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Documents authored by Ilyas, Ihab F.

Document
DOI: 10.4230/LIPIcs.ICDT.2019.6
A Formal Framework for Probabilistic Unclean Databases

Authors: Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, and Theodoros Rekatsinas

Published in: LIPIcs, Volume 127, 22nd International Conference on Database Theory (ICDT 2019)

Abstract
Most theoretical frameworks that focus on data errors and inconsistencies follow logic-based reasoning. Yet, practical data cleaning tools need to incorporate statistical reasoning to be effective in real-world data cleaning tasks. Motivated by empirical successes, we propose a formal framework for unclean databases, where two types of statistical knowledge are incorporated: The first represents a belief of how intended (clean) data is generated, and the second represents a belief of how noise is introduced in the actual observed database. To capture this noisy channel model, we introduce the concept of a Probabilistic Unclean Database (PUD), a triple that consists of a probabilistic database that we call the intention, a probabilistic data transformator that we call the realization and captures how noise is introduced, and an observed unclean database that we call the observation. We define three computational problems in the PUD framework: cleaning (infer the most probable intended database, given a PUD), probabilistic query answering (compute the probability of an answer tuple over the unclean observed database), and learning (estimate the most likely intention and realization models of a PUD, given examples as training data). We illustrate the PUD framework on concrete representations of the intention and realization, show that they generalize traditional concepts of repairs such as cardinality and value repairs, draw connections to consistent query answering, and prove tractability results. We further show that parameters can be learned in some practical instantiations, and in fact, prove that under certain conditions we can learn a PUD directly from a single dirty database without any need for clean examples.
Cite as

Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, and Theodoros Rekatsinas. A Formal Framework for Probabilistic Unclean Databases. In 22nd International Conference on Database Theory (ICDT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 127, pp. 6:1-6:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{desa_et_al:LIPIcs.ICDT.2019.6,
  author =	{De Sa, Christopher and Ilyas, Ihab F. and Kimelfeld, Benny and R\'{e}, Christopher and Rekatsinas, Theodoros},
  title =	{{A Formal Framework for Probabilistic Unclean Databases}},
  booktitle =	{22nd International Conference on Database Theory (ICDT 2019)},
  pages =	{6:1--6:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-101-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{127},
  editor =	{Barcelo, Pablo and Calautti, Marco},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2019.6},
  URN =		{urn:nbn:de:0030-drops-103083},
  doi =		{10.4230/LIPIcs.ICDT.2019.6},
  annote =	{Keywords: Unclean databases, data cleaning, probabilistic databases, noisy channel}
}
Document
DOI: 10.4230/DagSemProc.08421.3
08421 Working Group: Classification, Representation and Modeling

Authors: Anish Das Sarma, Ander de Keijzer, Amol Deshpande, Peter J. Haas, Ihab F. Ilyas, Christoph Koch, Thomas Neumann, Dan Olteanu, Martin Theobald, and Vasilis Vassalos

Published in: Dagstuhl Seminar Proceedings, Volume 8421, Uncertainty Management in Information Systems (2009)

Abstract
This report briefly summarizes the discussions carried out in the working group on classification, representation and modeling of uncertain data. The discussion was divided into two subgroups: the first subgroup studied how different representation and modeling alternatives currently proposed can fit in a bigger picture of theory and technology interaction, while the second subgroup focused on contrasting current system implementations and the reasons behind such diverse class of available prototypes. We summarize the findings of these two groups and the future steps suggested by group members.
Cite as

Anish Das Sarma, Ander de Keijzer, Amol Deshpande, Peter J. Haas, Ihab F. Ilyas, Christoph Koch, Thomas Neumann, Dan Olteanu, Martin Theobald, and Vasilis Vassalos. 08421 Working Group: Classification, Representation and Modeling. In Uncertainty Management in Information Systems. Dagstuhl Seminar Proceedings, Volume 8421, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{dassarma_et_al:DagSemProc.08421.3,
  author =	{Das Sarma, Anish and de Keijzer, Ander and Deshpande, Amol and Haas, Peter J. and Ilyas, Ihab F. and Koch, Christoph and Neumann, Thomas and Olteanu, Dan and Theobald, Martin and Vassalos, Vasilis},
  title =	{{08421 Working Group: Classification, Representation and Modeling}},
  booktitle =	{Uncertainty Management in Information Systems},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8421},
  editor =	{Christoph Koch and Birgitta K\"{o}nig-Ries and Volker Markl and Maurice van Keulen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08421.3},
  URN =		{urn:nbn:de:0030-drops-19410},
  doi =		{10.4230/DagSemProc.08421.3},
  annote =	{Keywords: }
}
Document
DOI: 10.4230/DagSemProc.08421.6
08421 Working Group: Lineage/Provenance

Authors: Anish Das Sarma, Amol Deshpande, Thomas Hubauer, Ihab F. Ilyas, Birgitta König-Ries, Matthias Renz, and Martin Theobald

Published in: Dagstuhl Seminar Proceedings, Volume 8421, Uncertainty Management in Information Systems (2009)

Abstract
The following summary tries to capture a collection of state-of-the-art techniques and challenges for future work on lineage management in uncertain and probabilistic databases that we discussed in our working group. It was one half of a larger committee that we had initially formed, which then got split into two groups---one focusing on lineage as a means of explanation of data, and one focusing more on lineage usage in probabilistic databases (see also the "Explanation" working group report for more details on the first subgroup).
Cite as

Anish Das Sarma, Amol Deshpande, Thomas Hubauer, Ihab F. Ilyas, Birgitta König-Ries, Matthias Renz, and Martin Theobald. 08421 Working Group: Lineage/Provenance. In Uncertainty Management in Information Systems. Dagstuhl Seminar Proceedings, Volume 8421, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{dassarma_et_al:DagSemProc.08421.6,
  author =	{Das Sarma, Anish and Deshpande, Amol and Hubauer, Thomas and Ilyas, Ihab F. and K\"{o}nig-Ries, Birgitta and Renz, Matthias and Theobald, Martin},
  title =	{{08421 Working Group: Lineage/Provenance}},
  booktitle =	{Uncertainty Management in Information Systems},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8421},
  editor =	{Christoph Koch and Birgitta K\"{o}nig-Ries and Volker Markl and Maurice van Keulen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08421.6},
  URN =		{urn:nbn:de:0030-drops-19318},
  doi =		{10.4230/DagSemProc.08421.6},
  annote =	{Keywords: Lineage and provenance, probabilistic databases, challenges}
}
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