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Abstract-We investigate an abstraction method, called meanfield method, for the performance evaluation of dynamic networks with pairwise communication between nodes. It allows us to evaluate systems with very large numbers of nodes, that is, systems of a size where traditional performance evaluation methods fall short.While the mean-field analysis is well-established in epidemics and for chemical reaction systems, it is rarely used for communication networks because a mean-field model tends to abstract away the underlying topology.To represent topological information, however, we extend the mean-field analysis with the concept of classes of states. At the abstraction level of classes we define the network topology by means of connectivity between nodes. This enables us to encode physical node positions and model dynamic networks by allowing nodes to change their class membership whenever they make a local state transition. Based on these extensions, we derive and implement algorithms for automating a mean-field based performance evaluation.

Abstract-Programmatic data integration approaches such as mashups have become a viable approach to dynamically integrate web data at runtime. Key data sources for mashups include entity search engines and hidden databases that need to be queried via source-specific search interfaces or web forms. Current mashups are typically restricted to simple query approaches such as using keyword search. Such approaches may need a high number of queries if many objects have to be found. Furthermore, the effectiveness of the queries may be limited, i.e., they may miss relevant results. We therefore propose more advanced search strategies that aim at finding a set of entities with high efficiency and high effectiveness. Our strategies use different kinds of queries that are determined by source-specific query generators. Furthermore, the queries are selected based on the characteristics of input entities. We introduce a flexible model for entity search strategies that includes a ranking of candidate queries determined by different query generators. We describe different query generators and outline their use within four entity search strategies. These strategies apply different query ranking and selection approaches to optimize efficiency and effectiveness. We evaluate our search strategies in detail for two domains: product search and publication search. The comparison with a standard keyword search shows that the proposed search strategies provide significant improvements in both domains.

We demonstrate a new powerful mashup tool called WETSUIT (Web EnTity Search and fUsIon Tool) to search and integrate web data from diverse sources and domain-specific entity search engines. WETSUIT supports adaptive search strategies to query sets of relevant entities with a minimum of communication overhead. Mashups can be composed using a set of high-level operators based on the Java-compatible language Scala. The operator implementation supports a high degree of parallel processing, in particular a streaming of entities between all data transformation operations facilitating a fast presentation of intermediate results. WETSUIT has already been applied to solve challenging integration tasks from different domains.