Social dynamics in conferences: analyses of data from the Live Social Semantics application

Popularity and spread of online social networking in recent years has given a great momentum to the study of dynamics and patterns of social interactions. However, these studies have often been confined to the online world, neglecting its interdependencies with the offline world. This is mainly due to the lack of real data that spans across this divide. The Live Social Semantics application is a novel platform that dissolves this divide, by collecting and integrating data about people from (a) their online social networks and tagging activities from popular social networking sites, (b) their publications and co-authorship networks from semantic repositories, and (c) their real-world face-to-face contacts with other attendees collected via a network of wearable active sensors. This paper investigates the data collected by this application during its deployment at three major conferences, where it was used by more than 400 people. Our analyses show the robustness of the patterns of contacts at various conferences, and the influence of various personal properties (e.g. seniority, conference attendance) on social networking patterns

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

RFID Context Data Management: The Missing Link to EPCIS-Based Supply Chain Monitoring

The potential of Radio Frequency Identification (RFID) for increasing supply chain efficiency has been stressed by practitioners and researchers alike. For the cross-company exchange of RFID-related data, the industry consortium EPCglobal has specified the EPC Information Services (EPCIS). According to EPCglobal, all RFID-related data recorded by an organization should be stored as EPCIS events in a dedicated database. The information that can be inferred from the EPCIS events stored in the EPCIS repositories will be sufficient to monitor the flow of goods. However, generating an EPCIS event does not only require the data that is provided by the RFID readers but also the corresponding context data (e.g. physical locations, related business process steps and related transactions). For this missing link, i.e. the association of read events and context data, we propose an architectural component called Event Capturing Application (ECA). In this paper we propose a data model for storing and exchanging EPCIS context data in an efficient and standardizable way. We also present an algorithm that can be used to assemble EPCIS events from read events and context data. The functionality of our prototypical ECA was evaluated using noFilis’ RFID middleware CrossTalk and the Fosstrack EPCIS implementation

spChains: A Declarative Framework for Data Stream Processing in Pervasive Applications

Pervasive applications rely on increasingly complex streams of sensor data continuously captured from the physical world. Such data is crucial to enable applications to ``understand'' the current context and to infer the right actions to perform, be they fully automatic or involving some user decisions. However, the continuous nature of such streams, the relatively high throughput at which data is generated and the number of sensors usually deployed in the environment, make direct data handling practically unfeasible. Data not only needs to be cleaned, but it must also be filtered and aggregated to relieve higher level algorithms from near real-time handling of such massive data flows. We propose here a stream-processing framework (spChains), based upon state-of-the-art stream processing engines, which enables declarative and modular composition of stream processing chains built atop of a set of extensible stream processing blocks. While stream processing blocks are delivered as a standard, yet extensible, library of application-independent processing elements, chains can be defined by the pervasive application engineering team. We demonstrate the flexibility and effectiveness of the spChains framework on two real-world applications in the energy management and in the industrial plant management domains, by evaluating them on a prototype implementation based on the Esper stream processo