2021/02/08 by Angelos‐Christos G. Anadiotis, Angelos-Christos Anadiotis, Oana Balalau +20 · 1 citation
Computer Science · Decision Sciences · Physics and Astronomy · #Advanced Database Systems and Queries #Complex Network Analysis Techniques #Computer science #Data Management and Algorithms #Data mining #Data science #Database #Databases (cs.DB) #FOS: Computer and information sciences #Graph #Information retrieval #Journalism #Keyword search #Limiting #Media studies #Scalability #Schema (genetic algorithms) #Scientific Computing and Data Management #Sociology #Theoretical computer science #Web Data Mining and Analysis #cs.DB
paper · pdf · open access · doi:10.48550/arxiv.2102.04141
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/02/08 · openalex publication_date 2021/02/08 · arxiv updated 2021/02/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Investigative Journalism (IJ, in short) is staple of modern, democratic societies. IJ often necessitates working with large, dynamic sets of heterogeneous, schema-less data sources, which can be structured, semi-structured, or textual, limiting the applicability of classical data integration approaches. In prior work, we have developed ConnectionLens, a system capable of integrating such sources into a single heterogeneous graph, leveraging Information Extraction (IE) techniques; users can then query the graph by means of keywords, and explore query results and their neighborhood using an interactive GUI. Our keyword search problem is complicated by the graph heterogeneity, and by the lack of a result score function that would allow to prune some of the search space. In this work, we describe an actual IJ application studying conflicts of interest in the biomedical domain, and we show how ConnectionLens supports it. Then, we present novel techniques addressing the scalability challenges raised by this application: one allows to reduce the significant IE costs while building the graph, while the other is a novel, parallel, in-memory keyword search engine, which achieves orders of magnitude speed-up over our previous engine. Our experimental study on the real-world IJ application data confirms the benefits of our contributions.