Checking date: 12/07/2021

Course: 2021/2022

Massive and Linked Data
Study: Master in Computer Engineering (228)

Coordinating teacher: GONZALEZ CARRASCO, ISRAEL

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 3.0 ECTS


Skills and learning outcomes
Description of contents: programme
BLOCK 1. MASSIVE DATA INTEGRATION. 1.1. Integration of data sources. 1.2. Big Data for data integration and analysis. 1.3. Main applications. BLOCK 2. BLOCKCHAIN. 2.1. Origin of Blockchain. 2.2. Blockchain operation. 2.3. Consensus algorithm. 2.4. Types of Blockchain. 2.5. Main applications.
Learning activities and methodology
TRAINING ACTIVITIES AF1 - Theoretical class [6.6 hours with 100% attendance, 0.20 ECTS]. AF2 - Practical classes [5 hours with 100% attendance, 0.19 ECTS]. AF4 - Laboratory practices [5 hours with 100% attendance, 0.20 ECTS]. AF5 - Tutorials [5.83 hours with 25% of attendance, 0.19 ECTS]. AF6 - Group work [30.5 hours with 0% attendance, 1.02 ECTS]. AF7 - Individual student work [30.5 hours with 0% attendance, 1.02 ECTS]. AF8 - Partial and final exams [6.66 hours with 100% attendance, 0.20 ECTS]. TEACHING METHODOLOGIES MD1 - Class lectures by the professor with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD2 - Critical reading of texts recommended by the professor of the subject: press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate the knowledge of the subject. MD3 - Resolution of practical cases, problems, etc. .... posed by the teacher individually or in groups. MD4 - Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the subject, as well as of practical cases. case studies. MD5 - Preparation of papers and reports individually or in groups.
Assessment System
  • % end-of-term-examination 10
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Judith R. Davis and Robert Eve. Data Virtualization Going Beyond Traditional Data Integration to Achieve Business Agility. Composite Software. . 2011
  • AnHai Doan, Alon Halevy, and Zachary Ives. Principles of Data Integration. . Morgan Kaufmann.. 2012
  • Bishop, Matt.. Computer security : art and science. Addison-Wesley. 2003
  • Daniel. Drescher. Blockchain basics a non-technical introduction in 25 steps. Berkeley, CA . 2017
  • Ross Anderson . Security engineering : a guide to building dependable distributed systems. Wiley. 2008
  • Trovati, M., Hill, R., Anjum, A., Zhu, S.Y., Liu, L. (Eds.). Big-Data Analytics and Cloud Computing. Springer. 2015
Additional Bibliography
  • Philip Bernstein and Laura Haas,. Information integration in the enterprise,. Communications of the ACM Vol 51, N 9, September 2008, Pages 72-79. 2008

The course syllabus and the academic weekly planning may change due academic events or other reasons.