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Found 19 papers, 4 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.
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
