Person in charge: | (-) |
Others: | (-) |
Credits | Dept. |
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7.5 (6.0 ECTS) | EIO-CS |
Person in charge: | (-) |
Others: | (-) |
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Estimated time (hours):
T | P | L | Alt | Ext. L | Stu | A. time |
Theory | Problems | Laboratory | Other activities | External Laboratory | Study | Additional time |
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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.
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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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. |
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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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. |
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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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. |
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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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.
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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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. |
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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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. |
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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7,0 | 0 | 0 | 0 | 0 | 3,0 | 0 | 10,0 | |||
Presentation of current data mining / knowledge exploitation projects in various business sectors.
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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0 | 0 | 18,0 | 0 | 36,0 | 0 | 0 | 54,0 | |||
Students will develop the project in groups under the tutor"s supervision.
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
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2,0 | 0 | 0 | 0 | 8,0 | 0 | 0 | 10,0 | |||
Oral presentation of the project. Defending the proposed options and the results obtained.
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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.
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.
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.
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.
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).