scispace - formally typeset
P

Paola Mello

Researcher at University of Bologna

Publications -  242
Citations -  5549

Paola Mello is an academic researcher from University of Bologna. The author has contributed to research in topics: Logic programming & Abductive logic programming. The author has an hindex of 34, co-authored 236 publications receiving 5148 citations. Previous affiliations of Paola Mello include University of Trento & ENEA.

Papers
More filters
Book ChapterDOI

Process Mining Manifesto

Wil M. P. van der Aalst, +78 more
TL;DR: This manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users to increase the maturity of process mining as a new tool to improve the design, control, and support of operational business processes.
Journal ArticleDOI

Image analysis and rule-based reasoning for a traffic monitoring system

TL;DR: An approach for detecting vehicles in urban traffic scenes by means of rule-based reasoning on visual data and the synergy between the artificial intelligence techniques of the high-level and the low-level image analysis techniques provides the system with flexibility and robustness.
Journal ArticleDOI

Declarative specification and verification of service choreographiess

TL;DR: This work presents how DecSerFlow semantics can be mapped onto Linear Temporal Logic and onto Abductive Logic Programming, and illustrates the advantages of using a declarative language in conjunction with logic-based semantics.
Journal ArticleDOI

Verifiable agent interaction in abductive logic programming: The SCIFF framework

TL;DR: The declarative and operational semantics of the SCIFF language, and the termination, soundness, and completeness results of theSCIFF proof procedure, are presented and it is demonstrated that SCIFF's possible application in the multiagent domain is demonstrated.
Proceedings ArticleDOI

Image analysis and rule-based reasoning for a traffic monitoring system

TL;DR: The paper describes a system for detecting vehicles in urban traffic scenes in daytime and at night by means of image analysis and rule-based reasoning and the synergy between the artificial intelligence techniques of the high level and low-level image analysis techniques provides the system with flexibility and robustness.