Semantic integration of traditional and web-based
information sources
Gergely Lukácsy, BUTEPéter Szeredi, BUTE Péter Krauth, IQSYSAttila Bodnár, IQSYS
What is a mashup?
• A mashup is a website or application that combines content from more than one source into an integrated experience.
• The etymology of this term possibly derives from its similar use in pop music.
/Wikipedia/
Quotes on mashups• “Web mashups, and other Web 2.0 development
(e.g. Ajax) are all facets of the same phenomenon that : – information and presentation are being separated in
ways that allow for novel forms of reuse.”
• “The mash-up is the offspring of an environment where application developers facilitate the creation of integrated, yet highly derivative application hybrids by third parties, something they do by providing rich public APIs to their user base.”
What’s so special about mashups?
• Content used in mashups is typically sourced from a third party via a public interface or API.
• Other methods of sourcing content for mashups include web feeds (e.g. RSS or Atom), web services and screen scrapping.
• Some in the community believe that only cases where public interfaces are not used count as mashups.
• Many people are experimenting with mashups using Google, eBay, Amazon, Flickr, and Yahoos APIs.
• Google has a mashup editor in beta.
Mashup = Application Integration á la Web 2.0Mashup = Application Integration á la Web 2.0?
What we are going to speak of?S emantic
IN tegration
T echnology
A pplied in
G rid-like,
M odel-driven
A rchitectures
R&D project:• Sponsored by the National
Research and Development Program, 2005-2007
Consortia: • Coordinator: IQSYS • Developer Organisations:
• IQSYS, BUTE, SZTAKI• User Organisations:
• OSZK, MTI, ARECO/eBolt
Information Integration with Sintagma
SINTAGMA
Database A
Database B
Application A(web service)
Application B(traditional)
External Application(e.g. mashup application)
(RDBMS, XML, RDF)
• Separates clearly the data access and transformation layers of integration from the presentation layer
• Uses a comprehensive metadata repository (Model Warehouse)– Semantics of data represented in the repository: maps local and remote
metadata to each other– Data access and transformation driven by the repository
Data access and transformation
Presentation and further processing
Search and analysis application (e.g. mashup)
m e t a d a t a m a p p i n g
Search and analysis of Web data
d a t a s e r v i c e
m e t a d a t a m a p p i n g
m e t a d
a t a m a p
p i n
g
SINTAGMA-node
Legend:
Sintagma – an approach to information integration
• Key Principles:– No duplication of data: Model Warehouse vs. Data Warehouse– Communication: one-way, on-line (no modification of data, instant access)– Integration of web services as information sources supported (no modification
required)• Key Components:
– Manages various forms of metadata (Model Manager)– Accesses various structured and semi-structured information sources
(Wrappers):• RDBMS• RDF• XML• Web Services
– Preprocesses various „unstructured” information sources (Annotators):• Texts• Raster maps (labels and signs)• Excel tables
– Optimises query execution: query planning using deduction (Mediator)– Data Quality Control
Model Manager /Model Warehouse
Mediator(local)
SintagmaGUI
Data Quality
Controller
DQ Engine(meta)
RDBMSHTMLRDFWeb
Service
MapAnnotator
MapServer
DQ Engine(native)
mapstexts
TextAnnotator
XML
JDBCWrapper
WSWrapper
RDFWrapper
WDWrapper
XMLWrapper
Model Manager
(remote)DQ log
Data Quality Control subsystem
Text Annotation subsystem
Map Annotation subsystem
Architecture of SINTAGMA
Special concepts of business areas
Domain specific terminology
Conceptual Level
Interface Level
Application Level
Source Level
Integrated Application Model
local
local local
Domain specificknowledge/ontologies
Externalmodel
(e.g. BPM)
Data Source
n
Data Source
2
Data Source
1
transformedunified
Conceptual viewsof workers
in a business area
local
mapping
input
Legend:
model
Data Source
3
local
Integrated Conceptual Model
Common, clarifiedconcepts
Model Warehouse of SINTAGMA
Modelling in SINTAGMA
• The Model Warehouse– content of the Model Warehouse– interface models and abstractions– ontology concepts
• Use cases– Product comparison– Workflow of Equipment purchase– Web service integration demos
Model Warehouse• Content of the Model Warehouse
– Object-oriented models• Structural properties of sources in UML Object Model• Non-structural information given as OCL Constraints• Mapping between models as abstractions
– Description Logic models– Queries: source and conceptual level
• Classification of models – interface– unified (application)– conceptual
• Modeling: SILan – Semantic Integration Language– Describes content of Model Warehouse in textual format– Has well-defined semantics
Interface Models
Higher level models
• Abstractions (data transformations)– populate higher level entities
• Filter low level data (suppliers)• Transform data to appropriate higher level form (clients)
– can have multiple suppliers and clients
Higher level models (cont’d)• Invariants
– have to be satisfied by all the instances of a model element
– can contain navigation
• Queries– can be formulated on any model
• Interface level models: directly accessing data sources• higher level models: using mediation
– are interchangable with abstractions
Conceptual Models
Conceptual models (cont’d)
• These models encapsulate concepts given in Description Logic formalism
Use case 1: Product comparison
• Goal: find products that are similar to the products in a host system
• Information sources– catalogues from various vendors in Excel– database of the host system
• Problems to solve– heterogenity of the catalogues: preprocessing– algorithm for product comparison
Solution in SINTAGMA
Unified Products
CatalogueHost Database
Similar Products
MySQL XML
Excel
Excel
Excel
Model Warehouse
Product comparison
Preprocessing
Use case 2: Equipment purchase
Equipment purchase in an organisation
• Scenario– Each department maintains a wish-list of equipments– There are vendors who provide products to departments
• Vendors sell different types of products (vendor A sells printers and toners, Vendor B monitors and printers etc.)
• The financial department dynamically designates a preferred vendor for each product
• Questions: is there any expensive order? what is teh total ? etc.
• Information Sources:– Department’s wish-list:
• relational database with columns description, category, e.g.: „we have run out of paper”, „15/18”
– Financial department: • Web service, with operation determining where to buy a given
product, e.g.: (15,8) -> (A4 paper, 4, 23)– Vendors:
• Heterogenous web service which return prices, units and delivery date, e.g.: 23 -> (12, 1, 2007-07-01)
Event Driven Process Chain
Solution in Sintagma
Use case 3: Web Service Integration
• Integrating Amazon and Barnes&Noble
• Integrating RSS-sources (e.g. origo, nol, index, metro)
• Integrating World Championship Results (20o2 and 2006)
Integrating Amazon and Barnes&Noble
Conceptual Level
Interface Level
Application Level
Source Level
Amazon
Barnesandnoble.com
web service
Amazon.comweb service
Legend:
model
Currency exchange
service
currency
Barnes&Noble
AmazonBN
Price comparison
Availability under
limit in HUF
query
input
mapping
Integrating results of World Championships
Conceptual Level
Interface Level
Application Level
Source Level
2002 WC
Result(2002 WC)
Web service
query
input
Legend:
model
Unified WC matches
Result(2006 WC)
Web Service
2006 WC
Optimised WC matches
transformation
combination
Score: n-m
Match Id: 0-63
Score1: nScore2: mMatch Id: 1-64
Team matches
First FourTeams
Team matchesby year
Positions
grouping
derivation
Teams in both WCsMatches in both WCs
Matches of teams
mapping
No of matches
played by teams
Team positions by year
Integrating RSS-feeds
Conceptual Level
Interface Level
Application Level
Source Level
origo
Nol.hu RSS
source
Origo.huRSS-
source
Legend:
model
Index.huRSS-
source
index
nol
Unified RSS-feeds
Search for
occurances of a
specific word
(e.g. „budapest”)
metro.hu RSS
source
metro
query
input
mappingVIP data-base
VIP
TextAnnotator
goverment
opposition
Search forhigh level concepts
(e.g. political conflicts)
combinationmembers of
Summary• The system
– is a semantic information integration tool– handles various structured sources
• relational, various semi-structured sources and web services
– preprocesses various unstructured sources• texts, maps, tables
– uses logic / constraint logic programming– can be used in mashup creation
• disciplined and flexible approach to data access in mashups• separates data integration from mashup presentation logic• resolves semantic and technical differences in sources
Real estate search - Trulia
• A real estate search engine that helps you find homes for sale and provides real estate information at the local level to help you make better decisions in the process. Trulia pulls in real estate data from partnerships with thousands of brokers and agents and displays it on a Google Maps interface.
• Trulia shows you how sales prices have been trending where it matters—in your county, city, ZIP code and neighborhood. They also offer heat maps and real estate guides.
• http://www.trulia.com/#start
Hotel Guide - Trivop
• The self-proclaimed first videoguide for hotels doesn’t disappoint. Locate hotels on this Google Maps + Hotel mashup and view user-created videos of the hotels. This gives a much better view of a prospective hotel before visiting.
• Currently looks like they only have hotels in England and France, but with their recruiting efforts one can only assume Trivop will becoming to a region near you.
• http://www.trivop.com
Visual Music search – Music Map
• Visual music search application mashed with Amazon data. Choose and artist and album, see related artists in an abstract tree graph. Wicked.
• http://www.dimvision.com/musicmap/
Search for Popular Music – Hype Machine
• The Hype Machine follows music blog discussion. Every day, hundreds of people around the world write about music they love.
• The Hype Machine tracks a variety of MP3 blogs. If a post contains MP3 links, it adds those links to its database and displays them on the front page.
• Some of the frequently accessed tracks are cached by the Hype Machine server, much like Google Search caches web pages, to reduce load on the bloggers' servers and protect their bandwidth. Those tracks are NOT available for download, but you can preview them via the "listen" links that are next to each track or using your media player.
• The blog that posted a particular track is identified under every track by name and with a "read post" link that leads to the blog post itself. If you enjoyed a track someone posted, stop by and let them know!
• You can purchase CDs and individual tracks by using the "amazon" and "itunes" links that appear next to most tracks. Each purchase you make via the Amazon and iTunes links supports both the artists and the Hype Machine. Please buy and enjoy.
• http://hypem.com/
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