Exclusion and Object Tracking in a Network of Processes

This paper concerns two fundamental problems in distributed computing---mutual exclusion and mobile object tracking. For a variant of the mutual exclusion problem where the network topology is taken into account, all existing distributed solutions make use of tokens. It turns out that these token-based solutions for mutual exclusion can also be adapted for object tracking, as the token behaves very much like a mobile object. To handle objects with replication, we go further to consider the more general $k$-exclusion problem which has not been as well studied in a network setting. A strong fairness property for $k$-exclusion requires that a process trying to enter the critical section will eventually succeed even if \emph{up to} $k-1$ processes stay in the critical section indefinitely. We present a comparative survey of existing token-based mutual exclusion algorithms, which have provided much inspiration for later $k$-exclusion algorithms. We then propose two solutions to the $k$-exclusion problem, the second of which meets the strong fairness requirement. Fault-tolerance issues are also discussed along with the suggestion of a third algorithm that is also strongly fair. Performances of the three algorithms are compared by simulation. Finally, we show how the various exclusion algorithms can be adapted for tracking mobile objects

State of B\"uchi Complementation

Complementation of B\"uchi automata has been studied for over five decades since the formalism was introduced in 1960. Known complementation constructions can be classified into Ramsey-based, determinization-based, rank-based, and slice-based approaches. Regarding the performance of these approaches, there have been several complexity analyses but very few experimental results. What especially lacks is a comparative experiment on all of the four approaches to see how they perform in practice. In this paper, we review the four approaches, propose several optimization heuristics, and perform comparative experimentation on four representative constructions that are considered the most efficient in each approach. The experimental results show that (1) the determinization-based Safra-Piterman construction outperforms the other three in producing smaller complements and finishing more tasks in the allocated time and (2) the proposed heuristics substantially improve the Safra-Piterman and the slice-based constructions.Comment: 28 pages, 4 figures, a preliminary version of this paper appeared in the Proceedings of the 15th International Conference on Implementation and Application of Automata (CIAA

Introducing the sequence model for text retrieval

We propose and explore a novel approach,called the se- quence model,to text retrieval.The model di ffers from classical ones in the extent of how positional information of term occurrences is used for relevance judgment.In the sequence model,documents and queries are viewed as sequences of term-position pairs and the relevance of a doc- ument to a query is judged by the similarity between their respective rep- resentative sequences.We suggest three primitive measures of sequence similarity,each capturing a distinct aspect of resemblance between two sequences.These similarity measures can be combined in various ways to suit di fferent information needs.We have developed a prototype system with the sequence model as its core.Experimental results show that our sequence-based approach is often more e ffective than appearance-based approaches