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Erasmus Mundus in Information Technologies for Business Intelligence (IT4BI – DC) # PROGRAM IN EXTINCTION #

Business intelligence (BI) allows organisations to gather and analyse internal and external data in order to create knowledge and value for the business and provide support for decisions at the strategic, tactical and operational level. Today, the success of a business is based on the constant transformation of data into information through efficient collection, cleansing, transformation, integration, analysis and monitoring. As a result, information has become an asset, and ICTs are key to using this asset to gain a competitive edge.
Today, companies have to deal with the problem of handling incredibly large amounts of data, also known as “big data”. Increasingly, this big data can be found in non-traditional formats, such as unstructured Twitter and social media (e.g., Facebook) messages and those possessing location information (e.g., GPS data). Sectors requiring advanced applications, such as those related to energy, public services and geo-social networks, need new products and technologies that can support the advanced analytical functions that utilise these data. The world’s largest software companies have realised this and are investing large amounts of money into research and development to tackle related technological challenges.
From this analysis, we can infer the following needs:
N1: A large number of highly qualified BI researchers are needed at academic institutions and across European industries.
• N2: Meaningful research is needed that enables useful information to be extracted from ever larger and more diverse sources of data.
• N3: New BI products and technologies are needed to provide advanced analyses of large volumes of data in specific fields.
• N4: European industries require knowledge of the latest BI techniques.
• N5: Unified organisation across Europe is needed in order to coordinate, lead and carry out research in BI.
Despite the critical importance of these needs to businesses and society, current BI research programmes are falling short. Europe needs a coordinated programme that takes into account the range of BI-related issues that will have to be addressed in the near future (and even today). The current IT4BI Erasmus Mundus Master’s programme, as with other computer science programmes, aims to produce graduates in the field of BI. But a doctoral programme is required for students to continue their training and education in areas related to BI so that they can produce the cutting-edge technologies needed to tackle the critical challenges that will arise in the years to come.
The IT4BI-DC programme is designed to meet this specific need. Its main objective is to prepare researchers who truly understand cutting-edge BI techniques and who can turn these techniques into innovative solutions. Graduates will be prepared for careers both in BI research as well as equally important careers in BI in ICT, the service industry and public service. All of this will be achieved via a solid interdisciplinary combination of quality researchers as main partners and associates from related industries, the world of academia and public institutions. The objectives of IT4BI-DC over the next 5 to 7 years, with regard to the previously mentioned needs, can be summarised as follows:
• O1: To educate and train more than 100 doctoral candidates in BI in Europe so that they may undertake careers in research and innovation, thus meeting need N1.
• O2: To provide meaningful contributions to BI research and publish findings via major scientific communication channels, thus meeting need N2.
• O3: To contribute to the development of advanced BI technologies and products, thus meeting need N3.
• O4: To establish the BI/economic development research network (eBird), which brings together BI researchers, vendors, professionals and users, who use the network to disseminate the knowledge produced, thus meeting need N4.
• O5: To establish a pan-European centre of excellence in BI as the nucleus of the eBird network. It will be comprised of the programme’s main partners, in addition to datasets and teams, and it will last beyond the duration of the project. This will meet need N5.

Other Universities

Aalborg University (Denmark)
Poznan University of Technology (Poland)
Technische Universität Dresden (Germany)
Université Libre de Bruxelles (Belgium)

COORDINATOR

Abello Gamazo, Alberto

CONTACT

C/ Jordi Girona, 1-3
Building B4 - 003
08034 Barcelona

doctorat.it4bi@upc.edu
Tel: 934 137 836

Programme website

General information

Access profile

This doctoral programme is the natural continuation of the Erasmus Mundus Master’s programme with the same name. However, any student with a master’s degree in engineering or computer science can apply. Though there is a business-analysis aspect to the programme, students must have prominently technical backgrounds. In addition to this academic profile, certain personal characteristics are also considered important, such as interest in the research projects being carried out in the programme, critical and analytical abilities, having initiative, being consistent and persistent with work, having the ability to work in a team and being able to communicate properly both in writing and orally (especially in English).

Output profile

Doctoral candidates who complete a doctoral degree will have acquired the following competencies, which are needed to carry out quality research (Royal Decree 99/2011, of 28 January, which regulates official doctoral studies):

a) A systematic understanding of the field of study and a mastery of the research skills and methods related to the field.
b) An ability to conceive, design or create, put into practice and adopt a substantial process of research or creation.
c) An ability to contribute to pushing back the frontiers of knowledge through original research.
d) A capacity for critical analysis and an ability to assess and summarise new and complex ideas.
e) An ability to communicate with the academic and scientific community and with society in general as regards their fields of knowledge in the manner and languages that are typical of the international scientific community to which they belong.
f) An ability to foster scientific, technological, social, artistic and cultural progress in academic and professional contexts within a knowledge-based society.

The award of a doctoral degree must equip the graduate for work in a variety of settings, especially those requiring creativity and innovation. Doctoral graduates must have at least acquired the personal skills needed to:

a) Develop in contexts in which there is little specific information.
b) Find the key questions that must be answered to solve a complex problem.
c) Design, create, develop and undertake original, innovative projects in their field.
d) Work as part of a team and independently in an international or multidisciplinary context.
e) Integrate knowledge, deal with complexity and make judgements with limited information.
f) Offer criticism on and intellectually defend solutions.

Finally, with respect to competencies, doctoral students must:
a) have acquired advanced knowledge at the frontier of their discipline and demonstrated, in the context of internationally recognised scientific research, a deep, detailed and well-grounded understanding of theoretical and practical issues and scientific methodology in one or more research fields;
b) have made an original and significant contribution to scientific research in their field of expertise that has been recognised as such by the international scientific community;
c) have demonstrated that they are capable of designing a research project that serves as a framework for carrying out a critical analysis and assessment of imprecise situations, in which they are able to apply their contributions, expertise and working method to synthesise new and complex ideas that yield a deeper knowledge of the research context in which they work;
d) have developed sufficient autonomy to set up, manage and lead innovative research teams and projects and scientific collaborations (both national and international) within their subject area, in multidisciplinary contexts and, where appropriate, with a substantial element of knowledge transfer;
e) have demonstrated that they are able to carry out their research activity in a socially responsible manner and with scientific integrity;
f) have demonstrated, within their specific scientific context, that they are able to make cultural, social or technological advances and promote innovation in all areas within a knowledge-based society;
g) have demonstrated that they are able to participate in scientific discussions at the international level in their field of expertise and disseminate the results of their research activity to audiences of all kinds.

Number of places

10

Duration of studies and dedication regime

Duration
The maximum period of study for full-time doctoral studies is three years, counted from the date of admission to the programme to the date of submission of the doctoral thesis. The academic committee of the doctoral programme may authorise a doctoral candidate to pursue doctoral studies on a part-time basis. In this case, the maximum period of study is five years, counting from the date of admission to the programme to the date of submission of the doctoral thesis. For calculating these periods, the date of admission is considered to be the date of the first enrolment for tutorials, and the date of submission the moment in which the Doctoral School officially deposits the doctoral thesis.

For full-time doctoral candidates, the minimum period of study is two years, counted from the date of an applicant's admission to the programme until the date on which the doctoral thesis is deposited; for part-time doctoral candidates it is four years. When there are justified grounds for doing so, and the thesis supervisor and academic tutor have given their authorisation, doctoral candidates may request that the academic committee of their doctoral programme exempt them from the minimum period of study requirement.

The calculation of periods of study will not include periods of absence due to illness, pregnancy or any other reason provided for in the regulations in force. Students who find themselves in any of these circumstances must notify the academic committee of the doctoral programme, which, where appropriate, must inform the Doctoral School. Doctoral candidates may also temporarily withdraw from the programme for up to one year, and this period may be extended for an additional year. Doctoral candidates who wish to interrupt their studies must submit a justified request to the academic committee of the doctoral programme, which will decide whether or not to approve the request. Each programme will establish conditions for readmission to doctoral studies.

Extension
If full-time doctoral candidates have not applied to deposit their thesis by the end of the three-year period of study, the academic committee of the programme may authorise an extension of up to one year. In exceptional circumstances, a further one-year extension may be granted, subject to the conditions established by the corresponding doctoral programme. In the case of part-time doctoral candidates, an extension of two years may be authorised. In both cases, in exceptional circumstances a further one-year extension may be granted by the Doctoral School's Standing Committee, upon the submission of a reasoned application by the academic committee of the doctoral programme.

Dismissal from the doctoral programme
A doctoral candidate may be dismissed from a doctoral programme for the following reasons:

  • The doctoral candidate submitting a justified application to withdraw from the programme.
  • The maximum period of study and of extensions thereof ending.
  • The doctoral candidate not having enrolled every academic year (unless he or she has been authorised to temporarily withdraw).
  • The doctoral candidate failing two consecutive assessments.
  • The doctoral candidate having disciplinary proceedings filed against him or her that rule that he or she must be dismissed from the UPC.

Dismissal from the programme implies that doctoral candidates cannot continue studying at the UPC and the closing of their academic record. This notwithstanding, they may apply to the academic committee of the programme for readmission and the committee must reevaluate them in accordance with the criteria established in the regulations.

Organization

COORDINATOR:
ACADEMIC COMMISSION OF THE PROGRAM:
Other Universities:
Aalborg University (Denmark)
Poznan University of Technology (Poland)
Technische Universität Dresden (Germany)
Université Libre de Bruxelles (Belgium)
STRUCTURAL UNITS:
  • Department of Service and Information System Engineering (PROMOTORA)
Specific URL of the doctoral program:
https://www.essi.upc.edu/es/docencia-es/estudios-de-doctorado-1

CONTACT:

C/ Jordi Girona, 1-3
Building B4 - 003
08034 Barcelona

doctorat.it4bi@upc.edu
Tel: 934 137 836


Access, admission and registration

Access profile

This doctoral programme is the natural continuation of the Erasmus Mundus Master’s programme with the same name. However, any student with a master’s degree in engineering or computer science can apply. Though there is a business-analysis aspect to the programme, students must have prominently technical backgrounds. In addition to this academic profile, certain personal characteristics are also considered important, such as interest in the research projects being carried out in the programme, critical and analytical abilities, having initiative, being consistent and persistent with work, having the ability to work in a team and being able to communicate properly both in writing and orally (especially in English).

Access requirements

Applicants must hold a Spanish bachelor’s degree or equivalent and a Spanish master’s degree or equivalent, provided they have completed a minimum of 300 ECTS credits on the two degrees (Royal Decree 43/2015, of 2 February)

In addition, the following may apply:

  • Holders of an official degree awarded by a university in Spain or any other country in the European Higher Education Area, pursuant to the provisions of Article 16 of Royal Decree 1393/2007, of 29 October, which establishes official university course regulations, who have completed a minimum of 300 ECTS credits on official university degrees, of which at least 60 must be at the master's degree level.
  • Holders of an official Spanish bachelor’s degree comprising at least 300 credits, as provided for by EU regulations. Holder of degrees of this kind must complete bridging courses unless the curriculum of the bachelor’s degree in question included research training credits equivalent in value to those which would be earned on a master's degree.
  • Holders of an official university qualification who, having passed the entrance examination for specialised medical training, have completed at least two years of a training course leading to an official degree in a health-sciences specialisation.
  • Holders of a degree issued under a foreign education system. In these cases, homologation is not required, but the UPC must verify that the degree certifies a level of training equivalent to an official Spanish master's degree and qualifies the holder for admission to doctoral studies in the country where it was issued. Admission on this basis does not imply homologation of the foreign degree or its recognition for any purpose other than admission to doctoral studies.
  • Holders of a Spanish doctoral qualification issued under previous university regulations.
  • Note 1: Doctoral studies entrance regulations for holders of an undergraduate degree awarded before the introduction of the EHEA (CG 47/02 2014)

    Note 2: Governing Council Decision 64/2014, which approves the procedure and criteria for assessing the fulfilment of academic admission requirements for doctoral studies by holders of non-homologated foreign degrees (CG 25/03 2014)

Admission criteria and merits assessment

Academic admission criteria are as follows:

• Regarding undergraduate and Master’s degrees (15%): Points will be assigned in proportion to the percentage of computer-science credits in applicants’ transcripts.

• Academic performance (35%): Calculated as the weighted average of applicants’ marks in the various subjects studied in undergraduate and master’s degree programmes.

• Research experience (10%): Publications in journals or at conferences (national or international) will be given special consideration, with a maximum of 10 points being awarded for each one.

• Professional experience (10%): Up to 10 points will be awarded per year of work in ICT or BI (fellowships will be given half points).

• Knowledge of languages (10%): Points will be awarded in proportion to applicants’ scores on related language certificates, considering their possible maximums and minimums.

• Letters of recommendation (10%): Points will be awarded according to the corresponding assessment table.

• CV, research proposal and motivation letter (10%): Points will be awarded according to the corresponding assessment table.

An interview will be carried out, as well; it will assess applicants’ personal characteristics.

Enrolment period for new doctoral students

Students enrolling in the doctoral programme for the first time must do so by the deadline specified in the admission decision.
Unless otherwise expressly indicated, enrolments corresponding to admission decisions issued from the second half of April on must be completed within the ordinary enrolment period for the current academic year.

More information at the registration section for new doctoral students

Enrolment period

Ordinary period for second and successive enrolments: first half of October.

More information at the general registration section

Monitoring and evaluation of the doctoral student

Procedure for the preparation and defense of the research plan

Doctoral candidates must submit a research plan, which will be included in their doctoral student activity report, before the end of the first year. The plan may be improved over the course of the doctoral degree. It must be endorsed by the tutor and the supervisor, and it must include the method that is to be followed and the aims of the research.

At least one of these annual assessments will include a public presentation and defence of the research plan and work done before a committee composed of three doctoral degree holders, which will be conducted in the manner determined by each academic committee. The examination committee awards a Pass or Fail mark. A Pass mark is a prerequisite for continuing on the doctoral programme. Doctoral candidates awarded a Fail mark must submit a new research plan for assessment by the academic committee of the doctoral programme within six months.

The committee assesses the research plan every year, in addition to all of the other activities in the doctoral student activity report. Doctoral candidates who are awarded two consecutive Fail marks for the research plan will be obliged to definitely withdraw from the programme.

If they change the subject of their thesis, they must submit a new research plan.

Formation activities

Activity: Assessment based on doctoral student activity report (DAD) and research plan.
Hours: 4.
Type: Confirmation of candidates’ academic progress and approval of the objectives and methodologies used the research work.
Annual candidate assessment report by the Academic Committee.
Public defence assessed by a panel of three doctors (one from the doctoral programme and two external).

Procedure for assignment of tutor and thesis director

The academic committee of the doctoral programme assigns a thesis supervisor to each doctoral candidate when they are admitted or enrol for the first time, taking account of the thesis supervision commitment referred to in the admission decision.

The thesis supervisor will ensure that training activities carried out by the doctoral candidate are coherent and suitable, and that the topic of the candidate’s doctoral thesis will have an impact and make a novel contribution to knowledge in the relevant field. The thesis supervisor will also guide the doctoral candidate in planning the thesis and, if necessary, tailoring it to any other projects or activities undertaken. The thesis supervisor will generally be a UPC professor or researcher who holds a doctoral degree and has documented research experience. This includes PhD-holding staff at associated schools (as determined by the Governing Council) and UPC-affiliated research institutes (in accordance with corresponding collaboration and affiliation agreements). When thesis supervisors are UPC staff members, they also act as the doctoral candidate’s tutor.

PhD holders who do not meet these criteria (as a result of their contractual relationship or the nature of the institution to which they are attached) must be approved by the UPC Doctoral School's Standing Committee in order to participate in a doctoral programme as researchers with documented research experience.

The academic committee of the doctoral programme may approve the appointment of a PhD-holding expert who is not a UPC staff member as a candidate’s thesis supervisor. In such cases, the prior authorisation of the UPC Doctoral School's Standing Committee is required. A UPC staff member who holds a doctoral degree and has documented research experience must also be proposed to act as a co-supervisor, or as the doctoral candidate’s tutor if one has not been assigned.

A thesis supervisor may step down from this role if there are justified reasons (recognised as valid by the committee) for doing so. If this occurs, the academic committee of the doctoral programme will assign the doctoral candidate a new thesis supervisor.

Provided there are justified reasons for doing so, and after hearing any relevant input from the doctoral candidate, the academic committee of the doctoral programme may assign a new thesis supervisor at any time during the period of doctoral study.

If there are academic reasons for doing so (an interdisciplinary topic, joint or international programmes, etc.) and the academic committee of the programme gives its approval, an additional thesis supervisor may be assigned. Supervisors and co-supervisors have the same responsibilities and academic recognition.

The maximum number of supervisors of a doctoral thesis is two: a supervisor and a co-supervisor.

For theses carried out under a cotutelle agreement or as part of an Industrial Doctorate, if necessary and if the agreement foresees it this maximum number of supervisors may not apply. This notwithstanding, the maximum number of supervisors belonging to the UPC is two.

More information at the PhD theses section

Permanence

The academic committee of the programme may authorise an extension of up to one year for full-time doctoral candidates who have not applied to deposit their thesis by the end of the three-year period of study, in the terms outlined in the Academic Regulations for Doctoral Studies of the Universitat Politècnica de Catalunya. In the case of part-time candidates, an extension of two years may be authorised. In both cases, in exceptional circumstances a further one-year extension may be granted by the Doctoral School's Standing Committee, upon the submission of a reasoned application by the academic committee of the doctoral programme.

A doctoral candidate may be dismissed from a doctoral programme for the following reasons:

  • The doctoral candidate submitting a justified application to withdraw from the programme.
  • The maximum period of study and of extensions thereof ending.
  • The doctoral candidate not having enrolled every academic year (unless he or she has been authorised to temporarily withdraw).
  • The doctoral candidate failing two consecutive assessments.
  • The doctoral candidate having disciplinary proceedings filed against him or her that rule that he or she must be dismissed from the UPC.

Dismissal from the programme implies that doctoral candidates cannot continue studying at the UPC and the closing of their academic record. This notwithstanding, they may apply to the academic committee of the programme for readmission and the committee must reevaluate them in accordance with the criteria established in the regulations.

International Mention

The doctoral degree certificate may include International Doctorate mention. In this case, the doctoral candidate must meet the following requirements:

a) During the period of study leading to the award of the doctoral degree, the doctoral candidate must have spent at least three months at a respected higher education institution or research centre outside Spain to complete courses or do research work. The stays and activities carried out must be endorsed by the thesis supervisor and authorised by the academic committee of the programme. The candidate must provide a certifying document issued by the person responsible for the research group of the body or bodies where the stay or activity was completed. This information will be added to the doctoral student’s activity report.
b) Part of the thesis (at least the summary and conclusions) must be written and presented in one of the languages commonly used for science communication in the relevant field of knowledge, which must not be an official language of Spain. This rule does not apply to stays and reports in Spanish or to experts from Spanish-speaking countries.
c) At least two PhD-holding experts belonging to a higher education institution or research centre outside Spain must have issued officially certified reports on the thesis.
d) The thesis examination committee must have included at least one PhD-holding expert from a higher education or research institution outside Spain who was not responsible for the candidate’s stay abroad (point a) above).
e) The thesis defence must have taken place on UPC premises or, in the case of joint programmes, at the location specified in the collaboration agreement.

Learning resources

Doctoral Theses

List of authorized thesis for defense

No hi ha registres per mostrar.

Last update: 16/10/2021 04:47:07.

List of lodged theses

No hi ha registres per mostrar.

Last update: 16/10/2021 04:30:36.

List of defended theses by year

  • ALSERAFI, AYMAN MOUNIR MOHAMED: Dataset proximity mining for supporting schema matching and data lake governance
    Author: ALSERAFI, AYMAN MOUNIR MOHAMED
    Thesis link: http://hdl.handle.net/10803/671540
    Programme: ERASMUS MUNDUS DOCTORAL DEGREE IN INFORMATION TECHNOLOGIES FOR BUSINESS INTELLIGENCE
    Department: Department of Service and Information System Engineering (ESSI)
    Mode: Change of supervisor
    Reading date: 05/02/2021
    Thesis director: ABELLO GAMAZO, ALBERTO | CALDERS, TOON | ROMERO MORAL, OSCAR

    Committee:
         PRESIDENT: LEHNER, WOLFGANG
         SECRETARI: ALDANA MONTES, JOSÉ FRANCISCO
         VOCAL NO PRESENCIAL: MARCEL, PATRICK
    Thesis abstract: With the huge growth in the amount of data generated by information systems, it is common practice today to store datasets in their raw formats (i.e., without any data preprocessing or transformations) in large-scale data repositories called Data Lakes (DLs). Such repositories store datasets from heterogeneous subject-areas (covering many business topics) and with many different schemata. Therefore, it is a challenge for data scientists using the DL for data analysis to find relevant datasets for their analysis tasks without any support or data governance. The goal is to be able to extract metadata and information about datasets stored in the DL to support the data scientist in finding relevant sources. This shapes the main goal of this thesis, where we explore different techniques of data profiling, holistic schema matching and analysis recommendation to support the data scientist. We propose a novel framework based on supervised machine learning to automatically extract metadata describing datasets, including computation of their similarities and data overlaps using holistic schema matching techniques. We use the extracted relationships between datasets in automatically categorizing them to support the data scientist in finding relevant datasets with intersection between their data. This is done via a novel metadata-driven technique called proximity mining which consumes the extracted metadata via automated data mining algorithms in order to detect related datasets and to propose relevant categories for them. We focus on flat (tabular) datasets organised as rows of data instances and columns of attributes describing the instances. Our proposed framework uses the following four main techniques: (1) Instance-based schema matching for detecting relevant data items between heterogeneous datasets, (2) Dataset level metadata extraction and proximity mining for detecting related datasets, (3) Attribute level metadata extraction and proximity mining for detecting related datasets, and finally, (4) Automatic dataset categorization via supervised k-Nearest-Neighbour (kNN) techniques. We implement our proposed algorithms via a prototype that shows the feasibility of this framework. We apply the prototype in an experiment on a real-world DL scenario to prove the feasibility, effectiveness and efficiency of our approach, whereby we were able to achieve high recall rates and efficiency gains while improving the computational space and time consumption by two orders of magnitude via our proposed early-pruning and pre-filtering techniques in comparison to classical instance-based schema matching techniques. This proves the effectiveness of our proposed automatic methods in the early-pruning and pre-filtering tasks for holistic schema matching and the automatic dataset categorisation, while also demonstrating improvements over human-based data analysis for the same tasks.

Last update: 16/10/2021 05:11:51.

Theses related publications

AUTHOR:ALSERAFI, AYMAN MOUNIR MOHAMED
Title:Dataset proximity mining for supporting schema matching and data lake governance
Reading date:05/02/2021
Director:ABELLO GAMAZO, ALBERTO
Co-director:CALDERS, TOON
Co-director:ROMERO MORAL, OSCAR
Mention:Mention de Doctor Internacional
RELATED PUBLICATIONS
Al-serafi, A.; Abello, A.; Romero, O.; Calders, Toon (2020). Keeping the data lake in form: proximity mining for pre-filtering schema matching. - ACM transactions on information systems, ISSN: 1046-8188 (JCR Impact Factor-2019: 2.889; Quartil: Q2)

Al-serafi, A.; Abello, A.; Romero, O.; Calders, T. (2016). Towards information profiling : data lake content metadata management.

Al-serafi, A.; Calders, T.; Abello, A.; Romero, O. (2017). DS-Prox : dataset proximity mining for governing the data lake.

Al-serafi, A.; Abello, A.; Romero, O.; Calders, Toon (2019). Keeping the data lake in form: DS-kNN datasets categorization using proximity mining.

AUTHOR:GHRAB, AMINE
Title:Graph data warehousing
Reading date:29/10/2020
Director:ROMERO MORAL, OSCAR
Co-director:ZIMANYI BORRAGEIROS, ESTEBAN
Mention:Mention de Doctor Internacional
RELATED PUBLICATIONS
Ghrab, A. (2015). A graph data warehouse model and its distribute implementation.

Ghrab, A.; Romero, O.; Skhiri, S.; Zimányi, E. (2020). TopoGraph: an end-to-end framework to build and analyze graph cubes. - Information systems frontiers, ISSN: 1387-3326 (JCR Impact Factor-2019: 3.63; Quartil: Q1)

Ghrab, A.; Romero, O.; Skhiri, S.; Vaisman, A.; Zimányi, E. (2015). A framework for building OLAP cubes on graphs.

Ghrab, A.; Romero, O.; Jouili, S.; Skhiri, S. (2018). Graph BI & analytics: current state and future challenges.

AUTHOR:KOCI, ELVIS
Title:Layout Inference and Table Detection in Spreadsheet Document
Reading date:04/06/2020
Director:ROMERO MORAL, OSCAR
Co-director:LEHNER, WOLFGANG
Mention:Mention de Doctor Internacional
RELATED PUBLICATIONS
Koci, E. (2016). From semi-structured documents to relations.

Koci, E.; Thiele, M.; Romero, O.; Lehner, W. (2016). A machine learning approach for layout inference in spreadsheets.

Koci, E.; Thiele, M.; Romero, O.; Lehner, W. (2017). Table identification and reconstruction in spreadsheets.

Koci, E.; Thiele, M.; Lehner, W.; Romero, O. (2018). Table recognition in spreadsheets via a graph representation.

Koci, E.; Kuban, D.; Luetting, N.; Olwig, D.; Thiele, M.; Gonsior, J.; Lehner, W.; Romero, O. (2019). XLIndy: interactive recognition and information extraction in spreadsheets.

Koci, E.; Thiele, M.; Romero, O.; Lehner, W. (2019). A genetic-based search for adaptive table recognition in spreadsheets.

Koci, E.; Thiele, M.; Rehak, J.; Romero, O.; Lehner, W. (2019). Deco: A dataset of annotated spreadsheets for layout and table recognition.

Koci, E. (2019). ICDAR - IAPR International Conference on Document Analysis and Recognition.

AUTHOR:MUNIR, RANA FAISAL
Title:Storage Format Selection and Optimization for Materialized Intermediate Results in Data-Intensive Flows
Reading date:05/12/2019
Director:ABELLO GAMAZO, ALBERTO
Co-director:LEHNER, WOLFGANG
Co-director:ROMERO MORAL, OSCAR
Mention:Mention de Doctor Internacional
RELATED PUBLICATIONS
Munir, R. (2016). Self-tuning bi Systems.

Munir, R.; Nadal, S.; Romero, O.; Abello, A.; Jovanovic, P.; Thiele, M.; Lehner, W. (2018). Intermediate results materialization selection and format for data-intensive flows. - Fundamenta informaticae, ISSN: 0169-2968 (JCR Impact Factor-2018: 1.204; Quartil: Q2)

Munir, R.; Abello, A.; Romero, O.; Thiele, M.; Lehner, W. (2019). A cost-based storage format selector for materialized results in big data frameworks. - Distributed and parallel databases, ISSN: 0926-8782 (JCR Impact Factor-2018: 1.147; Quartil: Q3)

Munir, R.; Abello, A.; Romero, O.; Thiele, M.; Lehner, W. (2020). Configuring parallelism for hybrid layouts using multi-objective optimization. - Big data, ISSN: 2167-6461 (JCR Impact Factor-2019: 3.644; Quartil: Q1)

Munir, R.; Romero, O.; Abello, A.; Bilalli, B.; Thiele, M.; Lehner, W. (2016). Resilient store: a heuristic-based data format selector for intermediate results.

Bilalli, B.; Abello, A.; Aluja, T.; Munir, R.; Wrembel, R. (2018). PRESISTANT : data pre-processing assistant.

Munir, R.; Abello, A.; Romero, O.; Thiele, M.; Lehner, W. (2018). ATUN-HL: auto tuning of hybrid layouts using workload and data characteristics.

Munir, R.; Abello, A.; Romero, O.; Thiele, M.; Lehner, W. (2019). Automatically configuring parallelism for hybrid layouts.

AUTHOR:NADAL FRANCESCH, SERGI
Title:Metadata-Driven Data Integration
Reading date:16/05/2019
Director:ABELLO GAMAZO, ALBERTO
Co-director:ROMERO MORAL, OSCAR
Co-director:VANSUMMEREN, STIJN
Mention:Mention de Doctor Internacional
RELATED PUBLICATIONS
Nadal, S.; Abello, A. (2018). Integration-oriented ontology.

Nadal, S. (2016). Self-Optimizing Data Stream Processing.

Al Bassit, Anas (2017). ECA rules in a bigdata context.

Busquet I seguí, Francesc (2018). Defining corporate credit ratings using Machine Learning techniques for Spanish Companies.

Wiejacha, Malgorzata (2018). Analysis of kernel matrices and their relation with SVM performance.

Parafita Martínez, Álvaro (2018). On explainability of Deep Neural Networks.

Yamaui, A. (2018). Embedding for Natural Language Processing.

Schmoor, Xavier (2018). Build d Big Data demonstrator.

Mas, Charline (2018). Virtual assistant with Natural Language Processing Capabilities.

Lorente, E. (2019). Optimizing vehicle profile speed settings based on historic data.

Llana, S. (2019). COM architecture for a Content Delivery Network infrastructure use case.

Ferrer-Cid, P. (2019). Calibration of low-cost air pollutant sensors using machine learning techniques.

Nadal, S.; Herrero, V.; Romero, O.; Abello, A.; Franch, X.; Vansummeren, S.; Valerio, D. (2017). A software reference architecture for semantic-aware big data systems. - Information and software technology, ISSN: 0950-5849 (JCR Impact Factor-2017: 2.627; Quartil: Q1)

Nadal, S.; Romero, O.; Abello, A.; Vassiliadis , P.; Vansummeren, S. (2019). An integration-oriented ontology to govern evolution in big data ecosystems. - Information systems, ISSN: 0306-4379 (JCR Impact Factor-2019: 2.466; Quartil: Q3)

Jovanovic, P.; Nadal, S.; Romero, O.; Abello, A.; Bilalli, B. (2020). Quarry: A user-centered big data integration platform. - Information systems frontiers, ISSN: 1387-3326 (JCR Impact Factor-2019: 3.63; Quartil: Q1)

Nadal, S.; Abello, A.; Romero, O.; Varga, J. (2017). Big data management challenges in SUPERSEDE.

Nadal, S.; Abello, A.; Romero, O.; Vansummeren, S.; Vassiliadis , P. (2018). MDM: governing evolution in big data ecosystems.

Franch, X.; Ralyté, J.; Perini, A.; Abello, A.; Ameller, D.; Gorroñogoitia, J.; Nadal, S.; Oriol, M.; Seyff, N.; Siena, A.; Susi, A. (2018). A situational approach for the definition and tailoring of a data-driven software evolution method.

Oriol, M.; Melanie Stade; Fotrousi, F.; Nadal, S.; Varga, J.; Seyff, N.; Abello, A.; Franch, X.; Marco, J.; Schmidt, O. (2018). FAME: supporting continuous requirements elicitation by combining user feedback and monitoring.

Nadal, S.; Rabbani, K.; Romero, O.; Nigatu, S. (2019). ODIN: A dataspace management system.

AUTHOR:BILALLI, BESIM
Title:Learning the impact of data pre-processing in data analysis
Reading date:28/06/2018
Director:ABELLO GAMAZO, ALBERTO
Director:ALUJA BANET, TOMAS
Director:WREMBEL, ROBERT
Mention:Mention de Doctor Internacional
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Thavornun, Varunya (2015). Metadata Management for Knowledge Discovery.

Batista Leite, Larissa (2017). Data Mining Experiments at Scale.

Gaset Carretero, Clara (2018). Implementació de BPM per a la distribució mundial d'Azitromicina per l'erradicació de Pian..

Bilalli, B.; Abello, A.; Aluja, T. (2017). On the predictive power of meta-features in OpenML. - International journal of applied mathematics and computer science, ISSN: 1641-876X (JCR Impact Factor-2017: 1.694; Quartil: Q1)

Bilalli, B.; Abello, A.; Aluja, T.; Wrembel, R. (2018). Intelligent assistance for data pre-processing. - Computer standards & interfaces, ISSN: 0920-5489 (JCR Impact Factor-2018: 2.441; Quartil: Q2)

Bilalli, B.; Abello, A.; Aluja, T.; Wrembel, R. (2016). Automated data pre-processing via meta-learning.

Bilalli, B.; Abello, A.; Aluja, T.; Wrembel, R. (2016). Towards intelligent data analysis : the metadata challenge.

AUTHOR:THEODOROU, VASILEIOS
Title:Automating User-Centered Design of Data-Intensive Processes
Reading date:27/01/2017
Director:ABELLO GAMAZO, ALBERTO
Director:LEHNER, WOLFGANG
Mention:Mention de Doctor Internacional
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Huber, S.; Seiger, R.; Kuhnert, A.; Theodorou, V.; Schlegel, T. (2016). Goal-based semantic queries for dynamic processes in the internet of things. - International Journal Of Semantic Computing, ISSN: 1793-7108

Theodorou, V.; Abello, A.; Lehner, W.; Thiele, M. (2016). Quality measures for ETL processes: from goals to implementation. - Concurrency and computation: practice and experience, ISSN: 1532-0626 (JCR Impact Factor-2016: 1.133; Quartil: Q3)

Theodorou, V.; Jovanovic, P.; Abello, A.; Nakuçi, E. (2017). Data generator for evaluating ETL process quality. - Information systems, ISSN: 0306-4379 (JCR Impact Factor-2017: 2.551; Quartil: Q2)

Theodorou, V.; Abello, A.; Thiele, M.; Lehner, W. (2017). Frequent patterns in ETL workflows: An empirical approach. - Data and knowledge engineering, ISSN: 0169-023X (JCR Impact Factor-2017: 1.467; Quartil: Q3)

Theodorou, V.; Abello, A.; Lehner, W. (2014). Quality measures for ETL processes.

Nakuçi, E.; Theodorou, V.; Jovanovic, P.; Abello, A. (2014). Bijoux : data generator for evaluating ETL process quality.

Theodorou, V.; Abello, A.; Thiele, M.; Lehner, W. (2014). A framework for user-centered declarative ETL.

Theodorou, V.; Abello, A.; Thiele, M.; Lehner, W. (2015). POIESIS: A tool for quality-aware ETL process redesign.

AUTHOR:VARGA, JOVAN
Title:Semantic Metadata for Supporting Exploratory OLAP
Reading date:24/01/2017
Director:BACH PEDERSEN, TORBEN
Director:ROMERO MORAL, OSCAR
Mention:Mention de Doctor Internacional
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Rodríguez Salazar, Eric Gerardo (2016). Enriching Data Integration Process by Involving end users in Social or Collaborative Ontology Matching.

Dobrokhotova, Ekaterina (2016). A Metadata-based Recommender System for Linked-Open Data.

Varga, J.; Vaisman, A.; Romero, O.; Etcheverry, L.; Bach, T.; Thomsen, C. (2016). Dimensional enrichment of statistical linked open data. - Journal of web semantics, ISSN: 1570-8268 (JCR Impact Factor-2016: 1.075; Quartil: Q3)

Varga, J.; Romero, O.; Bach, T.; Thomsen, C. (2014). Towards next generation BI systems : the analytical metadata challenge.

Varga, J.; Romero, O.; Bach, T.; Thomsen, C. (2014). SM4AM : a semantic metamodel for analytical metadata.

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Varga, J.; Dobrokhotova, E.; Romero, O.; Bach, T.; Thomsen, C. (2017). SM4MQ: a semantic model for multidimensional queries.

AUTHOR:JOVANOVIC, PETAR
Title:REQUIREMENT-DRIVEN DESIGN AND OPTIMIZATION OF DATA-INTENSIVE FLOWS
Reading date:26/09/2016
Director:ABELLO GAMAZO, ALBERTO
Co-director:CALDERS, TOON
Co-director:ROMERO MORAL, OSCAR
Mention:Mention de Doctor Internacional
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Jovanovic, P.; Romero, O.; Simitsis, A.; Abello, A.; Mayorova, D. (2014). A requirement-driven approach to the design and evolution of data warehouses. - Information systems, ISSN: 0306-4379 (JCR Impact Factor-2014: 1.456; Quartil: Q2)

Jovanovic, P.; Romero, O.; Simitsis, A.; Abello, A. (2016). Incremental consolidation of data-intensive multi-flows. - IEEE transactions on knowledge and data engineering, ISSN: 1041-4347 (JCR Impact Factor-2016: 3.438; Quartil: Q1)

Jovanovic, P.; Romero, O.; Abello, A. (2016). A unified view of data-intensive flows in business intelligence systems : a survey. - Transactions on Large-Scale Data- and Knowledge-Centered Systems, ISSN: 1869-1994

Jovanovic, P.; Romero, O.; Simitsis, A.; Abello, A.; Candón, H.; Nadal, S. (2015). Quarry: digging up the gems of your data treasury.

Touma, R.; Romero, O.; Jovanovic, P. (2015). Supporting data integration tasks with semi-automatic ontology construction.

Jovanovic, P.; Romero, O.; Calders, T.; Abello, A. (2016). H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution.

Glushkova, D.; Jovanovic, P.; Abello, A. (2017). MapReduce performance models for Hadoop 2.x.

Research projects

START DATEEND DATEACTIVITYFINANCING ENTITY
01/10/201913/12/2019Design, development and validation of the Forecasting Medical Supplies and Requests components of the WIMEDSOrganització Mundial de la Salut
26/06/201926/07/2019Desenvolupament prova de concepte d'arquitectura BIG DATAFUNDACIÓ i2CAT
05/06/201931/10/2020I+D Desarrollo Data LakeADQUIVER MEDIA, SL
07/01/201931/07/2019Analytical module and validation of the WISCC-WISCENTDOrganització Mundial de la Salut
17/07/201801/07/2020Col·laboració per promoure la innovació en els serveis TIC dels diferents àmbits d'acció pública i privada.SBS SEIDOR, S.L.
10/04/201815/12/2018Implementation of a computerized World information system for Chagas disease control (WISCC), phase 3Organització Mundial de la Salut
01/01/201831/12/2021Razonamiento automático, ejecución de modelos y análisis de datos a partir de ontologíasAGENCIA ESTATAL DE INVESTIGACION
30/12/201629/12/2020Generación y Evolución de Smart APlsMIN DE ECONOMIA Y COMPETITIVIDAD
01/11/201606/02/2020Quality-Aware Rapid Software DevelopmentCommission of European Communities
15/10/201615/09/2017Implementation of a computerized World information system for Chagas disease control (WISCC), phase 2Organització Mundial de la Salut
01/07/201530/06/2016Implementation of a computerized World information system for Chagas disease control (WISCC)Organització Mundial de la Salut
01/05/201530/04/2018SUpporting evolution and adaptation of PERsonalized Software by Exploiting contextual Data and End-user feedbackCommission of European Communities
01/09/201230/06/2015Erasmus Mundus Joint Doctorate Programme in Information Technologies for Business Intelligence - Doctoral CollegeUniversitat Politècnica de Catalunya

Teaching staff and research groups

Research projects

START DATEEND DATEACTIVITYFINANCING ENTITY
01/10/201913/12/2019Design, development and validation of the Forecasting Medical Supplies and Requests components of the WIMEDSOrganització Mundial de la Salut
26/06/201926/07/2019Desenvolupament prova de concepte d'arquitectura BIG DATAFUNDACIÓ i2CAT
05/06/201931/10/2020I+D Desarrollo Data LakeADQUIVER MEDIA, SL
07/01/201931/07/2019Analytical module and validation of the WISCC-WISCENTDOrganització Mundial de la Salut
17/07/201801/07/2020Col·laboració per promoure la innovació en els serveis TIC dels diferents àmbits d'acció pública i privada.SBS SEIDOR, S.L.
10/04/201815/12/2018Implementation of a computerized World information system for Chagas disease control (WISCC), phase 3Organització Mundial de la Salut
01/01/201831/12/2021Razonamiento automático, ejecución de modelos y análisis de datos a partir de ontologíasAGENCIA ESTATAL DE INVESTIGACION
30/12/201629/12/2020Generación y Evolución de Smart APlsMIN DE ECONOMIA Y COMPETITIVIDAD
01/11/201606/02/2020Quality-Aware Rapid Software DevelopmentCommission of European Communities
15/10/201615/09/2017Implementation of a computerized World information system for Chagas disease control (WISCC), phase 2Organització Mundial de la Salut
01/07/201530/06/2016Implementation of a computerized World information system for Chagas disease control (WISCC)Organització Mundial de la Salut
01/05/201530/04/2018SUpporting evolution and adaptation of PERsonalized Software by Exploiting contextual Data and End-user feedbackCommission of European Communities
01/09/201230/06/2015Erasmus Mundus Joint Doctorate Programme in Information Technologies for Business Intelligence - Doctoral CollegeUniversitat Politècnica de Catalunya

Quality

The Validation, Monitoring, Modification and Accreditation Framework (VSMA Framework) for official degrees ties the quality assurance processes (validation, monitoring, modification and accreditation) carried out over the lifetime of a course to two objectives—the goal of establishing coherent links between these processes, and that of achieving greater efficiency in their management—all with the overarching aim of improving programmes.

Validation

        Registry of Universities, Centers and Degrees (RUCT)

        Indicators

        Up