Abstract
In this paper we propose to use Interaction Nets as a formalism for Visual Functional Programming. We consider the use of recursion patterns as a programming idiom, and introduce a suitable archetype/instantiation mechanism for interaction agents, which allows one to define agents whose behaviour is based on recursion patterns. © 2007, Universitatsbibliothek TU Berlin.

Abstract
This paper presents a formalism for defining higher-order systems based on the notion of graph transformation (by rewriting or interaction). The syntax is inspired by the Combinatory Reduction Systems of Klop. The rewrite rules can be used to define first-order systems, such as graph or term-graph rewriting systems, Lafont's interaction nets, the interaction systems of Asperti and Laneve, the non-deterministic nets of Alexiev, or a process calculus. They can also be used to specify higher-order systems such as hierarchical graphs and proof nets of Linear Logic, or to specify the operational semantics of graph-based languages.

Abstract
In this paper we give a graph-based decision procedure for a calculus with sum and product types. Although our motivation comes from the Bird-Meertens approach to reasoning algebraically about functional programs, the language used here can be seen as the internal language of a category with binary products and coproducts. As such, the decision procedure presented has independent interest. A standard approach based on term rewriting would work modulo a set of equations; the present work proposes a simpler approach, based on graph-rewriting. We show in turn how the system covers reflection equational laws, fusion laws, and cancellation laws.

Abstract
Optimistic distributed systems often rely on version vectors or their variants in order to track updates on replicated objects. Some of these mechanisms rely on some form of global configuration or distributed naming protocol in order to assign unique identifiers to each replica. These approaches are incompatible with replica creation under arbitrary partitions, a typical operation mode in mobile or poorly connected environments. Other mechanisms assign unique identifiers relying on statistical correctness. In previous work we have introduced an update tracking mechanism that overcomes these limitations. This paper presents results from recent experimentation, that brought to surface a particular pattern of operation that results in an unforeseen, unlimited growth in space consumption. We also describe informally a new update tracking mechanism that does not exhibit this pathological growth while providing guaranteed unique identifiers for a dynamic number of replicas under arbitrary partitions and the same functionality of version vectors.

Abstract
Range queries, retrieving all keys within a given range, is an important add-on for Distributed Hash Tables (DHTs), as they rely only on exact key matching lookup. In this paper we support range queries through a balanced tree algorithm, Decentralized Balanced Tree, that runs over any DHT system. Our algorithm is based on the B(+)-tree design that efficiently stores clustered data while maintaining a balanced load on hosts. The internal structure of the balanced tree is suited for range queries operations over many data distributions since it easily handles clustered data without losing performance. We analyzed, and evaluated our algorithm under a simulated environment, to show it's operation scalability for both insertions and queries. We will show that the system design. imposes a fixed penalty over the DHT access cost, and thus inherits the scalability properties of the chosen underlying DHT.

Abstract
Bloom filters provide space-efficient storage of sets at the cost of a probability of false positives on membership queries. The size of the filter must be defined a priori based on the number of elements to store and the desired false positive probability, being impossible to store extra elements without increasing the false positive probability. This leads typically to a conservative assumption regarding maximum set size, possibly by orders of magnitude, and a consequent space waste. This paper proposes Scalable Bloom Filters, a variant of Bloom filters that can adapt dynamically to the number of elements stored, while assuring a maximum false positive probability.