Context and Problem: 

Recently, many large data lakes, such as web tables, have emerged based on publicly available datasets. To extract knowledge from these corpora of tables, many discovery tasks require efficient and effective execution. An example of a table discovery task is union table search [7]. The goal of union search is to discover table-rows that can be added to a given input table. To achieve this, recent papers introduce various similarity measures. Examples of such measures are the value overlap similarity and semantic similarity [1, 2].

Current union table search approaches do not consider the existence of quality constraints in the input table. The discovered tables and their rows could violate the quality rules that exist in the original dataset. Note that, in this thesis, we formulate the quality rules in the form of denial constraints [8].

For example, a user-provided table can contain the information of country names and their capitals. A simple quality rule that can be defined on this table is a rule that requires a one-to-one relationship between the city and country values. An overlap-based union search approach would initially find tables regardless of this constraint. Therefore, it might find tables that contain country names and city names with an entirely different relationship. To compensate for this discrepancy, one would need to apply a constraint verification step after the union phase. One can use the traditional data cleaning tools [3, 4, 5, 6] to remove the data points that violate initially set constraints. This approach has two disadvantages:  

  1. The data cleaning approaches are computationally expensive. Increasing the number of rows in the dataset leads to a no-linear increase in the cleaning time [8]. Thus, it would be time-consuming to union the erroneous tables first and then detect and clean the violating rows in an offline scenario.
  2. The data cleaning approaches clean the data in a cell- or row-level granularity. Cleaning algorithms ignore the connection between the added cells/rows in their original tables. Considering our example, if a unionable table contains random city names instead of capital names, it is rational to ignore the whole table in the first place.

In this thesis, we are looking for an efficient solution that finds the maximum set of unionable tables that do not violate any of the given quality constraints. In this thesis, we will consider denial constraints as the quality assurance rules. The student is supposed to read literature on the state-of-the-art union discovery approaches [1, 2] and be able to implement them (or adapt and run the available source codes) as baselines. The student will introduce a new index, algorithm, or pruning strategy that discovers the violating tables before the union operation. In the end, the student should evaluate the approach with regard to time efficiency in obtaining clean table unions.


  • Strong fundamentals in database concepts.
  • Strong algorithm background and familiarity with solving np-hard problems.
  • Programming experience in Python, Scala, or Java.
  • Interest in data integration and courage to work with large data.

Related Work:

[1] Nargesian, Fatemeh, et al. "Table union search on open data." Proceedings of the VLDB Endowment 11.7 (2018): 813-825.

[2] Bogatu, Alex, et al. "Dataset discovery in data lakes." 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020.

[3] Chu, Xu, Ihab F. Ilyas, and Paolo Papotti. "Holistic data cleaning: Putting violations into context." 2013 IEEE 29th International Conference on Data Engineering (ICDE). IEEE, 2013.

[4] Rekatsinas, Theodoros, et al. "Holoclean: Holistic data repairs with probabilistic inference." arXiv preprint arXiv:1702.00820 (2017).

[5] Mahdavi, Mohammad, et al. "Raha: A configuration-free error detection system." Proceedings of the 2019 International Conference on Management of Data. 2019.

[6] Mahdavi, Mohammad, and Ziawasch Abedjan. "Baran: Effective error correction via a unified context representation and transfer learning." Proceedings of the VLDB Endowment 13.12 (2020): 1948-1961.

[7] Koutras, Christos, et al. "Valentine: Evaluating Matching Techniques for Dataset Discovery." 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021.

[8] Pena, Eduardo HM, et al. "Efficient Detection of Data Dependency Violations." Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020.

Advisor and Contact:

Mahdi Esmailoghli <esmailoghli@dbs.uni-hannover.de> (LUH)

Prof. Dr. Ziawasch Abedjan <abedjan@dbs.uni-hannover.de> (LUH)