CrossFlow: Integrating Workflow Management and Electronic Commerce

The CrossFlow1 architecture provides support for cross-organisational workflow management in dynamically established virtual enterprises. The creation of a business relationship between a service provider organisation performing a service on behalf of a consumer organisation can be made dynamic when augmented by virtual market technology, the dynamic configuration of the contract enactment infrastructures, and the provision of fine grained service monitoring and control. Standard ways of describing services and contracts can be combined with matchmaking technology to create a virtual market for such service provision and consumption. A provider can then advertise its services in the market and consumers can search for a compatible business partner. This provides choice in selecting a partner and allows the deferment of the decision to a point in time where it can be made on the most up-to-date requirements of the consumer and service offers in the market. The penalty for deferred decision making is the time to set up the infrastructure in each organisation for the dynamically established contract. Thus, a further aspect of CrossFlow was to exploit the contract in the dynamic and automatic configuration of the contract enactment and supervision infrastructures of the respective organisations and in linking them in a dynamic fashion. The electronic contract, which results from the agreement between the newly established business partners, completely specifies the intended collaboration between them. Given the importance of the business process enacted by the provider, this includes fine-grained monitoring and control to allow tight co-operation between the organisations

Expertise-based peer selection in Peer-to-Peer networks

Peer-to-Peer systems have proven to be an effective way of sharing data. Modern protocols are able to efficiently route a message to a given peer. However, determining the destination peer in the first place is not always trivial. We propose a a message to a given peer. However, determining the destination peer in the first place is not always trivial. We propose a model in which peers advertise their expertise in the Peer-to-Peer network. The knowledge about the expertise of other peers forms a semantic topology. Based on the semantic similarity between the subject of a query and the expertise of other peers, a peer can select appropriate peers to forward queries to, instead of broadcasting the query or sending it to a random set of peers. To calculate our semantic similarity measure, we make the simplifying assumption that the peers share the same ontology. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments complemented with a real-world field experiment, we show how expertise-based peer selection improves the performance of a Peer-to-Peer system with respect to precision, recall and the number of messages

Enabling query technologies for the semantic sensor web

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes

Scalable Delivery of Stream Query Result

Continuous queries over data streams typically produce large volumes of result streams. To scale up the system, one should carefully study the problem of delivering the result streams to the end users, which, unfortunately, is often over-looked in existing systems. In this paper, we leverage Distributed Publish/Subscribe System (DPSS), a scalable data dissemination infrastructure, for efficient stream query result delivery. To take advantage of DPSS's multicast-like data dissemination architecture, one has to exploit the common contents among different result streams and maximize the sharing of their delivery. Hence, we propose to merge the user queries into a few representative queries whose results subsume those of the original ones, and disseminate the result streams of these representative queries through the DPSS. To realize this approach, we study the stream query containment theories and propose efficient query grouping and merging algorithms. The proposed approach is non-intrusive and hence can be easily implemented as a middleware to be incorporated into existing stream processing systems. A prototype is developed on top of an open- source stream processing system and results of an extensive performance study on real datasets verify the efficacy of the proposed techniques

GSN, Middleware for Streaming World (Best Demo Award)

In this paper we present the Global Sensor Networks (gsn.sf.net) and the NexTick (nextick.org) projects

Improving the Throughput of Distributed Hash Tables Using Congestion-Aware Routing

Advanced applications for Distributed Hash Tables (DHTs), such as Peer-to-Peer Information Retrieval, require a DHT to quickly and efficiently process a large number (in the order of millions) of requests. In this paper we study mechanisms to optimize the throughput of DHTs. Our goal is to maximize the number of route operations per peer per second a DHT can perform (given certain constraints on the lookup delay). Each peer receives congestion feedback from the DHT, which it uses to adjust its routing decisions. This way, peers can avoid routing through slow parts of the overlay network and hence increase the rate at which they insert new messages into the DHT.We provide a numerical analysis of congestion-aware routing in DHTs and show that considerable improvements in throughput are possible compared to DHTs with proximity neighbor selection and strictly greedy routing