Links Views Insight

Tabular GraphC1 a CellC1 inRow r1C1 value “name”

M

Entity Graphe1 a Entitye1 inRow r1e1 inCol c2

M

Annotation Graphe1 a Entitye1 rdfs:label “name”e1 addr “X st”e1 lat :53.23” M

Mapping Graphe1 a Entitye1 sameAs e2M


SPUD architecture

Internet

External Dataset

External Catalog

External Ontology / vocab.

External SPARQL Endpoint

Social Media

Publishing ContainerIBM Tivoli

Access

Manager

and

WebSEAL

IBM WebSphere Application Server

IBM Storage

(SAN)

Datasets

Upload

SFTP ServerPublish &

Sync

IBM Tivoli

Directory Server

Datasets

Files Index

RDF Index

Download

Search

Query

Browse

Catalog

Publish

GUIQuerioCity Enterprise Application

Data Layer

SPARQL

Endpoint

REST APIs Lucene

Code / Jar

Apache

PFDBox

Custom

Code

Apache

POI

DB2 Enterprise Edition

IBM DB2 Enterprise Edition

VocabulariesTripleStore

Enterprise Apps

DiagnoserSocial Media

Ingester

Trajectory

Miner

Privacy and

AnonymizerTaxonomizer

IBM HTTP

Server

Data

Fusion

And

Semantic

Annotation

Co

nsu

me

r

….

Pu

blish

er

[Kotoulas et al, SWC @ISWC’12 ]


SPUD

SPUD Demonstrator Won 2nd prize in ISWC Semantic Web Challenge 2012!

[Kotoulas et al, SWC @ISWC’12 ]


Other systems of City Data

Management

• STAR-CITY: Semantic Traffic Analytics and Reasoning for CITY

– http://dublinked.ie/sandbox/star-city/

• EXCED: Spatio-temporal topic detection for cities

– http://www.youtube.com/watch?v=Q_orwDfU8Yw&feature=youtu.be

• Link2Outcome: Coordinating Social Care and Health Care

– http://researcher.ibm.com/researcher/view_project.php?id=5034

…and many more at:

– http://dublinked.ie/sandbox/


Searching City Data

“We are searching for some kind of harmony between two intangibles: a form which we have not yet designed and a context which we cannot

properly describe.”

Christopher Alexander


•Relevance is a tradeoff between multiple relevance aspects

•Understanding query context for different domains•Query semantics

Technical challenges: outline

• City data comes in different forms and shapes• Standard indexing techniques (e.g. Lucene) is useful to handle

textual content• Multiple Indexing for supporting different lookup patterns

• How to classify/rewrite/expand city query?• How to understand query context/semantics?• How to model relevance: e.g. “reputation” to be more important for

“coffee shops” but distance for “bank office".

IndexingRetrieval &

RankingQuery

Processing

•Real-time data•Spatial Indexing•Handling structured data

Indexing


State-of-the-art

• Indexing:

– Real-time: Partial Indexing

• Indexing only the records with high chance of being queried [Chen et al,

SIGMOD’11]

– Spatial: Hybrid index (IR-Tree) combining R-tree for spatial DBs with

inverted index [Cong et al, VLDB’09]

– Structure: Data partitioning, structural similarity [Tran et al, TKDE’12]

• Query Processing:

– Classification: Using query logs [Jones et al, WWW’06]

– Context: Model learning for different domains [Bai et al, SIGIR’07]

– Semantics: Top-k graph exploration over RDF [Tran et al, ICDE’09]


State-of-the-art

• Relevance and ranking:

– Content:

• Structured relevance models [Bicer et al, CIKM11]

– Structure: ObjectRank [Hristidis et al, TDS08]

– Local popularity: Checkins [Cheng et al, CIKM’11]

– Distance: Relative distance [Berberich, SIGIR’11]

– Context (Temporal, Weather, Spatial, Personal):

• Hapori [Lane et al, Ubicomp’10]

• Aggregation of multiple aspects:

– [Kang et al, WSDM’12]


STAR-CITY core features

• Analysis: Real-time interpretation of traffic data

• Diagnosis: Identifying nature and cause of anomalies

• Exploration: Contextual search and navigation

• Prediction: Context-aware traffic prediction

http://dublinked.ie/sandbox/star-city/


STAR-CITY Data


Exploration

• Search anomalies and events in similar context:– Event types, e.g. roadworks vs.social

– Location, time

– Weather conditions

– Venues, tags and more…

• Multiple relevance aspects


Wrap Up

•Majority of World population live in cities

•Cities are dynamic entities combining people,

systems, infrastructure, businesses

•More and more city data becomes available

enabling more insight

•City data is heterogeneous, multi-domain,

noisy and big

Cities and City Data

•Managing City Data

• Characteristics and types of city data

• Semantic Processing and lifting

• Analytics and insight

•Searching City Data

• Challenges for IR community

• Synergy of different approaches

• Future directions

Building the solution

•City search as an evolution of search into city

context

•Characterized by specific information needs of

people in everyday city life

•Drift in search relevance from Web context to

the city context

•Drift in information sources used to drive

search process

City Search


More on City Search can be found at

IBM IR Webpage:


References

• Marty Himmelstein, Local search: The internet is the yellow pages, IEEE Computer, 2005

• Klaus Berberich, Arnd C. Konig, Dimitrios Lymberopoulos, Peixiang Zhao, Improving local search ranking through external logs, SIGIR 2011.

• Hannu Kukka, Vassilis Kostakos, Timo Ojala, Johanna Ylipulli, Tiina Suopajarvi, Marko Jurmu, Simo Hosio, This is not classified: everyday information seeking and encountering in smart urban spaces, Personal and Ubiquitous Computing, 2013

• Spink, A., Wolfram, D., Jansen, M. B., & Saracevic, T. (2001). Searching the web: The public and their queries. Journal of the American society for information

science and technology, 52(3), 226-234.

• Zhang, Wei Vivian, Benjamin Rey, Eugene Stipp, and Rosie Jones. Geomodificationin Query Rewriting. In GIR. 2006.


ReferencesQuerio City / Urban Data

• V. Lopez, S. Kotoulas, M. L. Sbodio, M. Stephenson, A. Gkoulalas-Divanis, P. Mac Aonghusa. QuerioCity: A Linked Data Platform for Urban Information Management. In Use track at ISWC 2012.

• V.Lopez, S.Kotoulas, M.L.Sbodio, R.Lloyd. Guided exploration and integration of urban data. Hypertext’13.

Reasonable City• Freddy Lecue, Jeff Z, Pan. Predicting Knowledge in an Ontology Stream. In Proc. of IJCAI 2013• Elizabeth M. Daly, Freddy Lecue, Veli Bicer. Westland Row Why So Slow? Fusing Social Media and Linked Data

Sources for Understanding Real-Time Traffic Conditions. In Proc. IUI 2013• Freddy Lecue, Anika Schumann, Marco Luca Sbodio. Applying Semantic Web Technologies for Diagnosing Road

Traffic Congestions. In Proc. of ISWC 2012.

Social City• Elizabeth M. Daly, Giusy Di Lorenzo, Daniele Quercia, Michael Muller. When the City Meets the Citizen. In Proc.

of ICWSM 2012.• Giusy Di Lorenzo, Marco Luca Sbodio, Vanessa Lopez, Raymond Lloyd. EXSED: an intelligence tool for

Exploration of Social Event Dynamics. In Proc. of MDM 2013.

Stream City• Simone Tallevi, Spyros Kotoulas, Luca Foshini, Freddy Lecue, Antonio Corradi. Real-time Urban Monitoring in

Dublin using Semantic and Stream Technologies. In Use track at ISWC 2013

Care City• Spyros Kotoulas, Vanesa Lopez, Martin Stephenson et al. Coordinating social care and health care using

Semantic Web technologies. Demo session at ISWC 2013 (submitted)

SPUD: Semantic Processing of Urban Data – Demo: www.dublinked.ie/sandbox/SemanticWebChallKotoulas, Vanessa Lopez, Raymond Lloyd, Marco Luca Sbodio, Freddy Lecue, Martin Stephenson, Elizabeth Daly,

Veli Bicer, Aris Gkoulalas-Divanis, Giusy Di Lorenzo, Anika Schumann, Denis Patterson, and Pol Mac Aonghusa


Processing and publishing Linked urban Data• [Maali, ESWC’12] Maali, F., Cyganiak, R., Peristeras, V.: A publishing pipeline for linked government data. Proc. of

ESWC, 2012.• [Datalift] Schar_e, F., Atemezing, G., R., T., Gandon, F.e.a.: Enabling linked-data publication with the datalift

platform. In (AAAI'12) Workshop on Semantic Cities, 2012• [TWC LOGD] Ding. ,L., Lebo., T., Erickson, J.S. et al.: Twc logd: A portal for linked open government data

ecosystems. Web Semantics, 2011.• IBM City Forward: http://cityforward.org

Semantic Lifting• [RF123] Han, L., Finin, T., Parr, C., Sachs, J., Joshi, A.: RDF123: From Spreadsheets to RDF. Proc. of ISWC

2008• [Csv2rdf4lod] http://data-gov.tw.rpi.edu/wiki/Csv2rdf4lod• Skjæveland, M.G., Lian, E. H., Horrocks, I. Publishing the Norwegian Petroleum Directorate’s FactPages as

Semantic Web Data. In use ISWC’13

Web Tables• Cafarella, M.J., Halevy, A., Madhavan, J.: Structured data on the Web. Communications of the ACM, 2011• Sarma A., Fang, L., Gupta, N., Halevy, A., et al.: Finding Related Tables, SIGMOD '12

Urban Dynamics• Kling f., Pozdnoukhov, A.: When a city tells a story. In ACM SIGSPATIAL GIS, 2012

Evaluation Campaigns• Blanco et al. Repeatable and Reliable Search System Evaluation using Crowd-Sourcing, SIGIR 2011• [QALD, JSW’13] Lopez, V., Unger, C., Cimiano P., Motta, E.: Evaluating Question Answering over Linked Data,

Journal Web Semantics 2013, http://greententacle.techfak.uni-bielefeld.de/~cunger/qald/

References


References

• Indexing– Chun Chen, Feng Li, Beng C. Ooi, Sai Wu, Ti: an efficient indexing mechanism for real-

time search on tweets, SIGMOD 2011

– Thanh Tran, Günter Ladwig, Sebastian Rudolph, RDF Data Partitioning and Query Processing Using Structure Indexes, Transactions on Knowledge and Data Engineering, 2012.

– Cong, Gao, Christian S. Jensen, and Dingming Wu. Efficient retrieval of the top-k most relevant spatial web objects. Proceedings of the VLDB Endowment 2.1 (2009): 337-348.

• Query Processing– Zhang, Wei Vivian, Benjamin Rey, Eugene Stipp, and Rosie Jones. Geomodification in

Query Rewriting. In GIR. 2006.

– Gravano, Luis, Vasileios Hatzivassiloglou, and Richard Lichtenstein. Categorizing web queries according to geographical locality. Proceedings of the twelfth international conference on Information and knowledge management. ACM, 2003.

– Jones, Rosie, Benjamin Rey, Omid Madani, and Wiley Greiner. Generating query substitutions. In Proceedings of the 15th international conference on World Wide Web, pp. 387-396. ACM, 2006.

– Thanh Tran, Haofen Wang, Sebastian Rudolph, Philipp Cimiano, Top-k exploration of query candidates for efficient keyword search on graph-shaped (rdf) data, ICDE, 2009.

– Bai, Jing, Jian-Yun Nie, Guihong Cao, and Hugues Bouchard. Using query contexts in information retrieval. In SIGIR, 2007.


References

• Retrieval– Changsung Kang, Xuanhui Wang, Yi Chang, Belle Tseng, Learning to rank with

multi-aspect relevance for vertical search, WSDM 2012

– Nicholas D Lane, Dimitrios Lymberopoulos, Feng Zhao, Andrew T. Campbell, Hapori: context-based local search for mobile phones using community behavioral modeling and similarity, Ubicomp,2010.

– Klaus Berberich, Arnd C. Konig, Dimitrios Lymberopoulos, Peixiang Zhao, Improving local search ranking through external logs, SIGIR 2011.

– Cheng, Zhiyuan, et al. Toward traffic-driven location-based Web search.CIKM, 2011.

– Hristidis, Vagelis, Heasoo Hwang, and Yannis Papakonstantinou. Authority-based keyword search in databases. ACM Transactions on Database Systems (TODS) 33, no. 1 (2008): 1.

– Li, Guoliang, Beng Chin Ooi, Jianhua Feng, Jianyong Wang, and Lizhu Zhou. EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. In SIGMOD, 2008.

– Guo, Lin, Feng Shao, Chavdar Botev, and Jayavel Shanmugasundaram. XRANK: ranked keyword search over XML documents. In SIGMOD, 2003.

– Bicer, Veli, Thanh Tran, and Radoslav Nedkov. Ranking support for keyword search on structured data using relevance models. In CIKM, 2011.


References

• Other Tutorials– Tony Russell-Rose, Designing Search Usability, Tutorial at

SIGIR 2013.

– Tie-Yan Liu, Learning to Rank for Information Retrieval, Tutorial at SIGIR 2008

– Yi Zhang and Rong Jin, Supervised and Semi-Supervised Learning for IR, Tutorial at SIGIR 2008

– Peter Mika, Thanh Tran, Semantic Search on the Rise, Tutorial at SemTech 2013

– Thanh Tran, Semantic Search - Focus: IR on Structured Data, ESSIR, 2011.

– Yi Chen, Keyword Search on Structured and Semi-structured Data. Tutorial in SIGMOD, 2009