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Documents authored by Milo, Tova

Document
DOI: 10.4230/LIPIcs.ICDT.2022.4
On the Hardness of Category Tree Construction

Authors: Shay Gershtein, Uri Avron, Ido Guy, Tova Milo, and Slava Novgorodov

Published in: LIPIcs, Volume 220, 25th International Conference on Database Theory (ICDT 2022)

Abstract
Category trees, or taxonomies, are rooted trees where each node, called a category, corresponds to a set of related items. The construction of taxonomies has been studied in various domains, including e-commerce, document management, and question answering. Multiple algorithms for automating construction have been proposed, employing a variety of clustering approaches and crowdsourcing. However, no formal model to capture such categorization problems has been devised, and their complexity has not been studied. To address this, we propose in this work a combinatorial model that captures many practical settings and show that the aforementioned empirical approach has been warranted, as we prove strong inapproximability bounds for various problem variants and special cases when the goal is to produce a categorization of the maximum utility. In our model, the input is a set of n weighted item sets that the tree would ideally contain as categories. Each category, rather than perfectly match the corresponding input set, is allowed to exceed a given threshold for a given similarity function. The goal is to produce a tree that maximizes the total weight of the sets for which it contains a matching category. A key parameter is an upper bound on the number of categories an item may belong to, which produces the hardness of the problem, as initially each item may be contained in an arbitrary number of input sets. For this model, we prove inapproximability bounds, of order Θ̃(√n) or Θ̃(n), for various problem variants and special cases, loosely justifying the aforementioned heuristic approach. Our work includes reductions based on parameterized randomized constructions that highlight how various problem parameters and properties of the input may affect the hardness. Moreover, for the special case where the category must be identical to the corresponding input set, we devise an algorithm whose approximation guarantee depends solely on a more granular parameter, allowing improved worst-case guarantees. Finally, we also generalize our results to DAG-based and non-hierarchical categorization.
Cite as

Shay Gershtein, Uri Avron, Ido Guy, Tova Milo, and Slava Novgorodov. On the Hardness of Category Tree Construction. In 25th International Conference on Database Theory (ICDT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 220, pp. 4:1-4:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{gershtein_et_al:LIPIcs.ICDT.2022.4,
  author =	{Gershtein, Shay and Avron, Uri and Guy, Ido and Milo, Tova and Novgorodov, Slava},
  title =	{{On the Hardness of Category Tree Construction}},
  booktitle =	{25th International Conference on Database Theory (ICDT 2022)},
  pages =	{4:1--4:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-223-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{220},
  editor =	{Olteanu, Dan and Vortmeier, Nils},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2022.4},
  URN =		{urn:nbn:de:0030-drops-158785},
  doi =		{10.4230/LIPIcs.ICDT.2022.4},
  annote =	{Keywords: maximum independent set, approximation algorithms, approximation hardness bounds, taxonomy construction, category tree construction}
}
Document
DOI: 10.4230/DagMan.7.1.1
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)

Authors: Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, and Ke Yi

Published in: Dagstuhl Manifestos, Volume 7, Issue 1 (2018)

Abstract
The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing data and knowledge. This work has involved a rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. We anticipate on-going expansion of PDM research as the technology and applications involving data management continue to grow and evolve. In particular, the lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. In this report we identify some of the most important research directions where the PDM community has the potential to make significant contributions. This is done from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.
Cite as

Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, and Ke Yi. Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). In Dagstuhl Manifestos, Volume 7, Issue 1, pp. 1-29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{abiteboul_et_al:DagMan.7.1.1,
  author =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  title =	{{Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)}},
  pages =	{1--29},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2018},
  volume =	{7},
  number =	{1},
  editor =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.7.1.1},
  URN =		{urn:nbn:de:0030-drops-86772},
  doi =		{10.4230/DagMan.7.1.1},
  annote =	{Keywords: database theory, principles of data management, query languages, efficient query processing, query optimization, heterogeneous data, uncertainty, knowledge-enriched data management, machine learning, workflows, human-related data, ethics}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.ICDT.2017.3
The Smart Crowd - Learning from the Ones Who Know (Invited Talk)

Authors: Tova Milo

Published in: LIPIcs, Volume 68, 20th International Conference on Database Theory (ICDT 2017)

Abstract
One of the foremost challenges for information technology over the last few years has been to explore, understand, and extract useful information from large amounts of data. Some particular tasks such as annotating data or matching entities have been outsourced to human workers for many years. But the last few years have seen the rise of a new research field called crowdsourcing that aims at delegating a wide range of tasks to human workers, building formal frameworks, and improving the efficiency of these processes. In order to provide sound scientific foundations for crowdsourcing and support the development of efficient crowd sourcing processes, adequate formal models and algorithms must be defined. In particular, the models must formalize unique characteristics of crowd-based settings, such as the knowledge of the crowd and crowd-provided data; the interaction with crowd members; the inherent inaccuracies and disagreements in crowd answers; and evaluation metrics that capture the cost and effort of the crowd. Clearly, what may be achieved with the help of the crowd depends heavily on the properties and knowledge of the given crowd. In this talk we will focus on knowledgeable crowds. We will examine the use of such crowds, and in particular domain experts, for assisting solving data management problems. Specifically we will consider three dimensions of the problem: (1) How domain experts can help in improving the data itself, e.g. by gathering missing data and improving the quality of existing data, (2) How they can assist in gathering meta-data that facilitate improved data processing, and (3) How can we find and identify the most relevant crowd for a given data management task.
Cite as

Tova Milo. The Smart Crowd - Learning from the Ones Who Know (Invited Talk). In 20th International Conference on Database Theory (ICDT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 68, p. 3:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{milo:LIPIcs.ICDT.2017.3,
  author =	{Milo, Tova},
  title =	{{The Smart Crowd - Learning from the Ones Who Know}},
  booktitle =	{20th International Conference on Database Theory (ICDT 2017)},
  pages =	{3:1--3:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-024-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{68},
  editor =	{Benedikt, Michael and Orsi, Giorgio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2017.3},
  URN =		{urn:nbn:de:0030-drops-70643},
  doi =		{10.4230/LIPIcs.ICDT.2017.3},
  annote =	{Keywords: data management, crowdsourcing}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2017.5
Top-k Querying of Unknown Values under Order Constraints

Authors: Antoine Amarilli, Yael Amsterdamer, Tova Milo, and Pierre Senellart

Published in: LIPIcs, Volume 68, 20th International Conference on Database Theory (ICDT 2017)

Abstract
Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given over known and unknown values: under these constraints, we assume that all possible worlds are equally likely. Our work is the first to propose a principled scheme to derive the value distributions and expected values of unknown items in this setting, with the goal of computing estimated top-k results by interpolating the unknown values from the known ones. We study the complexity of this general task, and show tight complexity bounds, proving that the problem is intractable, but can be tractably approximated. We then consider the case of tree-shaped partial orders, where we show a constructive PTIME solution. We also compare our problem setting to other top-k definitions on uncertain data.
Cite as

Antoine Amarilli, Yael Amsterdamer, Tova Milo, and Pierre Senellart. Top-k Querying of Unknown Values under Order Constraints. In 20th International Conference on Database Theory (ICDT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 68, pp. 5:1-5:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{amarilli_et_al:LIPIcs.ICDT.2017.5,
  author =	{Amarilli, Antoine and Amsterdamer, Yael and Milo, Tova and Senellart, Pierre},
  title =	{{Top-k Querying of Unknown Values under Order Constraints}},
  booktitle =	{20th International Conference on Database Theory (ICDT 2017)},
  pages =	{5:1--5:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-024-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{68},
  editor =	{Benedikt, Michael and Orsi, Giorgio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2017.5},
  URN =		{urn:nbn:de:0030-drops-70457},
  doi =		{10.4230/LIPIcs.ICDT.2017.5},
  annote =	{Keywords: uncertainty, partial order, unknown values, crowdsourcing, interpolation}
}
Document
DOI: 10.4230/DagRep.6.4.39
Foundations of Data Management (Dagstuhl Perspectives Workshop 16151)

Authors: Marcelo Arenas, Richard Hull, Wim Marten, Tova Milo, and Thomas Schwentick

Published in: Dagstuhl Reports, Volume 6, Issue 4 (2016)

Abstract
In this Workshop we have explored the degree to which principled foundations are crucial to the long-term success and effectiveness of the new generation of data management paradigms and applications, and investigated what forms of research need to be pursued to develop and advance these foundations. The workshop brought together specialists from the existing database theory community, and from adjoining areas, particularly from various subdisciplines within the Big Data community, to understand the challenge areas that might be resolved through principled foundations and mathematical theory.
Cite as

Marcelo Arenas, Richard Hull, Wim Marten, Tova Milo, and Thomas Schwentick. Foundations of Data Management (Dagstuhl Perspectives Workshop 16151). In Dagstuhl Reports, Volume 6, Issue 4, pp. 39-56, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@Article{arenas_et_al:DagRep.6.4.39,
  author =	{Arenas, Marcelo and Hull, Richard and Marten, Wim and Milo, Tova and Schwentick, Thomas},
  title =	{{Foundations of Data Management (Dagstuhl Perspectives Workshop 16151)}},
  pages =	{39--56},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{4},
  editor =	{Arenas, Marcelo and Hull, Richard and Marten, Wim and Milo, Tova and Schwentick, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.6.4.39},
  URN =		{urn:nbn:de:0030-drops-61526},
  doi =		{10.4230/DagRep.6.4.39},
  annote =	{Keywords: Foundations of data management, Principles of databases}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2016.12
Filtering With the Crowd: CrowdScreen Revisited

Authors: Benoit Groz, Ezra Levin, Isaac Meilijson, and Tova Milo

Published in: LIPIcs, Volume 48, 19th International Conference on Database Theory (ICDT 2016)

Abstract
Filtering a set of items, based on a set of properties that can be verified by humans, is a common application of CrowdSourcing. When the workers are error-prone, each item is presented to multiple users, to limit the probability of misclassification. Since the Crowd is a relatively expensive resource, minimizing the number of questions per item may naturally result in big savings. Several algorithms to address this minimization problem have been presented in the CrowdScreen framework by Parameswaran et al. However, those algorithms do not scale well and therefore cannot be used in scenarios where high accuracy is required in spite of high user error rates. The goal of this paper is thus to devise algorithms that can cope with such situations. To achieve this, we provide new theoretical insights to the problem, then use them to develop a new efficient algorithm. We also propose novel optimizations for the algorithms of CrowdScreen that improve their scalability. We complement our theoretical study by an experimental evaluation of the algorithms on a large set of synthetic parameters as well as real-life crowdsourcing scenarios, demonstrating the advantages of our solution.
Cite as

Benoit Groz, Ezra Levin, Isaac Meilijson, and Tova Milo. Filtering With the Crowd: CrowdScreen Revisited. In 19th International Conference on Database Theory (ICDT 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 48, pp. 12:1-12:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{groz_et_al:LIPIcs.ICDT.2016.12,
  author =	{Groz, Benoit and Levin, Ezra and Meilijson, Isaac and Milo, Tova},
  title =	{{Filtering With the Crowd: CrowdScreen Revisited}},
  booktitle =	{19th International Conference on Database Theory (ICDT 2016)},
  pages =	{12:1--12:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-002-6},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{48},
  editor =	{Martens, Wim and Zeume, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2016.12},
  URN =		{urn:nbn:de:0030-drops-57817},
  doi =		{10.4230/LIPIcs.ICDT.2016.12},
  annote =	{Keywords: CrowdSourcing, filtering, algorithms, sprt, hypothesis testing}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2015.76
Answering Conjunctive Queries with Inequalities

Authors: Paraschos Koutris, Tova Milo, Sudeepa Roy, and Dan Suciu

Published in: LIPIcs, Volume 31, 18th International Conference on Database Theory (ICDT 2015)

Abstract
In this parer, we study the complexity of answering conjunctive queries (CQ) with inequalities. In particular, we compare the complexity of the query with and without inequalities. The main contribution of our work is a novel combinatorial technique that enables the use of any Select-Project-Join query plan for a given CQ without inequalities in answering the CQ with inequalities, with an additional factor in running time that only depends on the query. To achieve this, we define a new projection operator that keeps a small representation (independent of the size of the database) of the set of input tuples that map to each tuple in the output of the projection; this representation is used to evaluate all the inequalities in the query. Second, we generalize a result by Papadimitriou-Yannakakis [PODS'97] and give an alternative algorithm based on the color-coding technique [Alon, Yuster and Zwick, PODS'02] to evaluate a CQ with inequalities by using an algorithm for the CQ without inequalities. Third, we investigate the structure of the query graph, inequality graph, and the augmented query graph with inequalities, and show that even if the query and the inequality graphs have bounded treewidth, the augmented graph not only can have an unbounded treewidth but can also be NP-hard to evaluate. Further, we illustrate classes of queries and inequalities where the augmented graphs have unbounded treewidth, but the CQ with inequalities can be evaluated in poly-time. Finally, we give necessary properties and sufficient properties that allow a class of CQs to have poly-time combined complexity with respect to any inequality pattern.
Cite as

Paraschos Koutris, Tova Milo, Sudeepa Roy, and Dan Suciu. Answering Conjunctive Queries with Inequalities. In 18th International Conference on Database Theory (ICDT 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 31, pp. 76-93, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{koutris_et_al:LIPIcs.ICDT.2015.76,
  author =	{Koutris, Paraschos and Milo, Tova and Roy, Sudeepa and Suciu, Dan},
  title =	{{Answering Conjunctive Queries with Inequalities}},
  booktitle =	{18th International Conference on Database Theory (ICDT 2015)},
  pages =	{76--93},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-79-8},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{31},
  editor =	{Arenas, Marcelo and Ugarte, Mart{\'\i}n},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2015.76},
  URN =		{urn:nbn:de:0030-drops-49781},
  doi =		{10.4230/LIPIcs.ICDT.2015.76},
  annote =	{Keywords: query evaluation, conjunctive query, inequality, treewidth}
}
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