Search Computing Overview

SearchComputing
Stefano Ceri, Keynote talk at CAISE, Hammamet, June 9, 2010
Joint work with: Adnan Abid, Mamoun Abu Helu, Davide Barbieri,
Daniele Braga, Marco Brambilla, Alessandro Bozzon, Alessandro
Campi, Sofia Ceppi, Francesco Corcoglioniti, Emanuele Della Valle,
Davide Eynard, Piero Fraternali, Nicola Gatti, Giorgio Ghisalberghi,
Michael Grossniklaus, Davide Martinenghi, Marco Masseroli,
Maristella Matera, Chiara Pasini, Elena Pellizzotti, Stefania Ronchi,
Marco Tagliasacchi, Luca Tettamanti, Salvatore Vadacca, Riccardo
Volonterio, Serge Zagorac

Genesis of Search Computing

 My “Gong Show” challenge at 2003 Lowell Workshop:
“Find an ethnical restaurant in a nice place close to Milano” .
 Logically a composition of domains:
– Restaurants (ethnical)
– Geo-locations (nice place close to Milano)
 Composing maps with “geo-located” information is now
solved by all search engines …

… but in general no system is capable of composing
arbitrary semantic domains

Database Management Prof. Stefano Ceri

Motivating Examples 3

 “Who are the strongest candidates in Europe for
competing on software ideas?”
 “Who is the best doctor who can cure insomnia in a
close-by hospital?”
 “Where can I attend an interesting scientific conference in
my field and at the same time relax on a beautiful beach
nearby?”


Their Common Aspect 4

 Multi-domain queries
 Individual answers are on the Web
 A knowledgeable user would do the query step-by-step:
– Search database conferences, get their city
– Check that the city average temperature is warm enough
– Search low-cost flights via a broker for that city
– Search luxury hotels via another broker

 We want a system for supporting this search process
– Build several “solutions” which already integrate all dimensions
– Rank “solutions” according to a global rank function and output
results in rank order
– Support user-friendly query definition and result browsing
– Add search domains while the search proceeds
– Possibly change the relative weight of each ranking


5

OVERALL FRAMEWORK


Search Computing architecture: overall view 6
Front End

High-Level Query
Final User
Query Analysis Results
Cache

Sub-queries
Cache
Query To Domain Result
Mapper Transformation
Cache

Low-level queries
Merged Results
Query Planner
Cache

Concrete
Query Plan
Query Engine
WS-Framework Main Query flow
OP 1 OP 2 ... OP N Cache
Cache

<Uses> relation

Domain Domain Service WS
Framework Repository Repository World
Cache


Front End
High level query
“Where can I attend a DB
Sub query 1 scientific conference close to
High-Level Query
“Where can I attend a beautiful beach reachable Final User
Query Analysis with cheap flights?” Results
a DB scientific Sub query 2
conference?” Cache
“place close to
a beautiful Sub-queries
beach?” Sub query 3 Cache
Mapper
“place reachable Transformation
with cheap flight?”
Cache

Low-level queries
Merged Results
Query Planner
Cache

Concrete
Query Plan
Query Engine
Cache

<Uses> relation

Cache


Front End

High-Level Query
Final User
Cache

Sub-queries
Low level query 1
Cache
ConfSearch(“DB”,placeX,dateY)
Low level query 2Cache

TourSearch(“Beach”,PlaceX) queries
Low-level
Merged Results
Query Planner Low level query 3
Flight(“cost<200”,PlaceX,DateY)
Cache

Concrete
Query Plan
Query Engine
Cache

<Uses> relation

Cache


Front End

High-Level Query
Presented results
Final User
ESWC-Crete-Olympic
Cache
CAISE- Hammamet – Alitalia
TOOLS-Malaga-EasyJet
Sub-queries
Cache
Cache

Low-level queries
Merged Results
Query Planner Results
Cache
Query plan
Concrete
Query Plan
Query Engine
Cache
OP 1 OP 2 ... OP N Services invocations Cache
and operators execution
<Uses> relation

Cache


Search Computing architecture: incremental prototyping
11
Front End

Concrete Query Plan
Low-level queries
Sub-queries
High-Level Query
Final User
Cache

Sub-queries
Cache

Admin Interface
Cache

Low-level queries
Merged Results
Query Planner
Cache

Concrete
Prototype 1: Query Plan
Core behaviour of the
Query Engine
system. WS-Framework
Cache
• Engine-based execution
of queries
• Domain repository <Uses> relation
• Service repository
• Coarse result
presentation
Cache


12
Front End

Concrete Query Plan
Low-level queries
Sub-queries
High-Level Query
Final User
Cache

Sub-queries
Cache

Admin Interface
Prototype 2: Mapper Transformation
Cache
Planning
Low-level queries
Merged Results
• Automatic optimized
query planning Query Planner
Cache

Concrete
Query Engine
Cache
of queries
• Coarse result
presentation
Cache


13
Prototype 4:
Front End
High level queries

Concrete Query Plan
Low-level queries
Prototype 3:

Sub-queries
High-Level Query
Mapping and Final User
presentation Query Analysis Results
Cache
• mapping to domains
• presentation of results Sub-queries
Cache

Admin Interface
Prototype 2: Mapper Transformation
Cache
Planning
Low-level queries
Merged Results
• Automatic optimized
query planning Query Planner
Cache

Concrete
Query Engine
Cache
of queries
• Coarse result
presentation
Cache


CAISE FOCUS on: Service Registration 14

Service Marts:
• Conceptualrepresentatio
nofresourcesasentities
and connections
• Logicalrepresentationofs
ignatures
• Physicalrepresentationa
s service
implementations


CAISE FOCUS on: Front-end 15

Liquid Query Front End

 Client-
High-Level Query
sideframeworkforconfi Final User
guration and Query Analysis Results
automaticrenderingof Cache

query and Sub-queries
resultinterfaces Query To Domain Result Cache

 User interaction Cache

Low-level queries
primitives that allow to Merged Results
perform explanatory Query Planner

search Cache

Concrete
Query Plan
Query Engine
WS-Framework
Cache

Cache


CAISE FOCUS on: Development Process 16

Development Support

Deploy
<<implements>> <<deploys>>

Time
Search Services SeCo platform
Environment Service
Developer
SeCo
Expert

Tools supporting

Service Publishing Time
Wrapping
 Service Registration <<implements>>

 Query Design <<defines>> Materialization /
Normalization
 Performance Monitoring Service
Publisher
<<uses>>
<<performs>> Registration of
Service Mart <<produces>>

Service Mart
<<uses>> Repository

Config.
Time
<<defines>> Liquid Query
<<produces>>
Template
Expert
User

Execution Time
<<uses>>
<<submits>> Liquid Query User Interface
Specification

<<manipulates>>
<<uses>>
Final User Liquid Result


17

SERVICE REGISTRATION


Service Registration in SeCo
Objective: providing a framework for registering services as
first-class citizens within SeCo

=> Service Marts
 High-level abstractions of “real world entities” that provide a simple
interface to users and hide implementation details
 Inspired by Data Marts, a data modeling pattern used in data
warehousing
 Each Service Mart can have multiple modalities of data access and
can be mapped to multiple service implementations, possibly
offered by different providers

=>Connection Patterns
 High-level abstractions of “real world relationships” that provide a
simple interface to users and hide implementation details
 Built by means of attributes that share the same domains


Service Marts – Conceptual Level
Every SM definition includes a name and a collection of the exposed
attributes,i.e. the attributes of the real world object described by the SM
Movie(Title, Director, Year, Language, Genres(Genre), Actors(Name, Sex))

 Atomic, single valued, typed attributes
 Repeating groups (multi-valued, typed attributes)
 Each “repeating group” is a non-empty set of typed sub-attributes
that collectively defines a property of the service mart

The model choices are:
 To support structural complexity with only one level of nesting
(rather than an arbitrary level of nestings)
 To avoid explicit descriptions of relationship (using repeating
groups for M:N relationships)


Service Marts – Logical Level

At this level, each SM is associated with one or more Access Patterns, i.e.:

Movie1(TitleO, DirectorO, ScoreRO, YearO, LanguageI, Genres.GenreO,
Actors.NameO , Actors.SexO,Genres.GenreI)

Movie2(TitleI, DirectorO, YearO, LanguageO, Genres.GenreO, Actors.NameO ,
Actors.SexO)

 Access patterns contain adorned attributes, i.e. attributes tagged with one of
the following:
 I, if they are input attributes
 O, if they are Output attributes
 R, if they are attributes used for ranking – they may or may not be visible in output

 Movie1 makes access to movies by Language and Genre (i.e., “action movies
in English”) and results are ranked by Score (a new attribute).
 Movie2 makes access to movies by Title (e.g. “Ben Hur”). We expect few
(zero, one, more) results which are not ranked.


Service Marts – Physical Level

At this level, every Access Pattern can match different
Service Implementations, having:
 Physical URI to be called
 Physical properties which are specific to the implementation
 Mapping between logical and physical parameters

IMDBMovie1(MovieTitleO, DirectorO, StarsRO, YearO, LanguageI,
Genres.GenreI, Actors.NameO , Actors.GenderO)

IMDBMovie AP: Movie1 URI: http://...

TTL=6000, chunksize=10, cacheable=true, exposed=false, ...

Title Director Score Year Language ...

MovieTitle Director Stars Year Lang ...


External and Selector Attributes

 external attributes, for supporting access and ranking

SM Movie(Title, Director, Year, Language, …)

AP Movie1: TitleO | DirectorO | YearO | … | ScoreRO | GenreI

AP Movien: TitleO | DirectorO | YearO | … | TitleI

External attributes
 selector attributes, for supporting choices among service implementations

SM Movie(Title, Director, Year, Language, …)

Language
SI Movie Implementation 1
... Selector
SI Movie Implementation n


Connection Patterns

Connections between marts only exist in terms of attributes that share the same
domains, on different levels of abstraction:
 Conceptually by a nondirected edge with a name:
PlayingMovie(Movie,Theatre)

Movie Theatre

 Logically by an edge (possibly directed) with name and join condition:
PlayingMovie(Movie,Theatre): (Title=Movie.Title)

Movie4 Theatre2


Connection Patterns – Logical Level

Directed edge: Information is “piped” from one access pattern to another,
along connection attributes which are in output in the first service and in
input in the second service -> PIPE JOIN

Movie1 Title Director Score Year Language A.Name A.Sex G.Genre

Theatre1 Name Address M.Start M.Title


Connection Patterns – Logical Level

Undirected edge: results are produced by both access patterns in output and
then joined -> PARALLEL JOIN

Movie1 Title Director Score … … G.Genre

Theatre1 Name Address M.Start M.Title


Join of two Services, Pipe Version, NY City
Search only in NY

Movie Theatre
Service Mart Service Mart

Movie1 Movie2 Theatre1
Access Access Access
pattern pattern pattern

IMDB2
Service
Interface

IMDB1 Hyperrev1
Service Service
Interface Interface
Google1 NYLocalSearch
Service Service
Interface Interface


27

JOIN OF TWO SEARCH
SERVICES


JOIN of Web Services

 Input: items resulting from TWO web service calls,
possibly ranked
 Output: composed items resulting from the
concatenation of matching items, presented in a
“global ranking order”
 Matching condition using:
– value equality,
– partial set matching
– term matching within a vocabulary
…..
 Services are known, their matching function is
predefined: this is not service discovery!


Join 29

Service X Service Y

bx5 by5

bx4 by4

bx3 by3

bx2 by2

bx1 by1

r1

r2

r3


Matching items 30


Choice of the join strategies
 The join search space
– Different explorations for different joins methods under different
assumptions and with different guarantees

Chunksize

Chunk
tij

Any exploration trajectory Candidate join result
for this space is a join strategy

Nested Loop - Rectangular 32


Merge scan - Triangular 33


Parallel and Pipe Joins 34

 Parallel join of two search services
(1)

S1

(1,2)n C1
period: 150 ms stop: 10
excess: (1,1)
(2)

S2

 Pipe join of two search services
(2)

(1)

(1,10)5(0,1)n S1 S2 size: 20
period: 500 ms stop: 1


35

SUPPORT OF “SIMILARITY
JOINS"


Supporting value similarity

 Concept of “nearness” is widely implemented depending
on different contexts, such as:
Lexical near (similar strings)
Spatial near (between addresses/geo locations)
Temporal near (between dates/times)
Economic near (between costs)

 Context is defined according to the attributes involved

=> Semantics of nearness built bottom-up, starting from the
physical layer (available services) up to the conceptual
one.


Similarity comes from Shared Domains

The attribute
“address” is shared
by the 4 entities. Its restaurant apartment
semantic type, Address Address
describing a location,
enables “nearness” Spatial
connections between Near
each pair of entities
(i.e. addresses can
Address Address
be compared for
“nearness” within the hotel theatre
same city, country,
…)


Supporting Nearness within Services
Severalphysicalservicesnativelysupport ranking
bydistances (e.g. GoogleMovies)
 E.g.: GoogleMovies receives the user address as input,
and returns theatres ranked by distance, each one with
its address as output. UserAddress and Distance are
external attributes.
GoogleMovies(UserAddressI, DistanceR| NameO, AddressO, Movie.TitleI,
Movie.StartTimeO)

GoogleMovies AP: Theatre1 URI: http://...

TTL=6000, chunksize=10, cacheable=true, provides=Spatial Near

UserAddress Name Address M.Title M.StartTime ...

IAddr Name OAddr MovieTit MovieTime ...


“Nearness” Support within Services

Theatre Restaurant

Spatial Near

Restaurant2 Address Name Cuisine Price
Spatial near

Theatre1 UserAddress Name Address M.Title M.StartTime Distance

GoogleMovies AP: Theatre1 URI: http://...

TTL=600, chunksize=10, cache=1, provides=Spatial Near

UserAddr Name Address M.Title ...

Addr Name Addr MovieTit ...


Nearness Services within the Execution Engine

Ad-hoc services providing the notion of distance at the physical level require
two domain values as input and produce their distance as output
 Two input attributes to specify two values of the domain
 One output attribute specifies the distance in given units

SpatialNear System URI: http://...

TTL=600, chunksize=1, cacheable=1, ...

Input1, Input2: Coordinates Output: Distance (Km)


Supporting Nearness within the Execution Engine

Theatre Restaurant

Spatial Near

Restaurant2 Address Name Cuisine Price

Theatre1 Address Name M.Title M.StartTime

Spatial Near Addr1 Addr2 Distance

SpatialNear System URI: http://...

TTL=600, chunksize=1, cacheable=1, ...

Input1, Input2: Coordinates Output: Distance (Km)


Join of three Services at the three Levels in NY
Search only in NY

Movie Theatre Restaurant
Service Mart Service Mart Service Mart

Spatial Near

Movie1 Movie2 Theatre1 Rest1 Rest2
AP
Access Access Access Access
providing
pattern pattern pattern pattern
spatial near

IMDB2 Yahoo1 Yahoo2
Service Service Service
Interface Interface Interface

IMDB1
Service
Interface Hyperrev1 Google1 NYLocalSearch
Service Service Service
Interface Interface Interface


Three Levels with Connection Semantics

Services Connections

Name (with associated
Conceptual Service Mart
semantics)
Bindings between SM and
AP attributes, plus
definition of extra
attributes
Join attributes,directed vs
Logical Access Pattern undirected edge (with nearness
service APs added as needed)
Bindings between AP
attributes and SI
parameters
Service Interface (with
Physical associated semantics and Nearness Services
with system services)


Resource graph
Specialized way for describing search service based
knowledge available on the web [ER model, ontology,
class diagram?]
News Restaurant
Exhibition
...
Piece
...
Concert
...
Artist
... Photo ...
Hotel

Movie

... Metro Station
Theatre
Landmark
... ShoppingCenter
...

46

APPLICATION DEVELOPMENT
PROCESS


SeCo development process

Search Service
and Registration Development
 Main Roles:
• Service
developer Service developer Implement search service

• Service
publisher

Adaptation
Service
• Expert user Service publisher Wrap or materialize Register service
• SeCo expert service mart and interface

 Dichotomy:
Configuration
Application

• Top-down Service Mart model
vs. Expert user Design Liquid Query Template

Bottom-up
• Run time Manual optimization
needed?
N
Liquid Query model
Y
vs.
Refinement
Query Plan

Design time Query Plan model

SeCo expert Panta Rhei plan refinement


The service registration process

Service
Description

SM Identification

Buttom up Strategy
YES NO
Some SM
retrieved
?
YES
SM CREATION
Modification Hybrid Strategy
of the SM
structure? SM UPDATE
NO
Associated SI Update
Top down Strategy (new connections)

SM MAPPING

AP CREATION

Service Physical
Description

END


The SM Creation process, with semantic hints
SM CREATION

Movie(Title, Director, Score, Year, Genres(Genre),
Openings(Country, Date), Actors(Name))
Type SM Name and attributes
conventions schema definition
Movie: S: (n) movie, film, picture, moving
picture, moving-picture show, motion picture,
motion-picture show, picture show, pic, flick
(a form of entertainment that enacts a story by
WN
sound and a sequence of images giving the
SM and attributes
Semantical Description illusion of continuous movement) "they went to
Synsets (and tags?)
a movie every Saturday night";

Automatic recommendation Director: S: (n) film director, director
of connectable SMs (the person who directs the making of a film)
SM1
Connection patterns Shows(Movie, Theatre): [(Title=Title)]
Theatres (CP) definition

SMn Defined CP: Shows Textual_near
Possible CP: Title (String)  Textual_near
Spatial_near Composition Language
Textual near operators association Year (Date)  Temporal_near …
Temporal_near


The SM Mapping procedure
SM MAPPING
Original
SM
Movie(Title, Director, Score, Year, …)
Director: String
Director: S: the
person who
directs the making
of a film)
f
Director (String)

SI

Selector ImdbMovie: Title | Director | Score | Year | …
Auxiliary
Selector CorrespondingSM attributes
attributes attributes (i.e. query
attributes)


SeCo Tools
• Online tool suite that covers the whole development
process
• Mashup-based
• Built by using state of the art technologies:

1. MVC on the client: Javascript MVC
2. UI organization and panels: Yahoo! User Interfaces
3. Diagram drawing and editing: WireIt


Service Mart Registration 53


Mapping editor 54


Query Registration Interface 55


Query Registration Editor, Logical Connections 56


57

LIQUID QUERY INTERFACE


Liquid Query

“ A new paradigm allowing users to formulate and get responses
to multi-domain queries through an exploratory information
seeking approach, based upon structured information
sources exposed as software services…”

• Composite answers obtained by aggregating search results
from various domains
• Highlight the contribution of each search service
• Join of results based on the structural information afforded by
the search service interfaces
• Refine the user query
• Re-shape the result list


Liquid query definition
It consists of subsetting and parametrizing the resource
graph...
News Restaurant
Exhibition
...
Piece
...
Concert
...
Artist
... Photo ...
Hotel

Movie

... Metro Station
Theatre
Landmark
... ShoppingCenter
...
= inputs, outputs + GR = global ranking

Liquid query definition
... And then characterizing the user interaction
News Restaurant
Exhibition

Concert
Artist
Photo
Hotel
Expand

Plus: Metro Station
• Parametrization of global ranking
• Data visualization options
• .. and so on

Query Submission

Concert Hotels
query conditions query conditions


Query Execution & Result Presentation


63

SECO ENGINE


Overview
 The tools is aimed at developers and permits to compose, plan and
run a SeCo query
 Four panels, one for each query processing phase:

Query Logical Physical Query
composition planning planning execution

Splashscreen!


Query composition (1)

Service interface browser
• listsregistered service interfaces
• Input and output parameters are listed

Selected service’s statistics
• collected service statistics are displayed
• statisticsmaybeeditedfortestingpurposes


Query composition (2)

User-entereddatalog-likequery
Queryoptimisationparameters
• joinsimplicitlyencodedbydatalogvars • control the behaviourof the planner
• $varsencodequeryinputsprovided at • trigger the planning process
runtime


Logical planning


Physical planning


Query execution (1)

Executionsession management
• a sessioncorrespondsto a single queryexecution, where
multiple usercommandsmaybeissued
• query input parameters are specified at
sessioninitialisation

Execution status
• displays the currentsession status
• displays the status of the executioncommandsissued so
far

Executioncommandsforms
• a more-allcommandrequires more queryresults
• a more-onecommandrequires more resultsbyextracting
more data from a specific service invokedby the query


Query execution (2)

Queryresults
• Displaysrankedr
esults,
assoonascomp
uted

Executiontime
line
• displaysactivati
onofexecutionu
nits (e.g.
service calls)
• usefulto fine
tune the engine
and the join
strategies


Query execution (3)

Service calls log
• displays service calls at the chunkgranularity
• showsresponsetimes, statistics, cache behaviour


DEMO

http://demo.search-computing.eu

73

SUMMARY OF SECO RESULTS


Results after 18 months 74

 Concepts
– Service marts, rank join methods, pantarhei, liquidquery

 Researchresults
– Springer LNCS: SearchComputingChallenges and Directions
– Manypublications (withVLDB,WWW), manyongoingsubmissions
– Filingof US Patent (top-k method, random&sequentialservices)

 Prototypes
– Executionenvironment, focus on liquidquery and on integration
– Design supportenvironment, focus on mashups

 Dissemination
– Fifteenkeynotetalks, twelvearticles in the Italian press
– SeCo Web site, SeCo blog, facebook, linked-in, twittercommunities
– SearchComputing Graduate Course at PoliMi

 Temporary research positions (1 phd, 5 post-ms, 3 post-doc)


Publications
75
SeCo
- D. Braga, A. Campi, S. Ceri, A. RaffioJoining the results of heterogeneous search enginesInformation Systems, Vol. 33, Issues 7-8, (November-December 2008), Pages 658-680
- D. Braga, S. Ceri, F. Daniel, D. MartinenghiOptimization of Multi-Domain Queries on the WebVLDB 2008: 562-573, Auckland, New Zealand, August 2008
- D. Braga, S. Ceri, F. Daniel, D. MartinenghiMashing Up Search Services, IEEE Internet Computing 12(5): 16-23 (2008)
- D. Braga, D. Calvanese, A. Campi, S. Ceri, F. Daniel, D. Martinenghi, P. Merialdo, R. Torlone, NGS: a framework for multi-domain query answering, ICDE Workshops 2008: 254-261
- S. Ceri, Search Computin Invited Paper, 25th International Conference on Data Engineering, Shanghai, March 29 - April 2, 2009
- D. Barbieri, A. Bozzon, D. Braga, M. Brambilla,A. Campi, S. Ceri, E. Della Valle, P. Fraternali, M. Grossniklaus, D. Martinenghi, S. Ronchi, M. TagliasacchiData-driven optimization of -
search service composition for answering multi-domain queries (USETIM 2009) workshop at VLDB 2009, Lyon, France, August 24-28, 2009
- M.Brambilla, S. Ceri, Engineering Search Computing Applications: Vision and Challenges The 7th joint meeting of the European Software Engineering Conference (ESEC) and the ACM
SIGSOFT Symposium on the Foundations of Software Engineering (FSE), Amsterdam, The Netherlands, August 24-28 2009
- S. Ceri Search Computing The 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Milan, Italy, September 15-18 2009
- S. Ceppi and N. Gatti, An Automated Mechanism Design Approach for Sponsored Search Auctions with Federated Search Engines In Proceedings of the 12^th Workshop on Agent-
Mediated Electronic Commerce (AMEC) in the 9^th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Toronto, Canada May 10 2010
- D. Martinenghi, M. Tagliasacchi, and S. Ceri Top-k pipe-join International Workshop on Ranking in Databases, Long Beach, USA, March 2010
- A. Bozzon, M. Brambilla, S. Ceri, P. FraternaliLiquid Query: Multi-Domain Exploratory Search on the WebWWW 2010 - 19th International World Wide Web Conference - Raleigh,
North Carolina, April 26-30 2010
- A. Campi, S. Ceri, A. Maesani, S. RonchiDesigning Service Marts for Engineering Search Computing Applications The Tenth International Conference on Web Engineering, ICWE
2010, Vienna, Austria, July 5-9 2010

Related
- M. Brambilla, S. Ceri, I. Celino, D. Cerizza, E. Della Valle, F. M. Facca, A. Turati, C. TziviskouExperiences in the Design of Semantic Services Using Web Engineering Methods and Tools
Journal on Data Semantics 2008- A. Raffio, D. Braga, S. Ceri, P. Papotti, M. Hernandez Clip: a Visual Language for Explicit Schema Mappings International Conference on Data Engineering (ICDE), April 2008
- D. Braga, D. Calvanese, A. Campi, S. Ceri, F. Daniel, D. Martinenghi, P. Merialdo, R. TorloneA New Generation Search Engine Supporting Cross Domain Queries Italian Symposium on
Advanced Database Systems (SEBD), June 2008
- D. Braga, D. Calvanese, A. Campi, S. Ceri, F. Daniel, D. Martinenghi, P. Merialdo, R. TorloneNGS: a Framework for Multi-Domain Query Answering IIMAS, International Conference on Data
Engineering Workshops (ICDE), April 2008
- A. Raffio, D. Braga, S. Ceri, P. Papotti, M. Hernandez Clip: a Tool for Mapping Hierarchical Schemas ACM SIGMOD/PODS Conference, Demo Session, June 2008
- A. Bozzon, M. Brambilla, P. FraternaliConceptual Modeling of Multimedia Search Applications Using Rich Process Models ICWE 2009, Springer LNCS, vol. 5648, ISBN 978-3-642-02817-5.
- E. Della Valle, S. Ceri, D. F. Barbieri, D. Braga, A. CampiA First Step Towards Stream Reasoning Future Internet Symposium (FIS) 2008, pp. 72-81.
- A. Bozzon, M. Brambilla, F. M. Facca, G. ToffettiCarughiA Conceptual Modeling Approach to Business Service Mashup Development IEEE International Conference on Web Services, ICWS
2009, Los Angeles. IEEE Press, July 2009, pp. 751 - 758.
- P. Fraternali, M. Brambilla, A. Bozzon, Model-Driven Design of Audiovisual Indexing Processes for Search-Based Applications Content-Based Multimedia Indexing, 2009, CBMI '09, IEEE Press,
ISBN: 978-1-4244-4265-2, pp. 120-125.
- D. F. Barbieri, D. Braga, S. Ceri, E. Della Valle and M. Grossniklaus, C-SPARQL: SPARQL for Continuous Querying Proceedings of WWW 2009, 18th International World Wide Web Conference
(Poster), Madrid, Spain, April 2009
- D. F. Barbieri, D. Braga, S. Ceri, E. Della Valle and M. GrossniklausContinuous Queries and Real-time Analysis of Social Semantic Data with C-SPARQL
In Proceedings of SDoW 2009, 2nd ISWC Workshop on Social Data on the Web, Washington, DC, USA, October 2009
- D. F. Barbieri, D. Braga, S. Ceri, E. Della Valle and M. GrossniklausC-SPARQL: A Continuous Query Language for RDF Data Streams International Journal of Semantic Computing (IJSC), 2010,
World Scientific Publishing
- D. F. Barbieri, D. Braga, S. Ceri and M. GrossniklausAn Execution Environment for C-SPARQL Queries In Proceedings of EDBT 2010, 13th International Conference on Extending Database Technology,
Lausanne, Switzerland, March 2010


Web Site & Blog 76

 Web Site

 TechWatch Blog

Blog stats: ~ 900 absoluteuniquevisitors in the last twomonths


Accessesto Web Site & Blog 77
Visits: 20% USA, 18% Italy, 6% UK, 4% India, 4% Canada
 Provenance

 Sources


Search Computing First Workshop
June 17-19, 2009 78


Search Computing Challenges and Directions
(LNCS, vol. 5950, Ceri-Brambilla eds.) 79

 Part 1: Vision
– Ceri: Search computing
– Baeza-Yates: Next generation search
– Weikum: Search for knowledge

 Part 2: Technology Watch
– Della Valle-Buganza-Gatti: The search engine industry
– Casati-Daniel-Soi: Mashup technologies
– Baumgartner-Campi-Gottlob-Herzog: Web data extraction
– Hedeler-Belhajjame-Campi-Embury-Fernandez-Paton:Dataspaces
– Bozzon-Fraternali: Multimedia and multimodal information retrieval

 Part 3: Issues in Search Computing
– Campi-Ceri-Gottlob-Ronchi: Service marts
– Braga-Campi-Grossniklaus: Join methods and query optimization
– Ilyas-Martinenghi-Tagliasacchi: Rank aggregation
– Braga-Grossinklaus-Ceri: Panta Rhei, a query execution environment
– Brambilla-Ceri-Fraternali-Manolescu: Liquid queries and liquid results
– Brambilla-Ceri: Software engineering of search computing applications
– Masseroli-Paton-Spasic: Search computing and the life sciences

Second Workshop: Design Principles 80

 Consolidate severalongoingresearchchapterstouching the
variousaspectsof the project
 Developconnectionstootherresearchprojects so asto share
knowledge - and possiblybuildcooperationsbased on
mutualcomplementarity.
 Settinginternaldeadlinesto project evolution
– Beingreadyfor the workshop
– Dump organisational responsibility to session chairs
 Try a more discussion-oriented format
– Ourview
– Guest’s views
– Panel/discussion (sometimes driven, sometimes not)
 Produce Proceedings as Springer LNCS, each session contributing
to a short part


Second SeCo Workshop Last Week 81


Second Workshop: Sessions 82

 Pre-Workshop (Milano, May 25)
– Searchas a Process
– Business Models

 Workshop (Como, May 26-28)
– SemanticResourceFramework
– WrappingTechnology and OntologicalAnnotation
– Design Tools and MashupLanguages
– SearchComputing and ResearchEvaluation
– Query Processing
– Rank Join
– SearchComputingforBioMedicalApplications
– User-CenteredApproachtoSearchComputingApplications

 Post-Workshop (Milano, May 31)
– VisualInterfacesforComplexSearch


Lookingforward 83

 Establishstrongerco-operationwithotherprojects
– Bothfortechnology and applications

 StrengthenSeCo “coreresearch”
– Cover the processlifecyclewithmethods&tools
– Improve result visualization and user interaction
– Usesemantics in service registration and query processing
– Turn PantaRheiinto a full Service Base Management System
(SBMS) withnewrank join methods, proximity, uncertainty…

 Strengthen the prototypes
– Fullydevelop the registrationenvironment
– Extend the executionenvironment, makeitscalableoverclouds
– Extend the liquid interface, cover mobile interfaces

 Put a “killer” application online (usable!)
 Exploreexploitationoptions


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