μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers

Luís Cruz-Filipe; Sofia Kostopoulou; Fabrizio Montesi; Jonas Vistrup

doi:10.1007/978-3-031-46235-1_1

μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers

Luís Cruz-Filipe, Sofia Kostopoulou, Fabrizio Montesi, Jonas Vistrup^*

^*Kontaktforfatter

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

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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.

Originalsprog	Engelsk
Titel	Service-Oriented and Cloud Computing - : Proceedings of IFIP WG 6.12 European Conference
Redaktører	George A. Papadopoulos, Florian Rademacher, Jacopo Soldani
Forlag	Springer Science+Business Media
Publikationsdato	2023
Sider	3-18
ISBN (Trykt)	9783031462344
DOI	https://doi.org/10.1007/978-3-031-46235-1_1
Status	Udgivet - 2023
Begivenhed	10th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2023 - Larnaca, Cypern Varighed: 24. okt. 2023 → 25. okt. 2023

Konference

Konference	10th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2023
Land/Område	Cypern
By	Larnaca
Periode	24/10/2023 → 25/10/2023

Navn	Lecture Notes in Computer Science
Vol/bind	14183
ISSN	0302-9743

Bibliografisk note

Funding Information:
Work partially supported by Villum Fonden, grants no. 29518 and 50079, and the Independent Research Fund Denmark, grant no. 0135-00219.

Dokumenter og links

10.1007/978-3-031-46235-1_1

Open Access VersionAccepteret manuskript, 570 KB

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

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Citationsformater

Cruz-Filipe, L., Kostopoulou, S., Montesi, F., & Vistrup, J. (2023). μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers. I G. A. Papadopoulos, F. Rademacher, & J. Soldani (red.), Service-Oriented and Cloud Computing - : Proceedings of IFIP WG 6.12 European Conference (s. 3-18). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-46235-1_1

Cruz-Filipe, Luís ; Kostopoulou, Sofia ; Montesi, Fabrizio et al. / μ XL : Explainable Lead Generation with Microservices and Hypothetical Answers. Service-Oriented and Cloud Computing - : Proceedings of IFIP WG 6.12 European Conference. red. / George A. Papadopoulos ; Florian Rademacher ; Jacopo Soldani. Springer Science+Business Media, 2023. s. 3-18 (Lecture Notes in Computer Science, Bind 14183).

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title = "μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers",

abstract = "Lead generation refers to the identification of potential topics (the {\textquoteleft}leads{\textquoteright}) 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.",

keywords = "Explainable AI, Lead generation, Microservices",

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note = "Funding Information: Work partially supported by Villum Fonden, grants no. 29518 and 50079, and the Independent Research Fund Denmark, grant no. 0135-00219.; 10th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2023 ; Conference date: 24-10-2023 Through 25-10-2023",

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doi = "10.1007/978-3-031-46235-1_1",

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Cruz-Filipe, L, Kostopoulou, S , Montesi, F & Vistrup, J 2023, μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers. i GA Papadopoulos, F Rademacher & J Soldani (red), Service-Oriented and Cloud Computing - : Proceedings of IFIP WG 6.12 European Conference. Springer Science+Business Media, Lecture Notes in Computer Science, bind 14183, s. 3-18, 10th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2023, Larnaca, Cypern, 24/10/2023. https://doi.org/10.1007/978-3-031-46235-1_1

μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers. / Cruz-Filipe, Luís; Kostopoulou, Sofia ; Montesi, Fabrizio et al.
Service-Oriented and Cloud Computing - : Proceedings of IFIP WG 6.12 European Conference. red. / George A. Papadopoulos; Florian Rademacher; Jacopo Soldani. Springer Science+Business Media, 2023. s. 3-18 (Lecture Notes in Computer Science, Bind 14183).

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - μ XL

T2 - 10th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2023

AU - Cruz-Filipe, Luís

AU - Kostopoulou, Sofia

AU - Montesi, Fabrizio

AU - Vistrup, Jonas

N1 - Funding Information: Work partially supported by Villum Fonden, grants no. 29518 and 50079, and the Independent Research Fund Denmark, grant no. 0135-00219.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Explainable AI

KW - Lead generation

KW - Microservices

U2 - 10.1007/978-3-031-46235-1_1

DO - 10.1007/978-3-031-46235-1_1

M3 - Article in proceedings

AN - SCOPUS:85175827095

SN - 9783031462344

T3 - Lecture Notes in Computer Science

SP - 3

EP - 18

BT - Service-Oriented and Cloud Computing -

A2 - Papadopoulos, George A.

A2 - Rademacher, Florian

A2 - Soldani, Jacopo

PB - Springer Science+Business Media

Y2 - 24 October 2023 through 25 October 2023

ER -

Cruz-Filipe L, Kostopoulou S , Montesi F , Vistrup J. μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers. I Papadopoulos GA, Rademacher F, Soldani J, red., Service-Oriented and Cloud Computing - : Proceedings of IFIP WG 6.12 European Conference. Springer Science+Business Media. 2023. s. 3-18. (Lecture Notes in Computer Science, Bind 14183). doi: 10.1007/978-3-031-46235-1_1

μ XL: Explainable Lead Generation with Microservices and Hypothetical Answers

Abstract

Konference

Bibliografisk note

Dokumenter og links

SDU link

Fingeraftryk

Relaterede projekter

X-IDF: X-IDF: Explainable Internet Data Flows

Forklarlig Kunstig Intelligens og Loven

Choco: Choco: Choreographies for connected IT systems

Citationsformater