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Information Exploitation Project (PEI)

Credits Dept.
7.5 (6.0 ECTS) EIO-CS


Person in charge:  (-)

General goals


Specific goals


  1. Familiarisation with the role played by data, information and knowledge in business processes.
  2. Learn how to plan and design processes for exploiting information.
  3. Integration of information management techniques, quantitative and qualitative techniques for treating information and knowledge.
  4. Introduce students to the use of knowledge models and mathematical functions for modelling the relationships between business variables.
  5. Concept of result presentation systems.
  6. Familiarisation with business data mining projects and tools.
  7. Learn specific programming tools and how to use them.


  1. Ability to identify Data Mining business situations.
  2. Ability to identify the quantitative techniques quantitatives (statistics, operational research and/or computing) and the qualitative techniques (AI, inductive learning, data mining) that are most appropriate for solving a given problem.
  3. Ability to establish reliable quantitative analytical methods and/or qualitative inductive methods providing decision-making support.
  4. Implement data-gathering computing systems.
  5. Implementation and/or integration of information treatment software components.
  6. Ability to determine the best form of user interaction.
  7. Documentation of all project stages.
  8. Graphic presentation of results.
  9. Ability to plan and schedule a project and set its objectives.


  1. Teamwork
  2. Ability to solve quantitative and/or qualitative problems in a business context, providing decision-making support.
  3. Drawing up reports and orally defending them.
  4. Ability to integrate data mining tools and results.


Estimated time (hours):

T P L Alt Ext. L Stu A. time
Theory Problems Laboratory Other activities External Laboratory Study Additional time

T      P      L      Alt    Ext. L Stu    A. time Total 
5,0 0 0 0 0 2,0 0 7,0

1. Introduction.

2. Importance of quantitative and qualitative models in companies.

3. Examples of systems for extracting and exploiting information and knowledge.

4. The various parts of a data mining project.

5. Explanation of the project and discussion of the alternatives.

6. Explanation of the development plan, the objectives of each stage, and the methodology to be followed.

7. Explanation of the content of each assignment submission and the assessment criteria employed.

T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 3,0 0 0 2,0 0 7,0
Based on the knowledge acquired throughout the course, determine the most suitable alternatives for extracting and exploiting information and knowledge depending on the objectives, limitations, and resources in each case.

T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 3,0 0 0 2,0 0 7,0
1. Data analysis, integration, merging, and treatment of missing data.

2. Data-gathering types. Automatic data-gathering systems.

3. Filters. Quality. Security and confidentiality.

T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 3,0 0 0 2,0 0 7,0
1. Data management computing systems. Metadata.

2. Metadata conceptual model.

3. Special focus for information and knowledge extraction and exploitation systems.

T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 0 6,0 0 6,0 8,0 0 24,0
1. Specialised software for use in statistical techniques, simulations, operational research, AI, data mining, and automatic learning.
2. Demonstrations and proofs.
3. Selection of components and integration.

T      P      L      Alt    Ext. L Stu    A. time Total 
1,0 0 3,0 0 0 1,0 0 5,0
Determining the most suitable ways of communicating with users, depending on applications, business sectors, and organisation types.

T      P      L      Alt    Ext. L Stu    A. time Total 
1,0 0 3,0 0 3,0 1,0 0 8,0

1. Designing the data structure for results.

2. Visual optimisation and graphic representation of results.

3. Visual ergonomy.

T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 3,0 0 3,0 2,0 0 10,0

1. Validating models, data, flows, and information. Testing. Quality criteria.

2. Technical information. Documentation standards. Documentation help tools.

T      P      L      Alt    Ext. L Stu    A. time Total 
7,0 0 0 0 0 3,0 0 10,0
Presentation of current data mining / knowledge exploitation projects in various business sectors.

T      P      L      Alt    Ext. L Stu    A. time Total 
0 0 18,0 0 36,0 0 0 54,0
Students will develop the project in groups under the tutor"s supervision.

  • Laboratory
    Interviews with the project tutor to discuss alternatives, resolve questions, receive comments on submissions, and in general, monitor project progress.
  • Additional laboratory activities:
    Work on project development outside class hours. This includes time spent with other members of the group and in off-line interaction (for example, by e-mail) and with the tutor.

T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 0 0 8,0 0 0 10,0
Oral presentation of the project. Defending the proposed options and the results obtained.

Total per kind T      P      L      Alt    Ext. L Stu    A. time Total 
28,0 0 42,0 0 56,0 23,0 0 149,0
Avaluation additional hours 3,0
Total work hours for student 152,0



The projects proposed by students will exhibit the following features:

- Various business projects for exploiting information and knowledge will be proposed. All of them provide a minimum solution. However, students may achieve more complex solutions if they so choose.

- The projects involves integrating various components.

- Sizeable project components are: data structures; algorithmic and statistical treatment and/or operational research, data mining and/or artificial intelligence.

- Students will create a comprehensive system covering all the stages of data and knowledge extraction.

- The project will be implemented by teams and draw on components from other projects.

The approach adopted will be as follows:

- Groups will be formed, comprising up to 4 students.

- Each group will be assigned a tutor.

- A minimum of 4 problems will be set each term. Each group will choose a problem to be solved, or negotiate their own proposal with the teacher.

- Each student will have his/her exclusive responsibilities within the group.

Development plan

The first week of the course will be given over to presentation of projects and forming of work groups.

Each group must formally set out the project schedule. The submission deadlines will be decided in the light of this schedule.

The submission will be as follows:

- First submission: Report defining the project: the project chosen, project components, the person in charge of each component, and the project schedule. Explanation of the problem to be solved and the programme"s technical and user requirements. This covers a problem in Natural Language, a detailed description of the functions to be developed, a model of the problem domain, and a list of the non-functional requirements for the programme.

- Second submission: Specification, design and analysis of the overall project and its components. In order to prevent unnecessary repetition of work, students should carry out a preliminary analysis of the proposed objectives before submitting the final design. This implies splitting the submission into two parts: a) specification of the overall project and b) specification of each of its components. A session will be specifically devoted to the preliminary evaluation.

Specification, design and analysis of the overall project and its components.

- Third submission: Progress report halfway through the project.

- Fourth submission: Project final report. Groups make a public presentation lasting roughly an hour each, in which students demonstrate their system and respond to the tutor"s questions.

Docent Methodolgy

Learning will be in groups, and will follow the case-study methodology and based on a list of project proposals. In addition, the course will foster contact with cutting-edge companies involved in big information exploitation projects: TSS, AIS, LCFIB, TNS, Aleasoft, etc.

Theory classes present the general course contents, explanations on the problems to be solved in the project, the methodology to be pursued at each stage, and the materials to be included in each submission.

The teacher uses some of these classes (normally at the beginning of the course) to briefly present notation schemes, languages, libraries and tools. Students take the initiative in most of the lab classes. Groups devote their time in these classes to working together, asking the teacher questions, and receiving his/her comments on previously submitted work.

Given that the course comprises projects, a significant part of the work involved is undertaken by groups of students working outside class hours.

Evaluation Methodgy

The assessment will be based on four project submissions, which will be weighted as follows:

Submission 1 (Definition and overall design of the system): 10%

Submission 2 (Specifications and analysis of each component): 15%

Submission 3 (Mid-term report): 15%

Submission 4 (Final): 60%

These submissions will be made at regular intervals throughout the term in order to ensure smooth development of the project.

Project assessment will take into account students" individual contributions and the results attained by their respective groups (which will also be reflected in each student"s grade). The final group grade (NG) is calculated by applying the above percentages to each submission. The student participation grade (NE) will take into account the individual tasks assigned during the various stages of the the project. The final course grade for each student will be calculated using the following formula N = 0.5*NG + 0.5*NE.

The points assessed in the final report cover: the extent to which the objectives were attained; the completeness of the system and how well it works; the quality of the work done on the system"s component parts - design, coding, interface, presentation of the results; and the adequacy of user and technical documentation.

Basic Bibliography

  • Dewhurst, F. Quantitative Methods for Business and Management, McGraw-Hill, 2002.
  • Vasant Dhar, Roger Stein Intelligent decision support methods : the science of knowledge work, Prentice Hall, 1997.
  • George M. Marakas Decision support systems in the 21st century, Pearson Education, 2003.
  • Clare Morris Quantitative approaches in business studies, Prentice Hall-Financial Times, 2003.
  • James A. Senn Information technology : principles, practices, and opportunities, Pearson Prentice Hall, 2004.

Complementary Bibliography

  • Jon Curwin and Roger Slater Quantitative methods for business decisions, Thomson, 2002.
  • Vasant Dhar, Roger Stein Seven methods for transforming corporate data into business intelligence, Prentice-Hall,, 1997.
  • Usama M. Fayyad ... [et al.], [editors] Advances in knowledge discovery & data mining, AAA/MIT Press, 1996.
  • Michel R. Klein, Leif B. Methlie Knowledge-based decision support systems with applications in business, John Wiley & Sons, 1995.
  • Tom M. Mitchell Machine learning, The McGraw-Hill Companies, 1997.
  • Ian H. Witten, Eibe Frank Data mining : practical machine learning tools and techniques with java implementations, Morgan Kaufmann Publishers, 1999.

Web links









Previous capacities

Students should previously have familiarised themselves with the following concepts in order to follow the course:

- Mechanisms for structuring information. Ability to use and programme data structures (tables, linear structures, dictionaries, etc.).
- Design of computing systems.
- Data Mining methods, Automatic Learning, Forecasting, Operational Research Methods, Simulation).


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