1,721,141 research outputs found
Generation of game contents by social media analysis and MAS planning
No full textIn the age of pervasive computing and social networks, it has become commonplace to retrieve opinions about digital contents in games. In the case of multi-player, open world gaming, in fact even in “old-school” single players games, it is evident the need for adding new features in a game depending on users comments and needs. However this is a challenging task that usually requires considerable design and programming efforts, and more and more patches to games, with the inevitable consequence of loosing interest in the game by players over years. This is particularly a hard problem for all games that do not intend to be designed as interactive novels. Process Content Generation (PCG) of new contents could be a solution to this problem, but usually such techniques are used to design new maps or graphical contents. Here we propose a novel PCG technique able to introduce new contents in games by means of new story-lines and quests. We introduce new intelligent agents and events in the world: their attitudes and behaviors will promote new actions in the game, leading to the involvement of players in new gaming content. The whole methodology is driven by Social Media Analysis contents about the game, and by the use of formal planning techniques based on Multi-Agents models
Enabling IoT stream management in multi-cloud environment by orchestration
No full textEvery-Day lives are becoming increasingly instrumented by electronic devices and any kind of computer-based (distributed) service. As a result, organizations need to analyse an enormous amounts of data in order to increase their incomings or to improve their services. Anyway, setting-up a private infrastructure to execute analytics over Big Data is still expensive. The exploitation of Cloud infrastructure in IoT Stream management is appealing because of costs reductions and potentiality of storage, network and computing resources. The Cloud can consistently reduce the cost of analysis of data from different sources, opening analytics to big storages in a multi-cloud environment. Anyway, creating and executing this kind of service is very complex since different resources have to be provisioned and coordinated depending on users' needs. Orchestration is a solution to this problem, but it requires proper languages and methodologies for automatic composition and execution. In this work we propose a methodology for composition of services used for analyses of different IoT Stream and, in general, Big Data sources: In particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment
Multi-level orchestration of cloud services in orcs
No full textOrchestration is a well known topic in old web services literature. Anyway, what Orchestration means for Cloud services is not yet Clear. Services, especially at lower layers of Cloud Architecture, are complex: Scientific literature has focused on the problem of dealing with proper, efficient and even optimal allocation of Resources when deploying and delivering Cloud services. Hence, management of Resources is what is commonly addressed as Orchestration in the Cloud. Anyway, the increasing complexity of Cloud Architecture and the introduction of new paradigms like Internet of Things, introduced the problem of creating Value Added Services by composition, not only of Resources, but of Services too. In this work we describe an architectural solution for Orchestration at all Cloud Layers. The framework we propose (Orchestrator for Complex Services: OrCS) manages composition of services and resources in order to create composite service based on Cloud Design Patterns. It is based on a Workflow language for description of composition and it enables verification of composite services by means of Model Driven Engineering techniques, providing a precious and easy-to-use tool for Cloud Engineering
Cloud orchestration with orcs and openstack
No full textDuring the past recent years there is an increasing interests in Cloud Services Orchestration. Efficient and even optimal allocation of Cloud resources is one of the main problems on which the scientific and development community has focused their effort. Some proposals for standards and middleware are now available for Cloud users and designers. However, the need for advancing on composition techniques is still requiring major efforts due to the new features, namely, composition of services at any layer of Cloud architecture, not only orchestration of resources. To that end, there have been proposed some Cloud patterns in order to describe composition of services. In a real setting, the composition is really complex and challenging, leading to Orchestration of Cloud Service, whose aim is to deal with both pattern-based composition and resource orchestration. In this paper, we show how the framework Orchestrator for Complex Services (OrCS) enables the use of pattern-based composition and resource orchestration. We also discuss its integration with the OpenStack Orchestrator (Heat)
Semantic Analysis of Social Data Streams
No full textSocial Networks Analysis has become a common trend among scholars and researchers worldwide. A great number of companies, institutions and organisations are interested in social networks data mining. Information published on many social networks, like Facebook, Twitter or Instagram constitute an important asset in many application fields, overall sentiment analysis, but also economics analysis, politics analysis and so on. Social networks analysis comprehends many disciplines and involves the application of different methodologies and techniques to define the criteria for generating the analytics, according to the purpose of the study. In this work, we focused on the semantic analysis of the content of textual information obtained from social media, aiming at extracting hot topics from social networks. We considered, as case study, reviews from the Yelp social network. The same methodology can be also applied for social and political opinion mining campaigns
Data as a Service (DaaS) for sharing and processing of large data collections in the cloud
No full textData as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains. © 2013 IEEE
Smart intrusion detection with expert systems
No full textNowadays security concerns of computing devices are growing significantly. This is due to ever increasing number of devices connected to the network. In this context, optimising the performance of intrusion detection systems (IDS) is a key research issue to meet demanding requirements on security of complex and large scale networks. Within the IDS systems, attack classification plays an important role. In this work we propose and evaluate the use the generalizing power of neural networks to classify attacks. More precisely, we use multilayer perceptron (MLP) with the back-propagation algorithm and the sigmoidal activation function. The proposed attack classification system is validated and its performance studied through a subset of the DARPA dataset, known as KDD99, which is a public dataset labelled for an IDS and previously processed. We analysed the results corresponding to different configurations, by varying the number of hidden layers and the number of training epochs to obtain a low number of false results. We observed that it is required a large number of training epochs and that by using the entire data set consisting of 31 features the best classification is carried out for the type of Denial-Of-Service and Probe attacks
Simulation, modeling, and performance evaluation tools for cloud applications
No full textAs cloud computing adoption and deployment increase, the performance evaluation of the cloud environments is becoming very important. Cloud applications have different composition, configuration, and deployment requirements. Simulation and modeling techniques are suitable to quantify the performance of resource allocation policies and application scheduling algorithms in Cloud computing environments for different application and service models according to different work loads, energy performance and system size. In this paper, we give an overview of the existing distributed systems simulation and modeling tools in order to outline the main characteristics and peculiarities. We then present an outlook on new requirements to be addressed for performance evaluation of cloud applications through simulation and modeling
Model-based water quality assurance in ground and surface provisioning systems
No full textThe optimal management of water resources is a key problem for the sustainable exploitation of ground and surface water sources. One of the key issues is the assurance of quality of the provided water with respect to the presence of pollutant substances or micro biotic. This paper defines an automatic approach to evaluate the vulnerability of a provisioning network with respect the detection of a threat. By means of the creation of a high level model of a network and the automatic generation of proper formal models, it is possible to detect: (1) the effect of such threat on all the network nodes and (2) the most probable location of the contamination source
Multi-agent collaborative planning in smart environments
No full textNowadays Smart systems have become commonplace in our lives: domotics, social networks, automotive, smart application, virtual reality are having each time more and more users. One recent example of smart spaces can be found in domains like cultural heritages sites, museums or libraries where the use of new technologies grows up fast, namely, distributed sensors networks, virtual reality and smart systems are now being widely used to aid in preserving archaeological findings and sites as well as to enhancing presentation of cultural heritage assets. This work focuses on a problem that face the visitors at all large museums and ruins: the problem of scheduling tours depending on users preferences and on the time they can reserve for their visit. In particular, in museums and sites with a large number of visitors, the problem of queues to access to particular areas is also well-known. Sometimes waiting times are so high that visitors are not able to end their tours in time. In this work we present a modeling methodology and a planning technique able to redirect visitors tours in order to optimize their experiences within the desired available time. In addition, the system is able to face security and safety problems, providing a mean to redirect users to safe areas in case of emergency problems
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