On developing open mobile fault tolerant agent systems

The paper introduces the CAMA (Context-Aware Mobile Agents) framework intended for developing large-scale mobile applications using the agent paradigm. CAMA provides a powerful set of abstractions, a supporting middleware and an adaptation layer allowing developers to address the main characteristics of the mobile applications: openness, asynchronous and anonymous communication, fault tolerance, and device mobility. It ensures recursive system structuring using location, scope, agent, and role abstractions. CAMA supports system fault tolerance through exception handling and structured agent coordination within nested scopes. The applicability of the framework is demonstrated using an ambient lecture scenario - the first part of an ongoing work on a series of ambient campus applications. This scenario is developed starting from a thorough definition of the traceable requirements including the fault tolerance requirements. This is followed by the design phase at which the CAMA abstractions are applied. At the implementation phase, the CAMA middleware services are used through a provided API. This work is part of the FP6 IST RODIN project on Rigorous Open Development Environment for Complex Systems

Rigorous Development of Ambient Campus Applications that can Recover from Errors

In this paper, we discuss a new method for developing fault-tolerant ambient applications. It supports stepwise rigorous development producing a well structured design and resulting in disciplined integration of error recovery measures into the resulting implementation

On using the CAMA framework for developing open mobile fault tolerant agent systems

The paper introduces the Cama (Context-Aware Mobile Agents) framework intended for developing large-scale mobile applications using the agent paradigm. Cama provides a powerful set of abstractions, a supporting middleware and an adaptation layer allowing developers to address the main characteristics of the mobile applications: openness, asynchronous and anonymous communication, fault tolerance, device mobility. It ensures recursive system structuring using location, scope, agent and role abstractions. Cama supports system fault tolerance through exception handling and structured agent coordination. The applicability of the framework is demonstrated using an ambient lecture scenario - the first part of an ongoing work on a series of ambient campus applications

06121 Abstracts Collection -- Atomicity: A Unifying Concept in Computer Science

From 19.03.06 to 24.03.06, the Dagstuhl Seminar 06121 ``Atomicity: A Unifying Concept in Computer Science\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

A Generic Framework for the Engineering of Self-Adaptive and Self-Organising Systems

This paper provides a unifying view for the engineering of self-adaptive (SA) and self-organising (SO) systems. We first identify requirements for designing and building trustworthy self-adaptive and self-organising systems. Second, we propose a generic framework combining design-time and run-time features, which permit the definition and analysis at design-time of mechanisms that both ensure and constrain the run-time behaviour of an SA or SO system, thereby providing some assurance of its self-* capabilities. We show how this framework applies to both an SA and an SO system, and discuss several current proof-of-concept studies on the enabling technologies

On rigorous design and implementation of fault tolerant ambient systems

Developing fault tolerant ambient systems requires many challenging factors to be considered due to the nature of such systems, which tend to contain a lot of mobile elements that change their behaviour depending on the surrounding environment, as well as the possibility of their disconnection and re-connection. It is therefore necessary to construct the critical parts of fault tolerant ambient systems in a rigorous manner. This can be achieved by deploying formal approach at the design stage, coupled with sound framework and support at the implementation stage. In this paper, we briefly describe a middleware that we developed to provide system structuring through the concepts of roles, agents, locations and scopes, making it easier for the developers to achieve fault tolerance. We then outline our experience in developing an ambient lecture system using the combination of formal approach and our middleware

Recruiting from the network: discovering Twitter users who can help combat Zika epidemics

Tropical diseases like \textit{Chikungunya} and \textit{Zika} have come to prominence in recent years as the cause of serious, long-lasting, population-wide health problems. In large countries like Brasil, traditional disease prevention programs led by health authorities have not been particularly effective. We explore the hypothesis that monitoring and analysis of social media content streams may effectively complement such efforts. Specifically, we aim to identify selected members of the public who are likely to be sensitive to virus combat initiatives that are organised in local communities. Focusing on Twitter and on the topic of Zika, our approach involves (i) training a classifier to select topic-relevant tweets from the Twitter feed, and (ii) discovering the top users who are actively posting relevant content about the topic. We may then recommend these users as the prime candidates for direct engagement within their community. In this short paper we describe our analytical approach and prototype architecture, discuss the challenges of dealing with noisy and sparse signal, and present encouraging preliminary results

Dynamically Partitioning Workflow over Federated Clouds For Optimising the Monetary Cost and Handling Run-Time Failures

Several real-world problems in domain of healthcare, large scale scientific simulations, and manufacturing are organised as workflow applications. Efficiently managing workflow applications on the Cloud computing data-centres is challenging due to the following problems: (i) they need to perform computation over sensitive data (e.g. Healthcare workflows) hence leading to additional security and legal risks especially considering public cloud environments and (ii) the dynamism of the cloud environment can lead to several run-time problems such as data loss and abnormal termination of workflow task due to failures of computing, storage, and network services. To tackle above challenges, this paper proposes a novel workflow management framework call DoFCF (Deploy on Federated Cloud Framework) that can dynamically partition scientific workflows across federated cloud (public/private) data-centres for minimising the financial cost, adhering to security requirements, while gracefully handling run-time failures. The framework is validated in cloud simulation tool (CloudSim) as well as in a realistic workflow-based cloud platform (e-Science Central). The results showed that our approach is practical and is successful in meeting users security requirements and reduces overall cost, and dynamically adapts to the run-time failures

Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling

Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twitter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed the complexity of drastic seasonal changes on Twitter content across multiple epidemic outbreaks. In order to address this gap, this paper contrasts two complementary approaches to detecting Twitter content that is relevant for Dengue outbreak detection, namely supervised classification and unsupervised clustering using topic modelling. Each approach has benefits and shortcomings. Our classifier achieves a prediction accuracy of about 80\% based on a small training set of about 1,000 instances, but the need for manual annotation makes it hard to track seasonal changes in the nature of the epidemics, such as the emergence of new types of virus in certain geographical locations. In contrast, LDA-based topic modelling scales well, generating cohesive and well-separated clusters from larger samples. While clusters can be easily re-generated following changes in epidemics, however, this approach makes it hard to clearly segregate relevant tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano, Switzerlan