Hazar Harmouch, PhD.

Selected publications

1. 

   Relational Header Discovery using Similarity Search in a Table Corpus.

   Hazar Harmouch, Thorsten Papenbrock, and Felix Naumann

   Proceedings of the International Conference on Data Engineering (ICDE) 2021

   Abs Bib PDF

   Column headers are among the most relevant types of meta-data for relational tables, because they provide meaning and context in which the data is to be interpreted. Headers play an important role in many data integration, exploration, and cleaning scenarios, such as schema matching, knowledge base augmentation, and similarity search. Unfortunately, in many cases column headers are missing, because they were never defined properly, are meaningless, or have been lost during data extraction, transmission, or storage. For example, around one third of the tables on the Web have missing headers. Missing headers leave abundant tabular data shrouded and inaccessible to many data-driven applications.We introduce a fully automated, multi-phase system that discovers table column headers for cases where headers are missing, meaningless, or unrepresentative for the column values. It leverages existing table headers from web tables to suggest human-understandable, representative, and consistent headers for any target table. We evaluate our system on tables extracted from Wikipedia. Overall, 60% of the automatically discovered table headers are exact and complete. Considering more header candidates, top-5 for example, increases this percentage to 72%.

   @article{harmouch2019coherance,
     title = {Relational Header Discovery using Similarity Search in a Table Corpus.},
     author = {Harmouch, Hazar and Papenbrock, Thorsten and Naumann, Felix},
     journal = {Proceedings of the International Conference on Data Engineering (ICDE)},
     volume = {},
     number = {},
     pages = {444--455},
     year = {2021},
     doi = {10.1109/ICDE51399.2021.00045},
   }

2. 

   Single-column data profiling

   Hazar Harmouch

   2020

   Abs Bib PDF

   The research area of data profiling consists of a large set of methods and processes to examine a given dataset and determine metadata about it. Typically, different data profiling tasks address different kinds of metadata, comprising either various statistics about individual columns (Single-column Analysis) or relationships among them (Dependency Discovery). Among the basic statistics about a column are data type, header, the number of unique values (the column’s cardinality), maximum and minimum values, the number of null values, and the value distribution. Dependencies involve, for instance, functional dependencies (FDs), inclusion dependencies (INDs), and their approximate versions. Data profiling has a wide range of conventional use cases, namely data exploration, cleansing, and integration. The produced metadata is also useful for database management and schema reverse engineering. Data profiling has also more novel use cases, such as big data analytics. The generated metadata describes the structure of the data at hand, how to import it, what it is about, and how much of it there is. Thus, data profiling can be considered as an important preparatory task for many data analysis and mining scenarios to assess which data might be useful and to reveal and understand a new dataset’s characteristics. In this thesis, the main focus is on the single-column analysis class of data profiling tasks. We study the impact and the extraction of three of the most important metadata about a column, namely the cardinality, the header, and the number of null values. First, we present a detailed experimental study of twelve cardinality estimation algorithms. We classify the algorithms and analyze their efficiency, scaling far beyond the original experiments and testing theoretical guarantees. Our results highlight their trade-offs and point out the possibility to create a parallel or a distributed version of these algorithms to cope with the growing size of modern datasets. Then, we present a fully automated, multi-phase system to discover human-understandable, representative, and consistent headers for a target table in cases where headers are missing, meaningless, or unrepresentative for the column values. Our evaluation on Wikipedia tables shows that 60% of the automatically discovered schemata are exact and complete. Considering more schema candidates, top-5 for example, increases this percentage to 72%. Finally, we formally and experimentally show the ghost and fake FDs phenomenon caused by FD discovery over datasets with missing values. We propose two efficient scores, probabilistic and likelihood-based, for estimating the genuineness of a discovered FD. Our extensive set of experiments on real-world and semi-synthetic datasets show the effectiveness and efficiency of these scores

   @phdthesis{Harmouch20,
     author = {Harmouch, Hazar},
     title = {Single-column data profiling},
     school = {University of Potsdam, Germany},
     year = {2020},
     url = {https://publishup.uni-potsdam.de/frontdoor/index/index/docId/47455},
     urn = {urn:nbn:de:kobv:517-opus4-474554},
     timestamp = {Sat, 17 Jul 2021 09:02:38 +0200},
     biburl = {https://dblp.org/rec/phd/dnb/Harmouch20.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
   }