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Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.
Music streaming platforms offer music listeners an overwhelming choice of music. Therefore, users of streaming platforms need the support of music recommendation systems to find music that suits their personal taste. Currently, a new class of recommender systems based on knowledge graph embeddings promises to improve the quality of recommendations, in particular to provide diverse and novel recommendations. This paper investigates how knowledge graph embeddings can improve music recommendations. First, it is shown how a collaborative knowledge graph can be derived from open music data sources. Based on this knowledge graph, the music recommender system EARS (knowledge graph Embedding-based Artist Recommender System) is presented in detail, with particular emphasis on recommendation diversity and explainability. Finally, a comprehensive evaluation with real-world data is conducted, comparing of different embeddings and investigating the influence of different types of knowledge.
Nowadays, problems related with solid waste management become a challenge for most countries due to the rising generation of waste, related environmental issues, and associated costs of produced wastes. Effective waste management systems at different geographic levels require accurate forecasting of future waste generation. In this work, we investigate how open-access data, such as provided from the Organisation for Economic Co-operation and Development (OECD), can be used for the analysis of waste data. The main idea of this study is finding the links between socioeconomic and demographic variables that determine the amounts of types of solid wastes produced by countries. This would make it possible to accurately predict at the country level the waste production and determine the requirements for the development of effective waste management strategies. In particular, we use several machine learning data regression (Support Vector, Gradient Boosting, and Random Forest) and clustering models (k-means) to respectively predict waste production for OECD countries along years and also to perform clustering among these countries according to similar characteristics. The main contributions of our work are: (1) waste analysis at the OECD country-level to compare and cluster countries according to similar waste features predicted; (2) the detection of most relevant features for prediction models; and (3) the comparison between several regression models with respect to accuracy in predictions. Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), respectively, are used as indices of the efficiency of the developed models. Our experiments have shown that some data pre-processings on the OECD data are an essential stage required in the analysis; that Random Forest Regressor (RFR) produced the best prediction results over the dataset; and that these results are highly influenced by the quality of available socio-economic data. In particular, the RFR model exhibited the highest accuracy in predictions for most waste types. For example, for “municipal” waste, it produced, respectively, R2 = 1 and MAPE = 4.31 global error values for the test set; and for “household” waste, it, respectively, produced R2 = 1 and MAPE = 3.03. Our results indicate that the considered models (and specially RFR) all are effective in predicting the amount of produced wastes derived from input data for the considered countries.
Complex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel batinspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.
Nowadays, most recommender systems are based on a centralized architecture, which can cause crucial issues in terms of trust, privacy, dependability, and costs. In this paper, we propose a decentralized and distributed MANET-based (Mobile Ad-hoc NETwork) recommender system for open facilities. The system is based on mobile devices that collect sensor data about users locations to derive implicit ratings that are used for collaborative filtering recommendations. The mechanisms of deriving ratings and propagating them in a MANET network are discussed in detail. Finally, extensive experiments demonstrate the suitability of the approach in terms of different performance metrics.
In this paper, we consider the route coordination problem in emergency evacuation of large smart buildings. The building evacuation time is crucial in saving lives in emergency situations caused by imminent natural or man-made threats and disasters. Conventional approaches to evacuation route coordination are static and predefined. They rely on evacuation plans present only at a limited number of building locations and possibly a trained evacuation personnel to resolve unexpected contingencies. Smart buildings today are equipped with sensory infrastructure that can be used for an autonomous situation-aware evacuation guidance optimized in real time. A system providing such a guidance can help in avoiding additional evacuation casualties due to the flaws of the conventional evacuation approaches. Such a system should be robust and scalable to dynamically adapt to the number of evacuees and the size and safety conditions of a building. In this respect, we propose a distributed route recommender architecture for situation-aware evacuation guidance in smart buildings and describe its key modules in detail. We give an example of its functioning dynamics on a use case.
M2M (machine-to-machine) systems use various communication technologies for automatically monitoring and controlling machines. In M2M systems, each machine emits a continuous stream of data records, which must be analyzed in real-time. Intelligent M2M systems should be able to diagnose their actual states and to trigger appropriate actions as soon as critical situations occur. In this paper, we show how complex event processing (CEP) can be used as the key technology for intelligent M2M systems. We provide an event-driven architecture that is adapted to the M2M domain. In particular, we define different models for the M2M domain, M2M machine states and M2M events. Furthermore, we present a general reference architecture defining the main stages of processing machine data. To prove the usefulness of our approach, we consider two real-world examples ‘solar power plants’ and ‘printers’, which show how easily the general architecture can be extended to concrete M2M scenarios.
In service-oriented architectures the management of services is a crucial task during all stages of IT operations. Based on a case study performed for a group of finance companies the different aspects of service management are presented. First, the paper discusses how services must be described for management purposes. In particular, a special emphasis is placed on the integration of legacy/non web services. Secondly, the service lifecycle that underlies service management is presented. Especially, the relation to SOA governance and an appropriate tool support by registry repositories is outlined.
Decision support systems for traffic management systems have to cope with a high volume of events continuously generated by sensors. Conventional software architectures do not explicitly target the efficient processing of continuous event streams. Recently, event-driven architectures (EDA) have been proposed as a new paradigm for event-based applications. In this paper we propose a reference architecture for event-driven traffic management systems, which enables the analysis and processing of complex event streams in real-time and is therefore well-suited for decision support in sensor-based traffic control sys- tems. We will illustrate our approach in the domain of road traffic management. In particular, we will report on the redesign of an intelligent transportation management system (ITMS) prototype for the high-capacity road network in Bilbao, Spain.
In this article, we present the software architecture of a new generation of advisory systems using Intelligent Agent and Semantic Web technologies. Multi-agent systems provide a well-suited paradigm to implement negotiation processes in a consultancy situation. Software agents act as clients and advisors, using their knowledge to assist human users. In the presented architecture, the domain knowledge is modeled semantically by means of XML-based ontology languages such as OWL. Using an inference engine, the agents reason, based on their knowledge to make decisions or proposals. The agent knowledge consists of different types of data: on the one hand, private data, which has to be protected against unauthorized access; and on the other hand, publicly accessible knowledge spread over different Web sites. As in a real consultancy, an agent only reveals sensitive private data, if they are indispensable for finding a solution. In addition, depending on the actual consultancy situation, each agent dynamically expands its knowledge base by accessing OWL knowledge sources from the Internet. Due to the standardization of OWL, knowledge models easily can be shared and accessed via the Internet. The usefulness of our approach is proved by the implementation of an advisory system in the Semantic E-learning Agent (SEA) project, whose objective is to develop virtual student advisers that render support to university students in order to successfully organize and perform their studies.