Bachelor's and Master's Projects and Theses at the Chair for Algorithms and Data Structures. Note: This is a new page, created on 07-02-2017. The old page, which was outdated and in a terrible format, can still be found here.
Application for a project or thesis
If you are interested in doing a project or theses with us, please carefully read this page first and then send an e-mail to the prospective supervisor with the following information:
- Acknowledge that you have carefully read this whole page (not just the first section).
- Which courses have you already taken with us.
- A very short description of your interests and your strengths (concerning work on a project/thesis).
- A transcript of the grades of the courses you have take so far.
- If you have an own project in mind (not necessary): a very short description of the goal and the scientific merit.
If you want to do your thesis with a company, please provide the following information in a concise format (one understandable and informative paragraph for each of the items below):
- What is the expected / aimed at outcome?
How does your approach and the expected result differ from the state of the art? See the section on "Related Work" in our guidelines.
How do you plan to evaluate your work. See the sections on "Theoretical Analysis" and "Empirical Analysis" in our guidelines.
- Supervision must be provided by the company and the supervisor should provide an evaluation report at the end of the thesis, in a format to be discussed with us.
Deliverables of a project or thesis
B.Sc. and M.Sc. projects: (1) a project website; (2) well-documented version of your code and data, which allow reproducibility of your results, in our SVN. See the list at the end of this page for many examples (of project websites).
B.Sc. and M.Sc. thesis: (1) written thesis; (2) oral presentation (20 minutes + question); (3) well-documented version of your code and data, which allow reproducibility of your results, in our SVN. Here are some guidelines on how to write a good thesis. See the list at the end of this page for many examples (of submitted theses and the accompanying presentation).
For both thesis and projects, there should be a README at the top level of your codebase. That README should contain information on:
- An overview of the files (so that one knows what is what and where to find what)
- Instructions on how to install and run your code
- Instructions on how to reproduce your experiments
- List of data files which are not part of the code base (because they are too big), and where to obtain them from
Available and ongoing projects and theses
= Available; = Ongoing
Click on the titles for a more detailed description of the project, including background info, goals, and first steps. Note that for some topics, it can make sense if several people work on them concurrently. So ongoing does not necessarily mean, that the topic is no longer available.
Generating Regular-Interval Maps from Public Transit Data (project or thesis, ongoing): We are looking for well-structured input data for our transit-map drawing algorithm. The goal of this topic is to provide the transit-mapper with a graph from which it can render regular-intervall timetable maps ("Taktfahrplankarten") like this. You should be a fan of huge datasets, timetable data and of course graphs (but who isn't). As a bachelor or master project with the possibility of continuation as a thesis. Supervised by Patrick Brosi.
Merging Overlapping GTFS Feeds (Bachelor project or thesis, ongoing): Many transportation companies publish their timetable data either directly as GTFS feeds or in formats that can be converted to GTFS. As soon as you have two GTFS feeds (two sets of timetable data) that cover either the same or adjacent areas, the problem of duplicate trips arises. You should develop a tool that merges two or more GTFS feeds and solves duplication / fragmentation issues. As a bachelor project or thesis. Supervised by Patrick Brosi.
GTFS Browser Web App (Bachelor project or thesis): Develop a web-application that can be used to analyze huge GTFS datasets. There are already some tools available (for example, ScheduleViewer) but they all feel and look quite clumsy, are incredible slow and cannot handle large datasets. Supervised by Patrick Brosi.
Merging Hyphenated Words & Guessing Ligatures (Master thesis, ongoing): PDF is a layout-based format: it specifies the positions and fonts of the individual characters, of which the text is composed, but usually does not provide any information about words, paragraphs and sections. You should write code, that (1) merges hyphenated words with respect to compound words and (2) "guesses" characters (e.g., ligatures) that can't be identified as textual content. Supervised by Claudius Korzen.
Extract and Analyze Scientist's Homepages (project and/or thesis): Extract a large number of scientist's homepages from the CommonCrawl web crawl. Extract the central information from these pages, including: name, profession, gender, affiliation. It will be relativel straightforward to get results of medium quality. The challenge is to achieve results of high quality. Machine learning will be crucial to achieve that. Exploring suitable methods is part of the challenge. Supervised by Hannah Bast.
Tokenization Repair (project and/or thesis): Interesting and well-defined problem, the solution of which is relevant in a variety of information retrieval scenarios. Simple rule-based solutions come to mind easily, but machine learning is key to get very good results. A background in machine learning, or a strong willingness to aquire one as part of the project/thesis, is therefore mandatory for this project. Supervised by Hannah Bast.
A User Interface for the QLever SPARQL+Text engine (project or thesis): This is well-suited as a project (B.Sc. or M.Sc.) but also provides ample opportunity for continuation with a theses (B.Sc. or M.Sc). You should be fond of good user interfaces and have a good taste concerning layout and colors and such things. You should also like knowledge bases and big datasets and searching in them. Supervised by Hannah Bast.
Synonym Finder (project and/or theses): Find synonyms for all entities from Freebase or WikiData or Wikipedia. Evaluate the quality and compare it to existing synonym databases like CrossWikis. Motivation: Most entities are known under several names. For example, a person like "Ellen DeGeneres" is also known as just "Ellen". A profession like "Astronaut" is also known as "Spaceman", "Spacewoman" or "Cosmsonaut". Knowing these synonyms is key for many NLP (Natural Lanuage Processing) problems. Including complex problems like semantic search and question answering. Supervised by Hannah Bast.
A Search Engine for OpenStreetMap Data (project and/or thesis): Implement the backend for a search engine for OpenStreetMap data. Your server should load an arbitrary .osm file and answer (fuzzy) string searches over the entire dataset. This is a nice project to familiarize yourself with different index structures. Continuation as a thesis is possible. Preferably you have visited our lecture Information Retrieval, but this is not required. Code should be written in C++. Supervised by Patrick Brosi.
Tabular Information Extraction (project and/or thesis): Extract data from a knowledge base in a tabular format. This could for example be a list of cities with columns for the country they are in, population count and coordinates but really anything fitting in a table should be possible. Think of the typically hand crafted summary tables on Wikipedia. This is well-suited as a project (B.Sc. or M.Sc.) with possible continuation as a thesis. You should be interested in knowledge bases, big datasets and searching in them. Supervised by Niklas Schnelle.
Named Entity Recognition (project and/or thesis): In this project you will design and implement as system for recognizing named entities such as persons, companies, places etc. in a potentially very large corpus. These entities shall be linked to the respective entities in a knowledge base augmenting the classic NER task. This is well-suited as a project (B.Sc. or M.Sc.) with possible continuation as a thesis. You should be interested in machine learning, knowledge bases and working with large data sets. Supervised by Niklas Schnelle.
Instructions for students and supervisors
In the first meeting with the supervisor, create a Google Doc where you copy and fill out the following template. The Google Doc must be named Firstname Lastname and it should be shared with at least the following people: Student, Supervisor(s), Hannah Bast ( email@example.com ), Frank Dal-Ri ( firstname.lastname@example.org , our system administrator), Heike Hägle ( email@example.com , our secretary).
Type: [one of: Bachelor Project, Bachelor Thesis, Master Project, Master Thesis] Short working title of project/thesis: [this may change later] RZ Account: [initials + number, e.g. ''es437''] Department Account: [usually the first seven letters of the given name + the initial of the first name, e.g. ''scissore''] Primary e-mail adress: SVN: [subdirectory in student-projects or student-theses, named firstname-lastname, e.g. ''edward-scissorhands'']] Special RAM requirements: Special Disk space requirements: Actual beginning of work: Planned end of work: Goal: Subgoals: First steps: Deadline for first steps:
The first steps should be something relative fail-safe which can just "be done". If the first steps are not even remotely finished by the agreed-upon deadline, we might not want to supervise you further. Supervision is a considerable effort for us and we care a lot about it. In particular, we spent a lot of thought and effort on formulating interesting topics (and goals, and subgoals, and first steps) which are interesting and relevant and related to our actual research. We care about the results of these projects/theses, so you should also care working on them.
It usually makes sense to have a few follow-up / progress meetings over the course of the projects. They usually take place, after certain milestones have been reached. Students should come well-prepared to these meetings. Here is a checklist:
- Concise(!!) list of what has been done in the Google Doc (at the top)
- Concise list of questions, if you have any
- Code written so far should be in the SVN, not (only) on your local computer
- All relevant files should be on our file system, not (only) on your local computer
- A working demo of whatever you have done so far
- The demo should run on our servers, not just locally on your computer
- Bring your laptop (just in case there is uncommitted stuff)
Completed projects and theses
List of completed B.Sc. and M.Sc. theses (each with the written thesis and presentation)
List of completed B.Sc. and M.Sc. projects (each with a project website)