Similarity Search in Large Databases
News
- The due date for worksheet 06 has been extended to Tuesday Jan 15th 2025 - 23:59.
About this Course
This course comes in two variants:
- Variant A for Computer Science students: combined lecture and lab (termed UV - Übung mit Vorlesung) with 5 ECTS.
- Variant B for Data Science students: lecture (VO, 2.5 ECTS) + lab (PS, 2.5 ECTS)
Data Science students are encouraged to enroll into Variant A (5 ECTS UV rather than VO+PS) to take advantage of the midterms for the lecture part of the course. The recognition of Variant A as Variant B (as required for Data Science students) is ensured. For students enrolled into Variant B the midterms will be part of the lab grade (PS), and an additional final exam will be required to pass the lecture (VO).
Lecture
Questions and discussions
For questions and discussions (also among students) regarding course specific topics please use the Slack channel #ssdb-uv-ps (Workspace dbteaching.slack.com).
Slack registration: Students register with their university email here: https://dbteaching.slack.com/signup
Schedule
Schedule of the course according to PlusOnline. Deviations will be communicated explicitly in the Slack channel #ssdb-uv-ps and/or the course website.Slides
Each set of slides treats a specific topic area and will be discussed in one or more lecture units. Slides that have not yet been discussed during the lecture may be subject to change. Once a slide set has been discussed in class, only bug fixes will be applied. Slide sets have a version (date) on the title page.
The slides and their discussion during the lecture are essential for the exam perparation.
Note: The slide version of last year is already online to give you an overview, but this version may be subject to change.
| Topics | Slides | ||
|---|---|---|---|
| 0. | Course Organisation and Demo | [1up] [4up] | |
| 1. | Introduction to Similarity Search | [1up] [4up] | |
| 2. | Edit Distance: Definition, Brute Force Algorithm, Dynamic Programming Algorithm, Edit Distance Variants | [1up] [4up] | |
| 3. | q-Gram Distance: Approximate String Join, Lower Bound Filtering, Length Filter, q-Gram Count Filter, q-Gram Position Filter, q-Gram Distance, Experiments | [1up] [4up] | |
| 4. | Trees: Tree Definition | [1up] [4up] | |
| 5. | Tree Edit Distance: Definition, Edit Cost, Edit Mapping, Deriving the Recursive Formulas, Dynamic Programming Algorithm, Complexity | [1up] [4up] | |
| 6. | Pruning: Traversal String Lower Bound, Constrained Edit Distance Upper Bound | [1up] [4up] | |
| 7. | Token-Based Tree Distances: Tree Tokens, Binary Branches, pq-Grams | [1up] [4up] | |
| 8. | Set Similarity Join: Signatures for Overlap and Hamming Distance, Implementation | [1up] [4up] | |
Literature
The following book treats selected lecture topics:
N. Augsten, M. H. Böhlen. Similarity joins in relational database systems.
Synthesis lectures on data management. Morgan Claypool Publishers, 2013.
The book is available online from our university library.
Grading
The grading of the course is based on:
- Two midterms: You will write two midterm exams.
- Homework: You will solve exercises at home and present your solution in class. The grading depends on the number of homework exercises that you solve and the quality of your presentions in class.
Midterm Exams
The midterm exams take place during the lecture hours and are planned for:
- Wed Dec 4th, 14:15
- Wed Jan 29th, 14:15
The midterm exam lasts for 60 minutes and you can get a total of 70 points for the two midterms.
Cheat sheet:You may use one A4 sheet with your personal notes (single-sided, handwritten or printed).
Previous exams: 30.01.2023, 17.03.2023, 31.01.2024, 28.02.2024
Please note that these exams were a part of the lecture (VO) variant of this course and differ in their format from the midterm exams.
Homework
You will solve worksheets at home during the semester. The worksheets must be solved by the due date indicated on the worksheet.
By the due date of the worksheet, you also tick the exercises that you solved in Blackboard. Ticking an exercises means that
- you have solved the exercise,
- you understand the solution,
- you will be able to present the solution,
- you will be able to answer questions regarding the solution.
A score (number of points) is assigned to each homework exercise. The score depends on the complexity and difficulty of the exercise. For a single worksheet, at most 10 points can be achieved.
The homework worksheets will be available at least 6 days before the due date.
Worksheet Schedule
| Date | Worksheet Nr. |
|---|---|
| 2024-10-16 | Worksheet 01 |
| 2024-10-30 | Worksheet 02 |
| 2024-11-13 | Worksheet 03 |
| 2024-11-27 | Worksheet 04 |
| 2024-12-17 | Worksheet 05 |
| 2025-01-15 | Worksheet 06 |
| 2025-01-22 | Worksheet 07 |
Presentations in Class
In class, the lecturer will pick a student for each exercise of the homework and ask the student to present the solution. The lecturer (and the fellow students) will ask questions about the solution. The quality of the presentation will be rated A, B, or C. Quality criteria for the presentation are
- the technical correctness of the solution,
- the clearness of the presentation,
- the correct use of terminology, and
- the ability to answer in-depth questions.
Quality ratings of the presentation:
- A: good presentation and in-depth understanding of the solutions
- B: solution not correct or presentation issues, but sufficient understanding of the solution
- C: insufficient understanding of the solution
Scores for the Homework
You get points for each of the homework worksheets. The overall score is the sum over the points for all worksheets.
Each worksheet contributes as follows to the overall score:
- If your presentation was rated A or you were not asked to present any of the solutions, then all ticked excercises of the worksheet count towards the overall score.
- If your presentation was rated B, you will not receive the points for the exercise you presented, but all other ticked excercises of the worksheet count towards the overall score.
- If your presentation was rated C:
- For the first C-rated presentation, you will get zero points for the worksheet (i.e., none of the excercises of the worksheet counts).
- For further C-rated presentations, the points of all ticked exercises of the worksheet will be subtracted from the overall score.
- If your absent from class, you will not get points for the respective worksheet. If you are not in class while a specific exercise is being presented (coming late or leaving early), the points for that exercise will not count.
Scores and Grades
The overall score is the sum of the midterm score and the homework score. The maximum overall score is 140.
You need to achieve a midterm score of at least 35 points and a homework score of at least 35 points to pass the course.
If both the midterm score and the worksheet score are are at least 35 each, the final grade is computed from the overall score as follows:
| Percent | Score | Grade |
|---|---|---|
| 81-100 | 113-140 | 1 |
| 71-80 | 99-112 | 2 |
| 61-70 | 85-98 | 3 |
| 50-60 | 70-84 | 4 |
| 0-49 | 0-69 | 5 |
Attendance and unenrolment: The students must attend at least 75% of the lab lectures to achieve a positive grade. Unenrolements are possible only until before the 3rd lab unit, i.e., all students that are still enrolled at the time of the 3rd lab unit will be graded.