Article

$μ$ XL: Explainable Lead Generation with Microservices and Hypothetical Answers

Authors:

Luís Cruz-Filipe,

Sofia Kostopoulou,

Fabrizio Montesi,

Jonas VistrupAuthors Info & Claims

Service-Oriented and Cloud Computing: 10th IFIP WG 6.12 European Conference, ESOCC 2023, Larnaca, Cyprus, October 24–25, 2023, Proceedings

Pages 3 - 18

https://doi.org/10.1007/978-3-031-46235-1_1

Published: 24 October 2023 Publication History

Abstract

Lead generation refers to the identification of potential topics (the ‘leads’) of importance for journalists to report on. In this paper we present a new lead generation tool based on a microservice architecture, which includes a component of explainable AI. The lead generation tool collects and stores historical and real-time data from a web source, like Google Trends, and generates current and future leads. These leads are produced by an engine for hypothetical reasoning based on logical rules, which is a novel implementation of a recent theory. Finally, the leads are displayed on a web interface for end users, in particular journalists. This interface provides information on why a specific topic is or may become a lead, assisting journalists in deciding where to focus their attention. We carry out an empirical evaluation of the performance of our tool.

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Cited By

Cruz-Filipe LGaspar GNunes I(2024)Hypothetical Answers to Continuous Queries Over Data StreamsACM Transactions on Computational Logic10.1145/368884525:4(1-40)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3688845
Cruz-Filipe LKostopoulou SMontesi FVistrup J(2024)XL: explainable lead generation with microservices and hypothetical answersComputing10.1007/s00607-024-01321-x106:11(3419-3445)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1007/s00607-024-01321-x

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Published In

cover image Guide Proceedings

Service-Oriented and Cloud Computing: 10th IFIP WG 6.12 European Conference, ESOCC 2023, Larnaca, Cyprus, October 24–25, 2023, Proceedings

Oct 2023

294 pages

ISBN:978-3-031-46234-4

DOI:10.1007/978-3-031-46235-1

Editors:
George A. Papadopoulos
https://ror.org/02qjrjx09University of Cyprus, Nicosia, Cyprus
,
Florian Rademacher
https://ror.org/04xfq0f34RWTH Aachen University, Aachen, Germany
,
Jacopo Soldani
https://ror.org/03ad39j10University of Pisa, Pisa, Italy

© IFIP International Federation for Information Processing 2023.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 24 October 2023

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Cited By

Cruz-Filipe LGaspar GNunes I(2024)Hypothetical Answers to Continuous Queries Over Data StreamsACM Transactions on Computational Logic10.1145/368884525:4(1-40)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3688845
Cruz-Filipe LKostopoulou SMontesi FVistrup J(2024)XL: explainable lead generation with microservices and hypothetical answersComputing10.1007/s00607-024-01321-x106:11(3419-3445)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1007/s00607-024-01321-x

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