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Yael Amsterdamer is a professor at the
Department of Computer Science,
Bar-Ilan University.
Short Bio
Yael Amsterdamer is a professor at the Department of Computer Science in Bar-Ilan University. Her main research field is databases and data management.
In 2016, Prof. Amsterdamer has received the Allon Fellowship for Outstanding Young Researchers by the Israeli Council for Higher Education.
Prof. Amsterdamer has completed her B.Sc. as a double major in Computer Science and
Linguistics at Tel Aviv University. Before and during
her studies she has worked as a software engineer.
Prof. Amsterdamer has completed her PhD studies
as a member of the Database Group of the Computer Science School, Tel Aviv University,
and under the supervision of Prof. Tova Milo.
Her PhD thesis is titled "On Harvesting and Exploiting Data Patterns Using the Crowd".
On 2010-2011 Prof. Amsterdamer has been a visiting scholar of the Computer and Information
Science Department at the University of Pennsylvania, Philadelphia, US,
hosted by Prof. Val Tannen. From Philadelphia she continued to Paris, France,
and was a visiting researcher at INRIA, working as part of the Webdam project
with Prof. Pierre Senellart and Prof. Serge Abiteboul.
Research
I am interested in the development of theoretical tools and algorithms for the management of data, and specifically Web data, data on collaborative platforms, knowledge graphs and crowdsourced data.
Some of my ongoing research projects:
Managing Fine-Grained Consent in Shared Databases. In this research project, we consider data whose usage requires its owners' consent, such as personal or proprietary data. Instead of asking for consent in advance, which is often unnecessarily broad, we consider an alternative consent management approach where fine-grained consent is asked on a need basis. This approach allows supporting data derived from multiple owner’s data and aims to minimize and efficiently select consent requests. This project is partially supported by the Israel Science Foundation (ISF).
Interactive Explorative Summarization. In this research project, we consider the task of interactive summarization of multiple documents, as means for providing an incremental summary whose content and scope are dynamically influenced by a user.This is a joint project with Prof. Ido Dagan and is partially supported by the Israel Ministry of Science, Technology and Space (MOST).
Publications
Also see my publications at DBLP, ORCID and Google Scholar
CleanEr: Interactive, Query-Guided Error Mitigation for Data Cleaning Systems
Ran Schreiber and Yael Amsterdamer.
ICDE 2024. | PDF |
Poster |
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Interactive SPARQL Query Formulation using Provenance
Yael Amsterdamer and Yehuda Callen.
Springer Knowledge and Information Systems (KAIS) 2023. | PDF |
BIB |
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Query-Guided Resolution of Uncertain Databases
Osnat Drien, Matanya Freiman, Antoine Amarilli and Yael Amsterdamer.
SIGMOD 2023. | PDF |
BIB |
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Selecting Sub-tables for Data Exploration
Yael Amsterdamer, Susan B. Davidson, Tova Milo, Kathy Razmadze and Amit Somech.
ICDE 2023. | PDF |
BIB |
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ActivePDB: Active Probabilistic Databases
Osnat Drien, Matanya Freiman, and Yael Amsterdamer.
PVLDB 2022. | PDF |
BIB |
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Interactive Knowledge Graph Querying through Examples and Facets
Yael Amsterdamer and Yael Amsterdamer and Laura Gáspár.
ADBIS 2022. | PDF |
Slides |
BIB |
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Provenance-Based SPARQL Query Formulation
Yael Amsterdamer and Yehuda Callen.
DEXA 2022. | PDF |
Slides |
BIB |
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Interactive Query-Assisted Summarization via Deep Reinforcement Learning
Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Ido Dagan, and Yael Amsterdamer.
NAACL-HLT 2022. | PDF |
BIB |
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SubTab: Data Exploration with Informative Sub-Tables
Kathy Razmadze, Yael Amsterdamer, Amit Somech, Susan B. Davidson and Tova Milo.
SIGMOD 2022. | PDF |
BIB |
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Worst-case Analysis for Interactive Evaluation of Boolean Provenance
Antoine Amarilli and Yael Amsterdamer.
TaPP 2022. | PDF |
Slides |
BIB |
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Automated Selection of Multiple Datasets for Extension by Integration
Yael Amsterdamer and Moran Ben-Yehuda.
CIKM 2021. | PDF |
Slides |
BIB |
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Managing Consent for Data Access in Shared Databases
Osnat Drien, Antoine Amarilli and Yael Amsterdamer.
ICDE 2021. | PDF |
BIB |
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SPARQLIt: Interactive SPARQL Query Refinement
Yael Amsterdamer and Yehuda Callen.
ICDE 2021. | PDF |
BIB |
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Extending Multi-Document Summarization Evaluation to the Interactive Setting
Ori Shapira, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Yael Amsterdamer and Ido Dagan.
NAACL-HLT 2021. | PDF |
BIB |
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Diverse User Selection for Opinion Procurement
Yael Amsterdamer and Oded Goldreich.
EDBT 2020. | PDF |
Slides |
BIB |
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Towards Fine-Grained Data Access Control through Active Peer Probing
Yael Amsterdamer and Osnat Drien.
EDBT 2020. | PDF |
BIB |
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Declarative User Selection with Soft Constraints
Yael Amsterdamer, Tova Milo, Amit Somech and Brit Youngman.
CIKM 2019. | PDF |
BIB |
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PePPer: Fine-grained Personal Access Control via Peer Probing
Yael Amsterdamer and Osnat Drien.
ICDE 2019. | PDF |
BIB |
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Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Ori Shapira, David Gabay, Yang Gao, Hadar Ronen, Ramakanth Pasunuru, Mohit Bansal, Yael Amsterdamer and Ido Dagan.
NAACL-HLT 2019. | PDF |
BIB |
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Evaluating Multiple System Summary Lengths: A Case Study
Ori Shapira, David Gabay, Hadar Ronen, Judit Bar-Ilan, Yael Amsterdamer, Ani Nenkova and Ido Dagan.
EMNLP 2018. | PDF |
BIB |
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PODIUM: Procuring Opinions from Diverse Users in a Multi-Dimensional World
Yael Amsterdamer and Oded Goldreich.
CIKM 2017. | PDF |
BIB |
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Interactive Abstractive Summarization for Event News Tweets
Ori Shapira, Hadar Ronen, Meni Adler, Yael Amsterdamer, Judit Bar-Ilan and Ido Dagan.
EMNLP 2017. | PDF |
BIB |
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Crowd Mining and Analysis
Yael Amsterdamer and Tova Milo.
Encyclopedia of Database Systems, L Liu, MT Özsu (eds.), Springer New York, NY, 2017. | PDF |
BIB |
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Top-k Queries on Unknown Values under Order Constraints
Antoine Amarilli, Yael Amsterdamer, Tova Milo and Pierre Senellart.
ICDT 2017. | PDF (extended version) |
BIB |
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December: A Declarative Tool for Crowd Member Selection
Yael Amsterdamer, Tova Milo, Amit Somech and Brit Youngman.
PVLDB 9(13): 1485-1488, 2016. | PDF |
BIB |
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Toward Semantic Image Similarity from Crowdsourced Clustering
Yanir Kleiman, George Goldberg, Yael Amsterdamer and Daniel Cohen-Or.
The Visual Computer 32(6-8): 1045-1055, 2016. | PDF |
BIB |
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A Natural Language Interface for Querying General and Individual Knowledge
Yael Amsterdamer, Anna Kukliansky and Tova Milo.
PVLDB 8(12): 1430-1441, 2015. | PDF (full version) |
BIB |
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Optimal Probabilistic Generation of XML Documents
Serge Abiteboul, Yael Amsterdamer, Daniel Deutch, Tova Milo and Pierre Senellart.
TOCS 57(4): 806-842, 2015. | PDF |
BIB |
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NL2CM: A Natural Language Interface to Crowd Mining (demo)
Yael Amsterdamer, Anna Kukliansky and Tova Milo.
SIGMOD 2015. | PDF |
BIB |
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Managing General and Individual Knowledge in Crowd Mining Applications
Yael Amsterdamer, Susan B. Davidson, Anna Kukliansky, Tova Milo, Slava Novgorodov, and Amit Somech.
CIDR 2015. | PDF |
Slides |
BIB |
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Foundations of Crowd Data Sourcing
Yael Amsterdamer and Tova Milo
SIGMOD Record 43(4), 2014. | PDF |
BIB |
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OASSIS: Query Driven Crowd Mining
Yael Amsterdamer, Susan B. Davidson, Tova Milo, Slava Novgorodov and Amit Somech.
SIGMOD 2014. | PDF |
BIB |
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Ontology Assisted Crowd Mining (demo)
Yael Amsterdamer, Susan B. Davidson, Tova Milo, Slava Novgorodov and Amit Somech.
PVLDB 7(13): 1597-1600, 2014. | PDF |
BIB |
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On the Complexity of Mining Itemsets from the Crowd Using Taxonomies
Antoine Amarilli, Yael Amsterdamer and Tova Milo.
ICDT 2014. | PDF |
BIB|
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Uncertainty in Crowd Data Sourcing Under Structural Constraints
Antoine Amarilli, Yael Amsterdamer and Tova Milo.
UnCrowd 2014. | PDF |
BIB |
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CrowdMiner: Mining association rules from the crowd (demo)
Yael Amsterdamer, Yael Grossman, Tova Milo and Pierre Senellart.
PVLDB 6(12):1250-1253, 2013. | PDF |
BIB |
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Crowd Mining
Yael Amsterdamer, Yael Grossman, Tova Milo and Pierre Senellart.
SIGMOD 2013. | PDF |
BIB |
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Auto-Completion Learning for XML (demo)
Serge Abiteboul, Yael Amsterdamer, Tova Milo and Pierre Senellart.
SIGMOD 2012. | PDF |
BIB |
poster |
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On Provenance Minimization
Yael Amsterdamer, Daniel Deutch, Tova Milo and Val Tannen.
ACM Trans. Database Syst. 37(4):30, 2012. | PDF |
BIB |
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Finding Optimal Probabilistic Generators for XML Collections
Serge Abiteboul, Yael Amsterdamer, Daniel Deutch, Tova Milo and Pierre Senellart.
ICDT 2012. | PDF |
BIB |
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Putting Lipstick on Pig: Enabling Database-style Workflow Provenance
Yael Amsterdamer, Susan B. Davidson, Daniel Deutch, Tova Milo, Julia Stoyanovich and Val Tannen.
PVLDB 5(4):346-357, 2011. | PDF |
BIB |
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On Provenance Minimization
Yael Amsterdamer, Daniel Deutch, Tova Milo and Val Tannen.
PODS 2011. | PDF
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Provenance for Aggregate Queries
Yael Amsterdamer, Daniel Deutch and Val Tannen.
PODS 2011. | PDF | BIB |
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On the Optimality of Top-k Algorithms for Interactive Web Applications
Yael Amsterdamer, Daniel Deutch and Tova Milo.
WebDB 2011. | PDF (full version) | BIB |
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On the Limitations of Provenance for Queries With Difference
Yael Amsterdamer, Daniel Deutch and Val Tannen.
TaPP 2011. | PDF | BIB |
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Service
Listed below are some recent roles I've held.
Teaching
Bar-Ilan University
Introduction to Object Oriented Programming
Management of Big Web Data
Workshop in Data Management
Web, Crowd and Big Data Management: seminar
Database Systems
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Yael Amsterdamer
Contact Details
| Email |
yael.amsterdamer biu.ac.il |
| Office Address |
Bar-Ilan University,
Max and Anna Webb st.
Ramat Gan 5290002,
Israel
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| Office |
Data Management Lab,
Building 216,
room 005 |
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