Subject » BMEVISZMA01
Very Large Databases
Nagyméretű adathalmazok kezelése
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Nagyméretű adathalmazok kezelése
Very Large Databases
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| Subject code | BMEVISZMA01 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | vizsga | ||||||||||||
| Credits | 4 | ||||||||||||
| Subject coordinator |
DR. Katona Gyula
position: egyetemi tanár
contact:
katona.gyula@vik.bme.hu
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| Responsible department |
Számítástudományi és Információelméleti Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | cs.bme.hu/nagyadat | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
1. Machine learning basic tasks first, discriminating and generative models, attribute types,
2. Nearest neighbor search: normalization, distance.
3. Decision trees: wood building models (C4.5, regression trees), the purity levels, cuts,
4. Early- and post-pruning, management of continuous variables.
5. Naive Bayes: Managing continuous variables, m-Estimate.
6. Perceptron: activation function, stochastic gradient.
7. Clustering: mid-point (k-Means, bisecting k-Means)
8. Density-based methods (DBSC, OPTICS), hierarchical clustering (linkage).
9. Recommendation Systems: collaborative filtering (matrix factorization, nearest neighbor methods), content-based recommendation.
10 Searching: index building, ranking (TF-IDF, BM25, PageRank)
11. Support vector machines (SVM): maximal margin, kernel functions
12. Principal Component Analysis (PCA)
13 Artificial Neural Networks (ANN): Unsupervised (Restricted Boltzmann Machines)
14 Artificial Neural Networks (ANN): Supervised (Multilayer Percetpron) case.
Overview of special theoretical and practical problems arising in the course aims for large data sets. Students are given an insight into the topic of modern trends, data mining, relational databases, large graphs, data streams theoretical and practical questions.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
Lectures and computer aided practice problems.
Tanulástámogató anyagok
Online források
Tan-Steinbach-Kumar: Introduction to Data Mining, Pearson Educacion; 2nd Revised edition edition (2013)
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Database theory, graph theory, basic algorithmic techniques
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Database theory, graph theory, basic algorithmic techniques
General rules
Requirements:
Signature:
2 midterms, both must be at >=40%, optional homework, extra points added to midterm results
Final:
The grade is based on the midterm results, can be improved at oral exam.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Not provided.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
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Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.