We are happy to announce the acceptance of two PVLDB papers, a first one was accepted in November.
Entity Resolution On-Demand
Giovanni Simonini, Luca Zecchini, Sonia Bergamaschi (Università degli Studi di Modena e Reggio Emilia, Italy), Felix Naumann
PVLDB, 2022
https://vldb.org/2022/
Entity Resolution (ER) aims to identify and merge records that refer to the same real-world entity. ER is typically employed as an expensive cleaning step on the entire data before consuming it. Yet, determining which entities are useful once cleaned depends solely on the user's application, which may need only a fraction of them. For instance, when dealing with Web data, we would like to be able to filter the entities of interest gathered from multiple sources without cleaning the entire, continuously-growing data. Similarly, when querying data lakes, we want to transform data on-demand and return the results in a timely manner - a fundamental requirement of ELT (Extract-Load-Transform) pipelines.
We propose BrewER, a framework to evaluate SQL SP queries on dirty data while progressively returning results as if they were issued on cleaned data. BrewER tries to focus the cleaning effort on one entity at a time, following an ORDER BY predicate. Thus, it inherently supports top-k and stop-and-resume execution. For a wide range of applications, a significant amount of resources can be saved. We exhaustively evaluate and show the efficacy of BrewER on four real-world datasets.
Detecting Layout Templates in Complex Multiregion Files
Gerardo Vitagliano, Lan Jiang, Felix Naumann
PVLDB, 2022
https://vldb.org/2022/
Spreadsheets are among the most commonly used file formats for data management, distribution, and analysis. Their widespread employment makes it easy to gather large collections of data, but their flexible canvas-based structure makes automated analysis difficult without heavy preparation. One of the common problems that practitioners face is the presence of multiple, independent regions in a single spreadsheet, possibly separated by repeated empty cells. We define such files as “multiregion” files. In collections of various spreadsheets, we can observe that some share the same layout. We present the Mondrian approach to automatically identify layout templates across multiple files and systematically extract the corresponding regions. Our approach is composed of three phases: first, each file is rendered as an image and inspected for elements that could form regions; then, using a clustering algorithm, the identified elements are grouped to form regions; finally, every file layout is represented as a graph and compared with others to find layout templates. We compare our method to state-of-the-art table recognition algorithms on two corpora of real-world enterprise spreadsheets. Our approach shows the best performances in detecting reliable region boundaries within each file and can correctly identify recurring layouts across files.
Fast Constraint-based Error Detection
Eduardo H. M. Pena, Eduardo C. de Almeida, Felix Naumann
PVLDB, 2022
https://vldb.org/2022/
The detection of constraint-based errors is a critical task in many data cleaning solutions. Previous works perform the task either using traditional data management systems or using specialized systems that speed up error detection. Unfortunately, both approaches may fail to execute in a reasonable time or even exhaust the available memory in the attempt. To address the main drawbacks of previous approaches, we present the FAst Constraint-based Error DeTector (FACET) to detect violations of denial constraints (DCs). FACET uses column sketch information to organize a pipeline of special operators for DC predicates and it implements these operators using a set of efficient algorithms and data structures that adapt to different data characteristics and predicate structures. We evaluate our system on a diverse array of datasets and constraints, showing its robustness and performance gains compared to different types of DBMSs and to a specialized system.
