Ontologa del rendimiento: Una herramienta de apoyo para sistemas AmI

1
Ambient Intelligence, Semantic Web and
Performance Engineering Dr. Carlos
Juiz COMPUTER SCIENCE DEPARTMENT COLLOQUIUM The
University of Texas at Dallas September 28th, 2006
2
Motivation

• Ambient Intelligence (AmI), Semantic Web (SWeb)
  and Performance Engineering (PE) are not usually
  related all together
• AmI is a RD hot topic but it is not concerned
  about performance (until now)
• AmI can be partially supported by SWeb
  engineering (and could be not)
• PE was involved in Web originally from network
  performance and arrived to maturity through the
  application of on Web Performance Engineering
  techniques.
• Therefore it should use the same techniques for
  SWeb (and possibly it will be less useful for
  AmI)
• Lets overview how PE can be applied to AmI
  through SWeb techniques (this would be
  interesting not only for PE, but also for other
  non-functional activities of systems)

3
Outline

• A. AmI
• The vision of Ambience Intelligence (AmI)
• AmI and related topics
• Enabling Technologies
• AmI RDi
• B. SWeb and Ontologies
• Some historical remarks
• Ontologies, Semantic Web and Artificial
  Intelligence
• Simple Example
• Building ontologies OWL tools
• C. Towards Software Performance Ontology
  Engineering (SPOE)
• Software and Performance Engineering (SPE)
• SPE stories
• Performance Engineering of AmI
• Performance Ontology
• Simple case studies
• I. Operational Analysis Tools for building
  Ontology applications
• II. Intelligent Conference Room (AmI emulation)
  Reasoning and rules for SPOE
• D. Conclusions and References

4
A. Ambient Intelligence (AmI)

• Ambient intelligence is the vision of technology
  that will become invisibly embedded in our
  natural surroundings, present whenever we need
  it, enabled by simple and effortless
  interactions, attuned to all our senses, adaptive
  to the users, context-sensitive and autonomous.
  W. Weber, J.M. Rabaey and E. Aarts, 2005

5
A. AmI and related topics

• Several terms that share a common vision
• Pervasive Computing
• Ubiquitous Computing
• Wearable Computing
• Context Awareness
• Ambient Intelligence
• ...
• gt Different Technological Views?
• gt Lets briefly depict each one of these topics

6
A. AmI and related topics

• Pervasive computing
• The most profound technologies are those that
  disappear. They weave themselves into the fabric
  of everyday life until they are indistinguishable
  from it Mark Weiser, Scientific American, 1991
  16.
• The combination, communication and interaction of
  computer miniaturization, sensors and actuators
  together with embedded software artifacts.
• Smart spaces
• Freely available computing

• Challenges
• Location of people and objects
• Privacy control over the visibility of own
  location
• Interoperability
• Anytime
• Anywhere
• Any device
• Any data

7
A. AmI and related topics

• Ubiquitous Computing
• Computers everywhere that activate the world gt
  Computing has moved beyond the desktop and
  becomes part of everyday environments
• Real world artifacts are augmented with computers
• Challenges
• How to allow access always and everywhere
• Enabling transparent use of technology
• Compatibility with everyday life implicit
  human-computer interaction (iHCI)
• Power consumption
• Wireless communication

• Ubiquity For example, in ones office there
  would be dozens of computers, displays, etc. All
  would be networked. Wireless networks would be
  widely available to support mobile and remote
  access.
• Transparency This technology is non intrusive
  and is as invisible and as integrated into the
  general ecology of the home or work place as, for
  example, a desk, chair or book.

8
A. AmI and related topics

• Context Awareness
• Context is any information that can be used to
  characterize the situation of an entity. An
  entity is a person, place, or object that is
  considered relevant to the interaction between an
  user and an application, including the user and
  the application themselves.A. K. Dey and G.D.
  Abowd, GA-Tech, 1999

• Challenges
• Understanding structure and behavior of context
  information context are fuzzy, overlapping,
  changing in time
• Lack of sensing methods for context information
  from various sources
• Lack of methods for fusing context information
• Lack of format of context information
• Sensor based context recognition
• hard to obtain reliable data
• signal processing and recognition consume memory,
  energy and processing time
• results may be ambiguous
• Connectivity to environment and other devices
• Are profiles available how about location
  information?
• Any privacy security risks?

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A. AmI and related topics

• Wearable Computing
• On human body
• Integration into everyday's clothing, electronic
  components should be designed in a functional,
  unobstrusive, robust, small, and inexpensive way.
  
• wearable electronics can be exploited using
  off-the-shelf chip systems today.
• Mobile but cheap.

• Challenges
• power
• heat
• networking
• privacy
• user interface
• intelligence

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A. AmI and related topics

• Ambient Intelligence
• Envisions a world where people are surrounded by
  intelligent and intuitive interfaces embedded in
  the everyday objects around them. These
  interfaces recognize and respond to the presence
  and behavior of an individual in a personalized
  and relevant way.
• Make life simpler, more enjoyable and more
  interesting
• Challenge
• Enable new functionality without need to learn
  new technology

• Ambient Intelligence Technology is
• Invisible
• Use/functions are immediately apparent
• Ubiquitous
• Available anywhere, integrated in physical
  environment objects around us
• Intelligent
• Relevant to user context-aware
• Unobtrusive
• Providing meaning (data vs. information vs.
  knowledge)

11
A. Enabling technologies
Ambient Intelligence

• Multimodal user interfaces
• - display keys
• - speech recog.
• gesture recog.

• Technical features
• - low cost devices
• high bandwidth
• invisible storage
• automation
• personalize and privacy information

• Devices
• - mobile
• wireless
• augmented reality
• user location sensors
• status tracking
• user interaction

• Intelligence
• - reasoning
• agents
• ontologies
• knowledge management
• information semantics

• Local Communication
• - IrDA
• - Bluetooth
• - WiFi
• - RF
• - RFID
• - WLAN
• WSN

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A. Enabling technologies
Ambient Intelligence

• Multimodal user interfaces
• - display keys
• - speech recog.
• gesture recog.

• Technical features
• - low cost devices
• high bandwidth
• invisible storage
• automation
• personalize and privacy information

• Devices
• - mobile
• wireless
• augmented reality
• user location sensors
• status tracking
• user interaction

• Intelligence
• - reasoning
• agents
• ontologies
• knowledge management
• information semantics

• Communication
• - IrDA
• - Bluetooth
• - WiFi
• - RF
• - RFID
• - WLAN
• WSN

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A. Enabling technologies

• The Old AmI Technological View
• Functional
• The ambient systems/environment/background view
• The Intelligence user/person/foreground view
• The New AmI
• Systemic
• Components view
• Integration view

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A. Enabling technologies

• AmI Components
• For Ambient
• Smart materials, MEMS and Sensor Technology,
  Embedded systems, Ubiquitous communication, I/O
  device technology, Adaptive software,
• For Intelligence
• Media management and handling, Natural
  interaction, Computational intelligence
  (Knowledge Management, Decision Support Systems,
  AI, Intelligent Agents, Ontological Engineering),
  High performance computing,

15
A. AmI Research

• Drawbacks for RDi
• Technical complexity and fragility of proposed
  ambient application systems with special
  networking, sensoring and reasoning
• Too high costs of components, installation and
  management
• Ethical or other psycho-social obstacles that
  relate to the properties or alleged properties of
  ambient application systems, such as threat to
  personal privacy
• Purely "proof of concept" or non-interesting
  nature of the first attempted ambient application
  systems (PDA based museum guides, smart coffee
  cups), i.e. the application systems had no
  commercial potential or paying-customer-interest
• Too complicated or non healthy business chains.

16
Outline

• A. AmI
• The vision of Ambience Intelligence (AmI)
• AmI and related topics
• Enabling Technologies
• AmI RDi
• B. SWeb and Ontologies
• Some historical remarks
• Ontologies, Semantic Web and Artificial
  Intelligence
• Simple Example
• Building ontologies OWL tools
• C. Towards Software Performance Ontology
  Engineering (SPOE)
• Software and Performance Engineering (SPE)
• SPE stories
• Performance Engineering of AmI
• Performance Ontology
• Simple case studies
• I. Operational Analysis Tools for building
  Ontology applications
• II. Intelligent Conference Room (AmI emulation)
  Reasoning and rules for SPOE
• E. Conclusions and References

17
B. Ontology Engineering

• Main branch of Metaphysics
• Including philosophical disciplines
• The Ontology is the study of being
• how to define it and to classify it univocally
• Semantic representation of the objects, their
  features and the relations among them in a domain
  (specific)

Theology
Gnoseology
Ontology
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B. Some historical remarks

• Players of Ontology evolution

• Greek origins, 530 b.C., Parmenides considered
  creator of Ontology.
• Objects are eternal, invariantso, how can
  we define a seed since it will be a tree in the
  future?
• Aristotle contributes with a category system for
  classifying objects through features like
  passion, time, place,

• Tim Berners-Lee (www) suggests the use of
  ontologies to represent Web knowledge gt
  Semantic Web (SW)

• Emanuel Kant (XVIII c.) Reality cannot be
  reached, reality is transcendental. Possible
  approximations for building objects depend on the
  designer experience.

• In early 90s, Tom Gruber and Nicola Guarino,
  used ontologies to represent knowledge of
  computing systems. They combined philosophy,
  language theory and logics to establish a new
  field in Artificial Intelligence.

filosofía
Tom Gruber and Nicola Guarino
Parmenides
Artificial Intelligence
Kant
530 b.C.
1990
XVIII
Aristotle
Semantic Web
Tim Berners-Lee
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B. Ontologies, Semantic Web and Artificial
Intelligence

• Berners-Lee vision of Semantic Web layers.
  Ontology Engineering provides new searching tools
  to make engines more powerful and efficient. It
  provides also a huge knowledge base.

Belief
Knowledge
Guarantee
Inference
Semantic representation
Object description
Sintax
Object identification
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B. Ontologies, Semantic Web and Artificial
Intelligence

• Therefore, ontologies may be useful to
• Represent knowledge ? AI
• Providing coherent information and some inference
  due to the implicit knowledge
• Ease sharing knowledge ? SW
• Defining an universal knowledge (philosophy), a
  common knowledge for everybody
• Avoiding to redesign information structures
• Providing means of knowledge distribution and its
  management
• Understand natural language ? AI-SW
• Avoiding ambiguities
• Providing interaction among humans and machines ?
  AmI

21
B. Simple Ontology Example

• Select a domain This room, a little AmI
• Goal People-resources interaction.
• Concepts
• Person
• Resource
• Feature
• Utilization - Probability type
• Relation
• Uses
• Restriction
• Transitive
• Importing knowledge defined by others (in this
  case people), we improve the representation of
  our domain.

passport

• Instances / facts
• Nary, Lawrence, Carlos, John,
• Laptop, Projector, Cellular,

Other Ontologies
friends
Carlos
Uses
Uses
Laptop
Implicit knowledge
Uses
domain
Projector
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B. Building Ontologies

• There is no official standard to build
  ontologies, however its possible to use Software
  Engineering techniques Ontology lifes cycle

Ontology
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B. Building Ontologies

• An ontology language will be used to described
  the characteristics of devices, the services, the
  means of access to such devices, the policy
  established by the owner,
• OWL (Web Ontology Language), W3C standard (July
  2002)
• Evolution
• OWL provides description logics subset of
  predicate logics where the domains are depicted
  in terms of concepts, roles (properties and
  relations) and facts

OWL
OIL
DAMLOIL
OML
RDF(S)
XOL
SHOE
XML
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B. Simple Ontology Example
Isaac
Relation Uses ltowlTransitiveProperty
rdfID"Uses"gt ltrdfsdomaingt
ltowlClassgt ltowlunionOf
rdfparseType"Collection"gt ltowlClass
rdfabout"Person"/gt ltowlClass
rdfabout"Resource"/gt lt/owlunionOfgt
lt/owlClassgt lt/rdfsdomaingt
ltrdfsrange rdfresource"Resource"/gt
ltrdftype rdfresource"http//www.w3.org/2002/07/
owlObjectProperty"/gt lt/owlTransitivePropertygt
Fact of concept Person Isaac ltPerson
rdfID"Isaac"gt lthasName rdfdatatype"http//
www.w3.org/2001/XMLSchemastring" gtIsaac
Newtonlt/hasNamegt ltUses rdfresource"Laptop"/
gt lt/Persongt
Concept Person ltowlClass rdfID"Person"/gt Fea
ture of concept hasName ltowlDatatypeProperty
rdfIDhasName"gt ltrdfsdomain
rdfresource"Person"/gt ltrdfsrange
rdfresource"http//www.w3.org/2001/XMLSchemastr
ing"/gt lt/owlDatatypePropertygt Particular fact
in a domain Isaac ltPerson rdfID"Isaac"gt
lthasName rdfdatatype"http//www.w3.org/2001/XMLS
chemastring" gtIsaac Newtonlt/hasNamegt
lt/Persongt

Uses
Laptop
50
Concept Resource ltowlClass rdfIDResource"/gt
Features of the concept Utilization
ltowlDatatypeProperty rdfID"Utilization"gt
ltrdfsdomain rdfresource"Resource"/gt
ltrdfsrange rdfresource"http//www.w3.org/2001/X
MLSchemafloat"/gt lt/owlDatatypePropertygt Partic
ular fact in a domain Laptop ltResource
rdfIDLaptop"gt ltUtilization
rdfdatatype"http//www.w3.org/2001/XMLSchemaflo
at" gt0.5lt/Utilizationgt lt/Resourcegt
25
B. Protégé One historical tool to create
ontologies
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Outline

• A. AmI
• The vision of Ambience Intelligence (AmI)
• AmI and related topics
• Enabling Technologies
• AmI RDi
• B. SWeb and Ontologies
• Some historical remarks
• Ontologies, Semantic Web and Artificial
  Intelligence
• Simple Example
• Building ontologies OWL tools
• C. Towards Software Performance Ontology
  Engineering (SPOE)
• Software and Performance Engineering (SPE)
• SPE stories
• Performance Engineering of AmI
• Performance Ontology
• Simple case studies
• I. Operational Analysis Tools for building
  Ontology applications
• II. Intelligent Conference Room (AmI emulation)
  Reasoning and rules for SPOE
• E. Conclusions and References

27
C. Software and Performance Engineering (SPE)

• Originally it intends to develop tools allowing
  the performance prediction, since the first
  phases of the design of software systems.
• Since performance is not included in behavioral
  modeling, it is necessary to complement the
  design information with performance annotations.
• There are notably different design approaches to
  be used (WOSP, ACM conference)

28
C. SPE stories
Isomorphism among models? Feedback of results?
29
C. Performance Engineering of AmI

• Ideal
• To reason (on-line) about the performance and the
  functionality (together) of the system through
  intelligent applications based on ontologies.
• An ontology is a formal, explicit specification
  of a shared conceptualization.
• Conceptualization is referring to the abstract
  model building, i.e. the utilization (and reuse)
  of a formal language to specify models
• Modelling the performance through ontologies
  should be natural in AmI systems.

30
C. Performance Engineering of AmI

• Two complementary issues
• Performance modelling and evaluation of the
  Ambient Intelligence will allow to know more
  information about the system and...
• The ability of reasoning of the system will allow
  taking decisions about performance and other
  non-functional features.
• Primary objectives
• Defining performance ontologiesgt how to model
  the performance of a software system through the
  ontology language, e.g. OWL.
• Evaluating the performance using traditional
  techniques and also reasoning about it the model
  is the system!

31
C. Performance Ontology

• Following the life-cycle methodology we started
  to build an performance ontology.
• Reusing the knowledge (performance concepts) of a
  metamodel in SPE(UML 2.0 SPT profile)

32
C. Performance Ontology

• and adding perfomance metrics, which is crucial
  for performance engineering.

• For example, lets use the utilization law
  (operational)

Confidence criteria
has
Operations are not provided by the language We
have to build specific applications
Importing the knowledge ofDAML-Time
Common features for metrics
33
C. Case study Operational Analysis

• Requirements for building an application

Programming language C
Java,
Library to manage OWL
, WonderWeb OWL API
Protégé API,
Jena
less functional
includes
Building Java objects
Kazuki, Protégé Bean Generator ,
Jastor
specific
specific
Jena includes a reasoning engine for queries over
OWL and allows to construct a database of facts.
Jastor uses JavaBeans for concepts from
ontologies based on Jena.
34
C. Case study Emulation of an AmI

• Intelligent Conference Room
• The Performance Ontology represents the AmI
  emulation sensors, agents, resources, services,
• Services include tasks that may saturate the AmI
• A broker schedules the tasks and may reject
  services to avoid the saturation

35
C. Emulation of an AmI

• Facts (instances) in an Intelligent Conference
  Room

Conference Room
Services Reception Desk News Shared Information
Central Server Broker
Service Running
Person
Attendants
36
C. Emulation of an AmI

• Integration and Importing ontologies at the
  Intelligent Conference Room

37
C. Emulation of an AmI

• Importing personal information
• Friend of a friend (FOAF) concepts to describe
  people and their relations.
• Including priorities among people and the
  services that they are demanding.
• Including different states for demands
• Running, waiting, rejected
• Architecture of Agents
• Agents represent attendants, resources,
  components,they are comunicating and
  collaborating
• FIPA (Foundation for Intelligent Physical Agents)
  standard specifications
• JADE is a platform to implement FIPA agents
• Java compatibility
• Support for mobility and platform cloning

38
C. Emulation of an AmI
Indentification of people (FOAF) Jastor stores
information in the knowledge base Jastor stores
task registers in the knowledge base
Communicating service demands through FIPA
messages to the scheduler Jade generates people
agents
Emules attendants Jastor Generates workload
news, shared information and messages Stores
demands in the knowledge base Communicate to the
scheduler demands through FIPA messages
Emules arrivals of attendants Sends FIPA messages
to reception agent
Jena stores the knowledge base, expandedwith
FOAF, priorities, Jastor creates facts
hardware/software resourcesthe context JADE
creates agents
Event agent
Emulates execution of services Jastor updates
performance metrics of resources i.e utilization
Jastor updates service states
Arrival of attendants
Performance Ontology store
Reception desk agent
creates
Person agent
Other services
Registration service
Scheduler agent
Monitor agent
Service to be performed
39
C. Emulation of an AmI

• Determines the service to be run based on service
  priority, attendant priority and waiting time of
  tasks
• Resource utilization may provoke service
• The intelligence is provided by implicit
  knowledge through OWL axioms

Scheduler agent
Task waiting ? Task ? hasstate.waiting and Service
rejected Resource.Server ? Utilization gt
Value However in order to work with agents we
may use rules
40
C. Emulation of an AmI

• Application of rules activates others and then
  AmI reacts to changes, i.e. it adapts to the
  ambient.
• SWRL (Semantic Web Rules Language) W3C standard
• If (Resource.Server ? has_Utilization gt Value) ?
  Saturation
• If (Saturation) ? Reject Services.News
• OWL Inference engines
• Jena does not support SWRL
• Racer SWRL works but its impossible to compare
  numbers
• Pellet reads comparison but cannot understand
  them
• Fact C programming, does not support SWRL
• Cerebra commercial implementation
• Jess OWL cannot be used

Jena allows to use Queries over OWL
41
Conclusions
Performance Engineering
SPE
SPOE
PerformanceEngineeringAmI
Software Engineering
Ontology Engineering
Semantic Web
42
Thank you
Is the mark of an instructed mind to rest
satisfied with the degree of precision that the
subject admits, an not to seek exactness when
only an approximation to the truth is
possible Aristotle
43
References

• U. Tuomela (Nokia) Challenges in Developing
  Context Aware Mobile Terminals, ITEA 0003
  Ambience Project, Seminar on Ambient
  Intelligence, 2003.
• E. Tuulari (VTT, Phillips) The Constitutes of
  Pervasive Computing, ITEA 0003 Ambience Project,
  Seminar on Ambient Intelligence, 2003.
• H. Ailisto, A. Kotila, E. Strömmer (VTT) Ubicom
  Applications and Technologies, 2002.
• M. Luck, R. Ashri, M. dInverno Agent-Based
  Software Development, Artech House, 2004.
• P. Maes http//interact.media.mit.edu/mas961/
• T. Skordas, G. Metakides (EU Commission) Major
  Challenges in Ambient Intelligence, Studies in
  Informatics and Control, vol. 12, nº2, 2003
• D. Guerri, M. Lettere, R. Fontanelli Ambient
  Intelligence Tutorial, GOOD FOOD
  FP6-IST-1-508774-IP, WP 7, 2004

44
References

• (The European Union report) Scenarios for Ambient
  Intelligence in 2010 ftp//ftp.cordis.lu/pub/ist/d
  ocs/istagscenarios2010.pdf
• (Georgia Techs Aware Home) www.cc.gatech.edu/fce/
  ahri
• (MITs House_n) http//architecture.mit.edu/house_
  n
• (MITs Oxygen project for pervasive
  human-centered Computing) http//oxygen.lcs.mit.ed
  u/Overview.html
• (Philips vision of ambient intelligence)
  www.philips.com/research/ami
• Lera, I. Juiz, C. Puigjaner, R. Web
  Operational Analysis through Performance-related
  Ontologies in OWL for Intelligent Applications,
  Lecture Notes in Computer Science, Vol 3579, pp.
  612-615, 2005
• Haring, G. Juiz, C. Kurz, C. Puigjaner, R.
  Zottl, J. Framework for the Performance
  Assessment of Architectural Options on
  Intelligent Distributed Applications,
  Proceedings of PERMIS'04. NIST Special
  Publication 1036, Gaithersburg, 2004

45
References

• Lera, I. Juiz, C. Kurz, C. Puigjaner, R.
  Haring, G. Zottl, J.Performance Assessment on
  Ambient Intelligent Applications through
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  Workshop on software and Performance, WOSP 2005,
  Palma de Mallorca, pp. 195-204, 2005
• Lera, I. Juiz, C. Puigjaner, R.
  Performance-related ontologies and semantic web
  applications for on-line performance assessment
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• Lera, I. Juiz, C. Puigjaner, R. Zottl, J.
  Haring, G. Performance assessment of
  intelligent distributed systems through software
  performance ontology engineering (SPOE) Software
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• Berners-Lee, T., Hendler, J. and Lassila, O. The
  Semantic Web. Scientific American, May 2001.
• Davies, J. Fensel, D. van Harmelen, F. Towards
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References

• Guarino N. Formal Ontology and Information
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  pages 3-15. IOS Press, 1998.
• Hobbs, J.R. A DAML Ontology of Time. Part of the
  DARPA Agent Markup Language project.
• Petriu, D. Shen, H. Applying the UML Performance
  Profile Graph Grammar-based Derivation of LQN
  Models from UML Specifications. February 2002.
• Protégé http//protege.stanford.edu/
• Object Management Group (OMG) UML Profile for
  Schedulability, Performance and Time
  Specification, March 2002
• OWL-S http//www.daml.org/services/owl-s/
• SweDE http//projects.semwebcentral.org/
• SWRL http//www.w3.org/Submission/SWRL/

47
References

• UML Profile for Modeling Quality of Service and
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Where is Palma?

• Archipelago in Western Mediterranean Sea
• Autonomous Community Balearic Islands
• Island Majorca (pop. 0.6 Million)
• Country Spain (pop. 44 Million)
• University of Balearic Islands (state)
• - 14 K students (small-medium size)
• - Ranking Spain 15 of 100

49
Pleasure
50
Sunshine and Beach
Pollença (Mallorca)
Magalluf (Mallorca)
51
Sunshine and Beach
Cala Figuera (Mallorca)
Es Trenc (Mallorca)
52
Sunshine and Beach
53
Small Villages
Andratx
Alcudia
Sant Elm
Valldemossa
54
and culture
55
Palma de Mallorca

• The Balearic Islands Archipelago is situated in
  Southwest Europe, in the centre of the Western
  Mediterranean basin.
• The city of Palma is the capital of the Balearic
  Islands, located in the island of Majorca.

56
The Cathedral

• Gothic style whose sandstone walls and flying
  buttresses seem to rise out of the sea.
• Year's Day 1230, a day after the fall of Palma,
  the foundation stone was symbolically laid on the
  site of the city's main mosque. Work continued
  for 400 years - and had to resume in 1851 when an
  earthquake destroyed the west front. More touches
  were added this century by the Catalan architect,
  Antoni Gaudi. Some works inside of modern
  painters as Miquel Barceló.

57
Bellvers Castle

• A well-preserved l4th-century royal fortress
  with fragrant pine woods,
• an interesting museum and view over Palma Bay.
• It is unique among Spanish castles in being
  entirely round. Three large towers
• surround a central courtyard, connected by an
  arch to a free standing keep.

58
Almudaina Palace

• Opposite the cathedral in Palma stands an
  austere fortress palace that was erected by the
  Moors and later became the residence of the kings
  of Mallorca.
• The palace is surrounded by a pleasant
  Moorish-style garden sporting fountains, which
  offers panoramic views of the harbour.

59
Palma Courtyards
Can Vivot (18th century)

• Can Juny (16th century)

60
Palma Courtyards
Cal compte de la Cova (19th century)

• Can March (19th century)

61
Palma Courtyards
Can Bennàsser (17th century)

• Can Sales (14th century)

62
Palma Courtyards
Estudi General Lullià (20th century)

• Can Ordines d'Almadrà (20th century)

63
Ancient Churches
San Francisco
Montesion

• Santa Eulalia

64
Historical Buildings
Arab Baths
Gran Hotel
Sa Llotja
65
Historical Buildings
Consolat de la Mar
Town Hall
66
Art Museums
Palau Solleric
Museu March