29 citations · 63 across the 11 of their papers we have counts for
11 papers
Worst-case Analysis for Interactive Evaluation of Boolean Provenance
Antoine Amarilli, Yael Amsterdamer
In recent work, we have introduced a framework for fine-grained consent management in databases, which combines Boolean data provenance with the field of interactive Boolean evalua…
Selecting Sub-tables for Data Exploration
Kathy Razmadze, Yael Amsterdamer, Amit Somech +2
We present a framework for creating small, informative sub-tables of large data tables to facilitate the first step of data science: data exploration. Given a large data table tabl…
Multi-Document Keyphrase Extraction: Dataset, Baselines and Review
Ori Shapira, Ramakanth Pasunuru, Ido Dagan +1
Keyphrase extraction has been extensively researched within the single-document setting, with an abundance of methods, datasets and applications. In contrast, multi-document keyphr…
Evaluating Interactive Summarization: an Expansion-Based Framework
Ori Shapira, Ramakanth Pasunuru, Hadar Ronen +3
Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization…
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Ori Shapira, David Gabay, Yang Gao +5
Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries…
Top-k Querying of Unknown Values under Order Constraints (Extended Version)
Antoine Amarilli, Yael Amsterdamer, Tova Milo +1
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…
Uncertainty in Crowd Data Sourcing under Structural Constraints
Antoine Amarilli, Yael Amsterdamer, Tova Milo
Applications extracting data from crowdsourcing platforms must deal with the uncertainty of crowd answers in two different ways: first, by deriving estimates of the correct value f…
On the Complexity of Mining Itemsets from the Crowd Using Taxonomies
Antoine Amarilli, Yael Amsterdamer, Tova Milo
We study the problem of frequent itemset mining in domains where data is not recorded in a conventional database but only exists in human knowledge. We provide examples of such sce…
Putting Lipstick on Pig: Enabling Database-style Workflow Provenance
Yael Amsterdamer, Susan B. Davidson, Daniel Deutch +3
Workflow provenance typically assumes that each module is a "black-box", so that each output depends on all inputs (coarse-grained dependencies). Furthermore, it does not model the…
On the Limitations of Provenance for Queries With Difference
Yael Amsterdamer, Daniel Deutch, Val Tannen
The annotation of the results of database transformations was shown to be very effective for various applications. Until recently, most works in this context focused on positive qu…
Provenance for Aggregate Queries
Yael Amsterdamer, Daniel Deutch, Val Tannen
We study in this paper provenance information for queries with aggregation. Provenance information was studied in the context of various query languages that do not allow for aggre…