Search Results for author:
Found 20 papers, 5 papers with code
Know Better – A Clickbait Resolving Challenge
In this paper, we present a new corpus of clickbait articles annotated by university students along with a corresponding shared task: clickbait articles use a headline or teaser that hides information from the reader to make them curious to open the article.
CardBench: A Benchmark for Learned Cardinality Estimation in Relational Databases
In contrast to other initial benchmarks, our benchmark is much more diverse and can be used for training and testing learned models systematically.
COSTREAM: Learned Cost Models for Operator Placement in Edge-Cloud Environments
In this work, we present COSTREAM, a novel learned cost model for Distributed Stream Processing Systems that provides accurate predictions of the execution costs of a streaming query in an edge-cloud environment.
SPARE: A Single-Pass Neural Model for Relational Databases
While there has been extensive work on deep neural networks for images and text, deep learning for relational databases (RDBs) is still a rather unexplored field.
Towards Foundation Models for Relational Databases [Vision Paper]
However, existing approaches only learn a representation from a single table, and thus ignore the potential to learn from the full structure of relational databases, including neighboring tables that can contain important information for a contextualized representation.
Towards Multi-Modal DBMSs for Seamless Querying of Texts and Tables
In this paper, we propose Multi-Modal Databases (MMDBs), which is a new class of database systems that can seamlessly query text and tables using SQL.
DiffML: End-to-end Differentiable ML Pipelines
In this paper, we present our vision of differentiable ML pipelines called DiffML to automate the construction of ML pipelines in an end-to-end fashion.
Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases
However, developing a conversational agent (i. e., a chatbot-like interface) to allow end-users to interact with an application using natural language requires both immense amounts of training data and NLP expertise.
ASET: Ad-hoc Structured Exploration of Text Collections [Extended Abstract]
In this paper, we propose a new system called ASET that allows users to perform structured explorations of text collections in an ad-hoc manner.
It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings
Since data is often stored in different sources, it needs to be integrated to gather a global view that is required in order to create value and derive knowledge from it.
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction
In this paper, we introduce zero-shot cost models which enable learned cost estimation that generalizes to unseen databases.
One Model to Rule them All: Towards Zero-Shot Learning for Databases
In this paper, we present our vision of so called zero-shot learning for databases which is a new learning approach for database components.
AnyDB: An Architecture-less DBMS for Any Workload
In this paper, we propose a radical new approach for scale-out distributed DBMSs.
Summarization Beyond News: The Automatically Acquired Fandom Corpora
Large state-of-the-art corpora for training neural networks to create abstractive summaries are mostly limited to the news genre, as it is expensive to acquire human-written summaries for other types of text at a large scale.
DeepDB: Learn from Data, not from Queries!
The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model.
Databases
Progressive Data Science: Potential and Challenges
We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.
Model-based Approximate Query Processing
However, classical AQP approaches suffer from various problems that limit the applicability to support the ad-hoc exploration of a new data set: (1) Classical AQP approaches that perform online sampling can support ad-hoc exploration queries but yield low quality if executed over rare subpopulations.
IDEBench: A Benchmark for Interactive Data Exploration
Existing benchmarks for analytical database systems such as TPC-DS and TPC-H are designed for static reporting scenarios.
Databases
An End-to-end Neural Natural Language Interface for Databases
The ability to extract insights from new data sets is critical for decision making.
A-Tree: A Bounded Approximate Index Structure
At the core of our index is a tunable error parameter that allows a DBA to balance lookup performance and space consumption.
Databases
