Berendt: Advanced databases, first semester 2009, http://www.cs.kuleuven.ac.be/~berendt/teaching 1 Advanced databases – Introduction and overview Prof. Dr. Bettina Berendt Katholieke Universiteit Leuven, Department of Computer Science http://www.cs.kuleuven.ac.be/~berendt/teaching/2009-10-1st semester/adb/ ast update: 23 September 2009
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1Berendt: Advanced databases, first semester 2009, http://www.cs.kuleuven.ac.be/~berendt/teaching
1
Advanced databases –
Introduction and overview
Prof. Dr. Bettina Berendt
Katholieke Universiteit Leuven, Department of Computer Science
3. ls -1 | xargs grep -HiFof /Volumes/UFS/terms.txt > /Volumes/UFS/matches.txt (or search by ISBN):
search term (or ISBN) {person name + city}
4. http://people.yahoo.com/
book {name + address}
5. http://www.ontok.com/geocode :
book {geo-coordinates}
6. Google Maps API: insert geo-coordinates into map
13Berendt: Advanced databases, first semester 2009, http://www.cs.kuleuven.ac.be/~berendt/teaching
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So: What is this course about? (2) – What will it be about?
The database field profits from a well-understood, well-functioning, commonly-used general model: relational databases
You have learned about this in the Databases course
Relational databases: a „homogenizing model“
What else makes databases so powerful today ?
Semantic integration of heterogeneous data
Integration over the Internet/Web
Analysis beyond retrieval: „Knowledge discovery (in databases)“ aka „Data mining“
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Outline of the course
Lectures (see Web page)
Exercises progress from small „Bachelor-type exercises“ to a larger joint „mini-
project“ with distributed teams
conceptual elements (modelling), tool use, programming, reports
Will be similar in structure to last year:
1. Create a conceptual model in UML of ...
2. Model the same domain in OWL
3. Federated search: Retrieve information from different databases
4. Convert information (2008: XML the OWL model created in ex. 2)
5. Extract implicit knowledge from a given relational database table
6. Extract implicit knowledge from a given semi-structured dataset
7. Knowledge discovery from real data on the Web (Wikipedia): retrieval, preprocessing, semantic enrichment, model integration, pattern extraction, visualisation, model comparison
15Berendt: Advanced databases, first semester 2009, http://www.cs.kuleuven.ac.be/~berendt/teaching
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Learning outcomes: After this course, you will ...
understand and master relevant concepts and techniques of current databases and processing based on databases
understand the potentials, limitations, and risks inherent in assembling, combining, and processing huge amounts of heterogeneous data in globally interconnected environments
be able to design such databases and connectivity and relevant methods for combining and enriching data
have worked with concrete examples of such data collection / processing
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Agenda
Organisation of the course
Motivation and overview
Data, information, and knowledge
Conceptual modelling, schemas, and ontologies
Recap: Entity-relationship model for data modelling
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Data and information
Datum / Data
Fact or concept from reality, in a form suitable for communicating it, interpreting it, and processing it
Information
Interpreted data
Example:
The length of the road is 400 km
Interpretation Data
(based on Henk Olivié: Gegevensbanken – 01. 2006/07)
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Data, information, and knowledge
Data represents a fact or statement of event
without relation to other things. Ex: It is raining.
Information embodies the understanding of a relationship of some sort, possibly cause and effect.
Ex: The temperature dropped 15 degrees and then it started raining.
Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next.
Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains.
(This is from knowledge-management theory. If you want to know about wisdom, check the Web page:
G. Bellinger, D. Castro, & A. Mills: Data, Information, Knowledge, and Wisdom. http://www.systems-thinking.org/dikw/dikw.htm )
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„Knowledge“ as used in this course
Data represents a fact or statement of event
without relation to other things. Ex: It is raining.
Information embodies the understanding of a relationship of some sort, possibly cause and effect.
Ex: The temperature dropped 15 degrees and then it started raining.
Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next.
Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains.
This definition of „knowledge“ corresponds to that used in Data mining (aka „knowledge discovery (in databases)“) (in particular symbolic) AI (e.g., „knowledge-based systems“)
It is not the only definition; e.g., cognitive psychology generally assumes that only people can have knowledge, such that computers can only possess (different types of) information.
20Berendt: Advanced databases, first semester 2009, http://www.cs.kuleuven.ac.be/~berendt/teaching
20Computerizing data, information, and knowledge:Databases and knowledge bases
Databases
= data + interpretation (metadata)
focus on data and information
= focus on the retrieval of data and information
Knowledge bases
a special kind of database
provide the means for the computerized collection, organization, and retrieval of knowledge
focus on knowledge
= focus on the inferences that can be made from data+information
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21Combining data and knowledge from different sources:The importance of conceptual models
To combine data from different databases:
know + integrate their conceptual models
To combine data from databases and knowledge bases:
1. understand the commonalities and differences of their conceptual meta-models
Simplified:
database conceptual models = entities + relations
knowledge base conceptual models = entities + relations + rules for inferencing
2. integrate these conceptual models (as for databases)
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Agenda
Organisation of the course
Motivation and overview
Data, information, and knowledge
Conceptual modelling, schemas, and ontologies
Recap: Entity-relationship model for data modelling
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Conceptual modelling as a part of database design
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Conceptual database schemas and conceptual models in general
Conceptual schema: a concise description of the data requirements of the users
includes detailed descriptions of the entity types, relationships, and constraints
does not include implementation details
can be used to communicate with non-technical users
(Elmasri, R. & Navathe, S.B. (2007). Fundamentals of Database Systems. Boston: Addison Wesley. 5th Edition. p. 60)
Conceptual model a theoretical construct that represents something, with a set of variables
and a set of logical and quantitative relationships between them.
describes the semantics of the modelled domain
Models in this sense are constructed to enable reasoning within an idealized logical framework
Often in the form of an ontology, or having an ontology as a part
– Ontology (a simple definition): ~ schema plus axioms for inference
25Berendt: Advanced databases, first semester 2009, http://www.cs.kuleuven.ac.be/~berendt/teaching
Typically, the conceptual model(s) that are developed are captured in a software tool, using a particular conceptual modeling language.
Entity-relationship models (ERM)
Unified modeling language (UML)
But also: resource description framework (RDF), Web ontology language (OWL)
Conceptual modeling is one of the key activities in developing computerized systems for two important reasons.
Firstly, more and more, it is now possible to use computerized tools that can generate part (or sometimes all) of a computer application from the conceptual models encoded in standardized modeling languages [such as UML].
Secondly, computerization of enterprises continues with a focus on integrating systems.
Integration of systems requires an understanding of the semantics of each of the systems to be integrated.
The availability of conceptual models for the participant systems can facilitate the integration process and will require the involved staff to be fluent with the basics of the models employed and to have some modeling capabilities of their own. ...
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Agenda
Organisation of the course
Motivation and overview
Data, information, and knowledge
Conceptual modelling, schemas, and ontologies
Recap: Entity-relationship model for data modelling
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Recap: Conceptual modelling in the Entity-Relationship Model