An inclusion dependency between two attribute sets X1,...,Xn and Y1,...,Yn of two relations R and S is defined in the following: For each tuple of values appearing in columns X1,...,Xn of Relation R, there must be an equivalent tuple in columns Y1,...,Yn in relation S. In short this is denoted as R[X1,...,Xn] ⊆ S[Y1,...,Yn].

Inclusion dependencies are an important topic in the research area of data profiling, which aims to extract new metadata for tables. In particular, inclusion dependencies are a prerequisite for foreign keys and thus are a starting point to detect meaningful relationships between tables. Existing work has suggested algorithms for the discovery of inclusion dependencies in databases [1] and webtables [2].

While the discovery of inclusion dependencies has been researched for static databases or webtables, the discovery of inclusion dependencies in historical data remains an open problem. In particular, it should be discussed how the definition of inclusion dependencies for database snapshots (as explained in the first paragraph) should be generalized to table histories and whether the development of the database over time can be used to reduce the search space.

Tasks in this thesis include:

• Define temporal inclusion dependencies as candidates for foreign keys between two dynamic tables.

• Design of a scalable, distributed algorithm for the discovery of inclusion dependencies. Implementation of the Algorithm in Apache Spark

• Creation of a Gold-Standard on webtables

• Evaluation on real-world datasets, such as all relational tables in the History of Wikipedia (the dataset is already available)

For more information please contact Prof. Felix Naumann, Tobias Bleifuß or Leon Bornemann.

[1] Papenbrock, Thorsten, et al. "Divide & conquer-based inclusion dependency discovery." Proceedings of the VLDB Endowment 8.7 (2015): 774-785.

[2] Tschirschnitz, Fabian, Thorsten Papenbrock, and Felix Naumann. "Detecting inclusion dependencies on very many tables." ACM Transactions on Database Systems (TODS) 42.3 (2017): 18.

Further Related Work on adapting Dependencies to Temporal Databases:

[3] Jensen, Christian S., Richard T. Snodgrass, and Michael D. Soo. "Extending existing dependency theory to temporal databases." IEEE Transactions on Knowledge and Data Engineering 8.4 (1996): 563-582.

[4] Artale, Alessandro, and Enrico Franconi. "Temporal ER modeling with description logics." International Conference on Conceptual Modeling. Springer, Berlin, Heidelberg, 1999.

The maintenance of many data sources takes place in laborious detail work by individual users. For example, the infoboxes on Wikipedia, which many search engines use as a source of factual knowledge, are maintained in this way. A user might update the number of inhabitants for all counties in Mississippi or update their mayors after a local election. For this purpose, (s)he searches for all articles for the individual counties and changes the semi-structured data of infoboxes there. However, different types of errors occur: the user may have forgotten counties or might confuse a county with an identical county in another state or use too many or too few trailing zeros.

In the Janus project [1], we are currently examining how this type of systematic change can follow a concise, logical description. For this purpose, we are translating a set of these changes into an update-query similar to SQL. But to do this, we first need to identify that set of changes that belongs to a systematic change. The recognition of the limits of systematic changes (i.e. when did the user start, when was (s)he finished, did (s)he stop the work and do something else in the meantime?) is a non-trivial matter, which shall be solved by this master thesis.

The work will develop a clustering procedure that considers both the temporal distribution of changes and the semantics of the changes. The resulting clusters should have a clearly recognizable systematic aspect and be complete with respect to this aspect (the user has not made any further changes that correspond to this aspect). Possibly the clusters could be organized hierarchically. Part of the work is the comparison against a baseline and selecting a suitable methodology for this comparison.

[1] https://IANVS.org/

For more information please contact Prof. Felix Naumann or Tobias Bleifuß.

Web tables constitute a rich and often free source of information. Unfortunately, not all information contained in them is always trustworthy and up-to-date. However, the table's change-history can help assess the trustworthiness and timeliness of its information: Who has authored the table? When was the table last updated? How much was changed? This analysis can, of course, be performed at table-level, but it is much more valuable if done on a cell- or value-level. Only then there is a chance to recognize quality discrepancies within a table.

Tracing the change history by hand is a very tedious task. Tables often change their layout and schema over the course of their lifetime, making it difficult to track which cells are predecessors or successors to each other. In addition, individual cells can be split into multiple cells and, vice-versa, merged into one. The goal of this thesis is to develop an algorithm that automatically constructs the edit history of individual cells in web tables as a graph. Each node in this graph represents one version of one cell at a particular point in time and each edge represents the relationship of these cell versions.

In the context of the Janus project [1] we have already developed an algorithm for matching tables over time and also a baseline algorithm that matches cells (with some constraints) over time. The master's thesis can build on this work and in particular overcome its limitations. For example, the work can focus on improved semantic interpretation of the tables and on scaling aspects. There is already a small gold standard for cell matching, but part of the task is to extend and possibly adapt it. The previous implementation is available in Java, so knowledge of this programming language is beneficial.

[1] https://www.IANVS.org

For more information please contact Prof. Felix Naumann or Tobias Bleifuß.

Machine learning and deep learning are increasingly seen as silver bullets for many difficult knowledge mining tasks. Stanford’s InfoLab has proposed and provides the novel Snorkel platform (http://hazyresearch.github.io/snorkel/), which alleviates many of the difficult preparatory tasks involved in applying learning methods to real-world problems. In particular, it significantly reduces the painful work of creating training data by reducing the task to the creation of a few simple tagging functions in lieu of manually tagging actual data.

We propose to apply this technique to the notoriously difficult problems of named entity recognition (NER) and relationship extraction (RE). One particular goal is to find out with how few and how simple tagging functions one can get away with.

For more information please contact Prof. Felix Naumann or Michael Loster.