Proving Continuity of Coinductive Global Bisimulation Distances: A Never Ending Story

We have developed a notion of global bisimulation distance between processes which goes somehow beyond the notions of bisimulation distance already existing in the literature, mainly based on bisimulation games. Our proposal is based on the cost of transformations: how much we need to modify one of the compared processes to obtain the other. Our original definition only covered finite processes, but a coinductive approach allows us to extend it to cover infinite but finitary trees. After having shown many interesting properties of our distance, it was our intention to prove continuity with respect to projections, but unfortunately the issue remains open. Nonetheless, we have obtained several partial results that are presented in this paper.Comment: In Proceedings PROLE 2015, arXiv:1512.0617

A Distance-Heuristic Tree Building Approach in Application Layer Multicast

In the application layer multicast (ALM), clustering nearby nodes can effectively improve the multicast performance. However, it is difficult for the ALM solution to quickly and accurately position the newcomer, because group members have no direct knowledge of underlying network topology. Additionally, ALM delivery trees with different performances are built when group members join the group in different join sequences. To alleviate the above problems, this paper proposes a distance-heuristic tree building protocol (called DHTB). DHTB uses our proposed distance-constrained cluster model and close-member-first-receive (CF) rule. In the model, most nearby nodes are grouped into some distance-constrained clusters, with little cluster organization and maintenance overhead. The CF rule arranges or rearranges the locations of group members according to related distances, and effectively positions the newcomer with the help of on-demand landmarks. Both the distance-constrained cluster model and CF rule are distance-heuristic. Therefore DHTB can alleviate the join sequence problem, and build the ALM tree with desirable performance

Pathfinder: XQuery - The Relational Way

Relational query processors are probably the best understood (as well as the best engineered) query engines available today. Although carefully tuned to process instances of the relational model (tables of tuples), these processors can also provide a foundation for the evaluation of "alien" (non-relational) query languages: if a relational encoding of the alien data model and its associated query language is given, the RDBMS may act like a special-purpose processor for the new language

Exploring Caching for Efficient Collection Operations

Many useful programs operate on collection types. Extensive libraries are available in many programming languages, such as the C++ Standard Template Library, which make programming with collections convenient. Extending programming languages to provide collection queries as first class constructs in the language would not only allow programmers to write queries explicitly in their programs but it would also allow compilers to leverage the wealth of experience available from the database domain to optimize such queries. This paper describes an approach to reducing the run time of programs involving explicit collection queries by leveraging a cache to store previously computed results. We propose caching the results of join (sub)queries which allows queries that miss the cache entirely to be answered partially from the cache thereby improving the query execution time. We also describe an effective cache policy to determine which join (sub)queries to cache. the cache is maintained incrementally, when the underlying collections change, and use of the cache space is optimized by a cache replacement policy. © 2011 IEEE

Implementation of composite semijoins using a variation of Bloom filters.

Different from a centralized database system, distributed query processing involves data transmission among different sites and this communication cost is a dominant factor compared to local processing cost. So, the objective of distributed query optimization is to find strategies to minimize the amount of data transmitted over the network. Since optimal query processing in distributed database systems has been shown to be an NP-hard problem, heuristics are applied to find a near-optimal processing strategy. Previous research has mainly focused on the use of joins, semijoins, and hash semijoins (Bloom filters). The semijoin is a commonly recognized operator, which provides efficient query results. As a variation of semijoin, the composite semijoin is beneficial to do semijoins as one composite rather than as multiple single column semijoins. The Hash semijoin (which uses a Bloom filter) is used to minimize the cost of a semijoin operation. This thesis report provides a summary of each category of query processing techniques and optimization algorithms. Also in this thesis, we propose a new algorithm called Composite Semijoin Filter by combining the idea of composite semijoins, Bloom filters and PERF joins. One of the advantages of this algorithm is to avoid collisions. The algorithm is evaluated and compared with initial feasible solution (IFS) and another filter-based algorithm. It has been shown that the algorithm gives substantial reduction on relations and the total cost.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Z58. Source: Masters Abstracts International, Volume: 43-01, page: 0249. Adviser: Joan Morrissey. Thesis (M.Sc.)--University of Windsor (Canada), 2004