In this paper, we describe the solver we submitted for both heuristic tracks of the PACE challenge 2025 on the dominating set problem and the hitting set problem. We solve both problems as unicost set covering problems. Our solver first performs reductions on the instance. Then greedy algorithms generate an initial solution that serves as starting point of the large neighborhood search and the local search which are executed afterwards. The solver ranked first in the dominating set heuristic track, and second in the hitting set heuristic track.

SUM queries are crucial for many applications that need to deal with uncertain data. In this paper, we are interested in the queries, called ALL_SUM, that return all possible sum values and their probabilities. In general, there is no efficient solution for the problem of evaluating ALL_SUM queries. But, for many practical applications, where aggregate values are small integers or real numbers with small precision, it is possible to develop efficient solutions. In this paper, based on a recursive approach, we propose a new solution for those applications. We implemented our solution and conducted an extensive experimental evaluation over synthetic and real-world data sets; the results show its effectiveness.

P2Prec is a social-based P2P recommendation system for large-scale content sharing that leverages content-based and social-based recommendation. The main idea is to recommend high quality documents related to query topics and contents held by useful friends (of friends) of the users, by exploiting friendship networks. We have implemented a prototype of P2Prec using the Shared-Data Overlay Network (SON), an open source development platform for P2P networks using web services, JXTA and OSGi. In this paper, we describe the demo of P2Prec's main services (installing P2Prec peers, initializing peers, gossiping topics of interest among friends, key-word querying for contents) using our prototype implemented as an application of SON.