14 citations · 56 across the 16 of their papers we have counts for
15 papers · 1 filter
LINX: A Language Driven Generative System for Goal-Oriented Automated Data Exploration
Tavor Lipman, Tova Milo, Amit Somech +2
Data exploration is a challenging process in which users examine a dataset by iteratively employing a series of queries. While in some cases the user explores a new dataset to beco…
Learning Approximation Sets for Exploratory Queries
Susan B. Davidson, Tova Milo, Kathy Razmadze +1
In data exploration, executing complex non-aggregate queries over large databases can be time-consuming. Our paper introduces a novel approach to address this challenge, focusing o…
FEDEX: An Explainability Framework for Data Exploration Steps
Daniel Deutch, Amir Gilad, Tova Milo +2
When exploring a new dataset, Data Scientists often apply analysis queries, look for insights in the resulting dataframe, and repeat to apply further queries. We propose in this pa…
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…
Just in Time: Personal Temporal Insights for Altering Model Decisions
Naama Boer, Daniel Deutch, Nave Frost +1
The interpretability of complex Machine Learning models is coming to be a critical social concern, as they are increasingly used in human-related decision-making processes such as…
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)
Serge Abiteboul, Marcelo Arenas, Pablo Barceló +18
In April 2016, a community of researchers working in the area of Principles of Data Management (PDM) joined in a workshop at the Dagstuhl Castle in Germany. The workshop was organi…
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…
Answering Conjunctive Queries with Inequalities
Paraschos Koutris, Tova Milo, Sudeepa Roy +1
In this paper, we study the complexity of answering conjunctive queries (CQ) with inequalities). In particular, we are interested in comparing the complexity of the query with and…
Answering Regular Path Queries on Workflow Provenance
Xiaocheng Huang, Zhuowei Bao, Susan B. Davidson +2
This paper proposes a novel approach for efficiently evaluating regular path queries over provenance graphs of workflows that may include recursion. The approach assumes that an ex…
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…
A Propagation Model for Provenance Views of Public/Private Workflows
Susan B. Davidson, Tova Milo, Sudeepa Roy
We study the problem of concealing functionality of a proprietary or private module when provenance information is shown over repeated executions of a workflow which contains both…
Labeling Workflow Views with Fine-Grained Dependencies
Zhuowei Bao, Susan B. Davidson, Tova Milo
This paper considers the problem of efficiently answering reachability queries over views of provenance graphs, derived from executions of workflows that may include recursion. Suc…
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…
Provenance Views for Module Privacy
Susan B. Davidson, Sanjeev Khanna, Tova Milo +2
Scientific workflow systems increasingly store provenance information about the module executions used to produce a data item, as well as the parameter settings and intermediate da…