
Giuseppe Della Penna, Benedetto Intrigila, Igor Melatti, Enrico Tronci, and Marisa Venturini Zilli. "Finite horizon analysis of Markov Chains with the Mur$\varphi$ verifier." Int. J. Softw. Tools Technol. Transf. 8, no. 4 (2006): 397–409. SpringerVerlag. ISSN: 14332779. DOI: 10.1007/s1000900502167.
Abstract: In this paper we present an explicit diskbased verification algorithm for Probabilistic Systems defining discrete time/finite state Markov Chains. Given a Markov Chain and an integer k (horizon), our algorithm checks whether the probability of reaching an error state in at most k steps is below a given threshold. We present an implementation of our algorithm within a suitable extension of the Mur$\varphi$ verifier. We call the resulting probabilistic model checker FHPMur$\varphi$ (Finite Horizon Probabilistic Mur$\varphi$). We present experimental results comparing FHPMur$\varphi$ with (a finite horizon subset of) PRISM, a stateoftheart symbolic model checker for Markov Chains. Our experimental results show that FHPMur$\varphi$ can handle systems that are out of reach for PRISM, namely those involving arithmetic operations on the state variables (e.g. hybrid systems).



Giuseppe Della Penna, Benedetto Intrigila, Igor Melatti, Enrico Tronci, and Marisa Venturini Zilli. "Exploiting Transition Locality in Automatic Verification of Finite State Concurrent Systems." Sttt 6, no. 4 (2004): 320–341. DOI: 10.1007/s1000900401496.
Abstract: In this paper we show that statistical properties of the transition graph of a system to be verified can be exploited to improve memory or time performances of verification algorithms. We show experimentally that protocols exhibit transition locality. That is, with respect to levels of a breadthfirst state space exploration, state transitions tend to be between states belonging to close levels of the transition graph. We support our claim by measuring transition locality for the set of protocols included in the Mur$\varphi$ verifier distribution. We present a cachebased verification algorithm that exploits transition locality to decrease memory usage and a diskbased verification algorithm that exploits transition locality to decrease disk read accesses, thus reducing the time overhead due to disk usage. Both algorithms have been implemented within the Mur$\varphi$ verifier. Our experimental results show that our cachebased algorithm can typically save more than 40% of memory with an average time penalty of about 50% when using (Mur$\varphi$) bit compression and 100% when using bit compression and hash compaction, whereas our diskbased verification algorithm is typically more than ten times faster than a previously proposed diskbased verification algorithm and, even when using 10% of the memory needed to complete verification, it is only between 40 and 530% (300% on average) slower than (RAM) Mur$\varphi$ with enough memory to complete the verification task at hand. Using just 300 MB of memory our diskbased Mur$\varphi$ was able to complete verification of a protocol with about $10^9$ reachable states. This would require more than 5 GB of memory using standard Mur$\varphi$.



Flavio Chierichetti, Silvio Lattanzi, Federico Mari, and Alessandro Panconesi. "On Placing Skips Optimally in Expectation." In Web Search and Web Data Mining (WSDM 2008), edited by M. Najork, A. Z. Broder and S. Chakrabarti, 15–24. Acm, 2008. DOI: 10.1145/1341531.1341537.
Abstract: We study the problem of optimal skip placement in an inverted list. Assuming the query distribution to be known in advance, we formally prove that an optimal skip placement can be computed quite efficiently. Our best algorithm runs in time O(n log n), n being the length of the list. The placement is optimal in the sense that it minimizes the expected time to process a query. Our theoretical results are matched by experiments with a real corpus, showing that substantial savings can be obtained with respect to the tra ditional skip placement strategy, that of placing consecutive skips, each spanning sqrt(n) many locations.
Keywords: Information Retrieval

