Advancements in Large Language Models (LLMs) allow solving many challenging tasks related to software security in an automatic manner, e.g., the generation of test cases. An important aspect concerns the deobfuscation of source code, especially for improving its readability or preventing the elusion of signature-based countermeasures. Although LLMs are increasingly deployed to reveal the presence of malicious payloads within obfuscated software components, a comprehensive understanding of their potential and limitations is still missing. In this work, we evaluate the effectiveness of deobfuscating JavaScript code through an LLM-based pipeline. In more detail, we investigate whether LLMs can preserve structural properties of the software, especially to enhance the identification of weaknesses. Compared to two standard tools (i.e., JSNice and js-deobfuscator ), our approach provides a more readable JavaScript prose according to several metrics, while retaining information on the Common Weaknesses Enumeration plaguing the software. To support the process of explaining issues within code, we performed tests on the use of two general-purpose LLMs, i.e., ChatGPT and Google Gemini. Results indicate that advancing the security of JavaScript through LLMs requires facing several challenges, which can be largely addressed via ad-hoc models.

Abstract Misinformation propagation in online networks involves multifaceted interactions between users, contents, and engagement mechanisms (likes, shares, comments). Addressing this issue entails both understanding how information spreads and identifying influential users driving the dissemination process. To tackle these challenges, this paper proposes a framework based on a Graph Attention Network model, applied to a heterogeneous graph representing social interactions and context-aware dynamics. Targeting the binary classification of real vs fake news, it offers insights into both propagation patterns and influential users in the dissemination process. A core contribution is the adoption of two post-hoc mechanisms for uncovering such users: uncertainty-based Active learning-like and GNN-Explainer. A detailed comparative analysis reveals that nodes where the model exhibits the highest confidence often lack rich content information; nevertheless, combining both high-confidence and content-rich nodes grasps complementary aspects and better aligns with influential users in information propagation. The framework is benchmarked against traditional centrality measures, widely used to identify influential users in social networks. A comparative evaluation on two heterogeneous, real-world, social networks confirms that the proposed method both achieves compelling accuracy in finding influential nodes and shows a potential to scale-up to densely-connected graphs on which classic approaches may fail.

Social media have become a key tool for rapidly spreading information worldwide, amplifying the risks of misinformation and fake news. This is also intensified by the fact that fake news covers a wide range of topics across multiple domains. Machine learning, particularly language models, offers a promising solution for detecting fake news. However, a major limitation of existing methods is their inability to classify instances from new or unseen domains. To tackle this issue, we introduce MERMAID, a mixture of experts approach that leverages the knowledge from different specialized models to classify examples from unknown domains. Each expert is initially trained on a specific known domain and then fine-tuned using data from other known domains. A model merging procedure is then applied to combine related experts, reducing the number of models required for predicting instances from unknown domains. In addition, our approach can effectively be used in few-shot learning scenarios, where a small amount of data from the target/unknown domain is available during training. Experiments on five benchmark datasets demonstrate the effectiveness of our method in both zero-shot and few-shot learning settings.

Abstract The convergence of social media data and mobility patterns presents a unique opportunity to delve deeper into societal behavior and its implications on epidemic dynamics. Social media platforms have become veritable repositories of real-time, user-generated data, reflecting public sentiments, discussions, and perceptions regarding health concerns, including infectious diseases. Concurrently, mobility patterns, derived from transportation usage, geolocation services, and movement data, offer insights into population movements and travel behaviors critical for understanding disease spread. This paper proposes an approach exploiting social media data and mobility patterns to perform epidemic predictions, whose experimental evaluation has been carried out on COVID-19 pandemic data. Leveraging these diverse data sources, we aim to uncover synergies and correlations between online discussions and travel behaviors that can contribute to more accurate and proactive epidemic forecasts. Our central focus lies in discerning the alignment between peaks in social media discussions and corresponding fluctuations in mobility patterns. By identifying and analyzing these alignments, our aim is to clarify their potential as predictive indicators for upcoming epidemic trends. Results obtained from real-world datasets about the city of Chicago (USA) demonstrate the efficacy of the proposed method in predicting the spread of the epidemic accurately. The explainability analysis reveals a significant correlation between tweet content and actual COVID-19 data, affirming Twitter’s credibility as a dependable indicator of epidemic spread. This underscores the growing importance of social media user-generated data as a valuable resource for monitoring and comprehending epidemic outbreaks.

Alzheimer’s disease (AD) is characterized by complex alterations in brain connectivity, making the understanding of these patterns critical for early diagnosis and intervention. This study presents a motif-based analysis of functional brain connectivity utilizing synthetic adjacency matrices of virtual connectomics derived from Alzheimer’s Disease Neuroimaging Initiative (ADNI) data. Our approach involves first sparsifying the functional brain network to enhance the significance of the connections, followed by the construction of a directed network from bidirectional correlation-based edge weights between ROIs (Regions of Interest) to accurately capture the flow of information. By systematically searching for recurrent motifs within these networks, we aim to identify specific patterns of connectivity that may distinguish AD patients from healthy controls. Motifs are fundamental building blocks that provide insights into the functional organization of the brain and can reveal underlying mechanisms of neurodegeneration. This analysis not only contributes to the characterization of brain network alterations in AD but also demonstrates the utility of synthetic connectomes in neuroimaging research. Our findings have implications for developing network-based biomarkers and enhancing our understanding of the pathophysiology of Alzheimer’s disease, highlighting the importance of integrating advanced network analysis techniques to unravel the complexities of brain connectivity in neurodegenerative conditions.

In this paper, we focus on network robustness in complex networks by identifying those links within a network graph G whose attack/removal would cause a severe network damage. More specifically, we investigate the role of the effective resistance matrix in identifying an order of links more vulnerable to attacks. In our previous works, we have both evaluated a strategy of link removals based on the ranking provided by the Hadamard product matrix between the adjacency matrix of G and the effective resistance matrix, and a strategy of node removals based on the ranking of the diagonal elements of the pseudoinverse of the Laplacian matrix associated to G. Now, through a real-world networking scenario of an Internet backbone, we start considering the line graph L(G) of G (i.e. the dual graph in which the links of G are nodes). Then, we investigate if removing nodes in the line graph is the same as removing links in G. The relation between the Laplacian of the line graph and the graph itself is not obvious, which does not allow us to immediately map the performance of a node removal strategy to the performance of a link removal strategy. Carrying out our analysis on Erdős-Rényi, Watts-Strogatz and Bárabasi-Albert networks, we look for a relation, if existing, between (a) the node removal in the line graph of G and (b) the link removal in G. Results show that the two attack strategies show a notable degree of similarity mostly on the Bárabasi-Albert networks.

The COVID-19 pandemic generated extensive and far-reaching discussions on social media platforms, effectively becoming a primary source for people to access and disseminate information regarding the outbreak. These social media conversations possess the potential to shape public opinions, but they also carry the risk of spreading panic and misinformation during crises like the COVID-19 pandemic.

Social media become the main tool for spreading news, discussing ideas and comments on world events. Accordingly, social media represents a precious source to extract insight into public opinion and sentiment. In particular, Twitter has been already recognized as an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. Since the very first days of COVID-19 outbreak, people exchanged news, updates, sentiment and opinion about the pandemics. The aim of the study reported in this paper is to explore how social media has been exploited to fight COVID-19. In particular, the attention is given on analyzing engagement and interest in the COVID-19 topics and their evolution on a global scale, identifying infodemics, also analysing people feelings and reactions.

Social media has become a key tool for spreading the news, discussing ideas and comments on world events, playing a relevant role also in public health management, especially in epidemics surveillance like seasonal flu. Online social media actually can provide an important help in monitoring disease spreading as users self-report their health-related issues. Since the very first days of COVID-19 outbreak, people exchanged news, updates, sentiment and opinion about the pandemics. The paper describes a study aiming at evaluating the correlation of tweets with official COVID-19 data. Based on the outcomes of the correlation study, the paper proposes a forecasting model to predict the number of new daily COVID-19 cases. The approach is formulated as an autoregressive model that combines tweets and official COVID-19 data. A real-word dataset of tweets is used for the correlation study and to evaluate the performance of the forecasting model. Results shown the feasibility of the approach, highlighting the improvement obtained when tweets are integrated in the forecasting model, allowing to predict new COVID-19 cases in advance, on average 4–6 days before they were confirmed.

This article presents a detailed analysis of Twitter data to inspect how information about the COVID-19 epidemics spread in US. To this purpose, the objectives are to identify the key terms and features used in the tweets, the interest in the COVID-19 topics, together with the evolution of the discussion all over US. To identify topics, the paper proposes an approach that combines peak detection and clustering techniques. Space-time features are extracted from the tweets and modeled as time series. After that, peaks are detected from the time series, and peaks of textual features are clustered based on the co-occurrence in the tweets. Each cluster obtained is then associated to a topic. Results, performed over a real-world dataset of tweets related to COVID-19 in US, show that the proposed approach is able to accurately detect several relevant topics of interest, of varying importance and character, including health status, symptoms, and pandemics implications on people living. A case study about the correlation of Twitter data with COVID-19 confirmed cases has been presented, also evaluating the feasibility of exploiting Twitter for the outbreak diffusion prediction. Results highlight a high correlation between tweets and real COVID-19 data, proving that Twitter can be considered a reliable indicator of the epidemic spreading and that data generated by user activity on social media is becoming an invaluable source for capturing and understanding epidemics outbreaks.

Medical staff can be considerably supported in patient healthcare delivery thanks to the adoption of machine learning and deep learning methods by enhancing clinicians decisions and analysis with targeted clinical knowledge, patient information, and other health data. This paper proposes a learning methodology that, on the basis of the current patient health status, clinical history, diagnostic and laboratory results, provides insights for clinicians in the diagnosis and therapy decision processes. The approach relies on the concept that patients with similar vital signs patterns are, in all probability, affected by the same or very similar health problems. Thus, they can have the same or very similar diagnoses. Patients physiological signals are modeled as time series and the similarity among them is exploited. The method is formulated as a classification problem in which an ad-hoc multi-label k-nearest neighbor approach is combined with similarity concepts based on word embedding. Experimental results on real-world clinical data have shown that the proposed approach allows detecting diagnoses with a precision up to about 75%.

Artificial Intelligence (AI), since the onset of the COVID-19 pandemic at the beginning of the last year, is playing an important role in supporting physicians and health authorities in different difficult tasks such as virus spreading, patient diagnosing and monitoring, contact tracing. In this paper, we provide an overview of the methods based on AI technologies proposed for COVID-19 forecasting. Summary statistics of the techniques adopted by researchers, categorized on the base of the underlying AI sub-area, are reported, along with publication venue of papers. The effectiveness of these approaches is investigated and their capabilities or weaknesses in providing reliable predictions are discussed. Future challenges are finally analyzed and research directions for improving current tools are suggested.

Facilitate clinicians in their complex decision- making processes allows to improve patient outcomes along disease-specific pathways and healthcare delivery. This could be realized by enhancing medical decisions with targeted clinical knowledge, patient information, and other health data. Nowadays, a key paradigm in healthcare, designed to be a direct aid to clinical-decision making, is the Clinical Decision Support System (CDSS). Along this line, the work presented in the paper focuses on detecting patients diagnosis, proposing a learning methodology that, on the basis of the current patient status, clinical history, diagnostic and results from their pathological reports, provides insights for clinicians in the diagnosis and therapy processes. The patients physiological signals have been modeled as time series and the similarity among them has been exploited. The main idea is that patients with similar patterns of vital signs are affected by the same or similar health problems and, therefore, may have the same or very close diagnoses. The diagnosis detection method is formulated as a classification problem, combining time series similarity and an ad-hoc multi- label k-nearest neighbor approach (ML-KNN). The proposed classifier exploits the semantic similarity of diagnoses catched through sentence embedding. Results, performed over a real- world clinical dataset, show that the proposed approach is able to successfully detect diagnoses with a precision up to about 75%.

COVID-19 pandemic is affecting the lives of the citizens worldwide. Epidemiologists, policy makers and clinicians need to understand public concerns and sentiment to make informed decisions and adopt preventive and corrective measures to avoid critical situations. In the last few years, social media become a tool for spreading the news, discussing ideas and comments on world events. In this context, social media plays a key role since represents one of the main source to extract insight into public opinion and sentiment. In particular, Twitter has been already recognized as an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. However, it is a challenging issue identifying interesting and useful content from large and noisy text-streams. The study proposed in the paper aims to extract insight from Twitter by detecting the most discussed topics regarding COVID-19. The proposed approach combines peak detection and clustering techniques. Tweets features are first modeled as time series. After that, peaks are detected from the time series, and peaks of textual features are clustered based on the co-occurrence in the tweets. Results, performed over real-world datasets of tweets related to COVID-19 in US, show that the proposed approach is able to accurately detect several relevant topics of interest, spanning from health status and symptoms, to government policy, economic crisis, COVID-19-related updates, prevention, vaccines and treatments.

The fast expansion during the recent years of online social networks, such as Twitter, Facebook, or Foursquare, is making available an enormous and continuous stream of user-generated contents including information on human mobility within urban context. In particular, online social networks allows for the collection of geo-tagged data obtained through the GPS readings of phones through which users have the possibility to tag posts, photos and videos with geographical coordinates. In this context, recommending the future position of a mobile object is key for the implementations of several applications aiming at improving mobility within urban areas. The paper proposes a location recommendation approach that exploits geo-tagged data on social networks. The approach integrates user preference, sequential mobility and geographic constraints. The recommendation task is formulated as a similarity problem among the visiting and mobility profiles of users, accounting the mobility sequentiality in the patterns. Two ranking metrics are introduced to predict places the user could like. The metrics are then combined into an overall recommendation ranking function. The candidate locations are then ranked according to the two similarity measures. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending unseen locations, outperforming representative state-of-the-art approaches.

Smart Health indicates the use of new technologies in the healthcare sector. Literally it means “intelligent health”, and the intelligence referred to is digital, guaranteed by innovative tools such as Internet of Things (IoT) devices, communication technologies, cloud computing, artificial intelligence (AI) and big data. Thanks to sensors and devices connected to patients, such as technologically advanced bracelets and watches, it is possible to collect data on the state of health of people and treat them, even remotely, anticipating critical situations before they occur. The use of IoT to support healthcare leads to suitable recommendations and set the best policies for improving the quality of patients life, assisting practitioners and healthcare providers in decision making, collecting and exchanging information, helping to prevent events, such as a heart attack or an illness. This is possible thanks to the use of AI, which process immense amounts of data to anticipate future events. In recent years, AI based on deep learning has sparked tremendous global interest and is impacting also in healthcare. Deep learning has been widely adopted in image recognition, speech recognition and natural language processing. It could be the vehicle for translating big biomedical data into improved human health. The paper presents a literature review conducted to determine the most important technologies, methodologies, algorithms and models for smart health systems. In addition, the main application areas and challenges of smart health were explored.

Internet of Things (IoT) based smart health and wellness systems are increasingly gaining popularity for the next generation of medical services. Systems of medical devices that communicate seamlessly integrated and securely can improve patient outcomes, reduce medical errors, lower costs and overcome the existing medical systems limitations. While these innovative medical systems are emerging, they also bring new challenges as data protection and power management. This paper proposes an IoT architecture that allows to collect and analyze huge volumes of heterogeneous clinical data in order to monitor health status of patients and improve clinical decision support, by tackling the energy shortage challenge. The key feature of the proposed approach is a power-aware strategy based on energy load balancing in order to keep on the greatest number of power operated devices involved in the computational tasks. The paper presents a case study of power-aware smart health monitoring exploiting different types of data mining techniques. The experimental results show the effectiveness of the approach able to achieve energy savings by means of the proposed power-aware strategy.

The aim of the paper is to provide a novel location recommendation system exploiting user preference, social relationships and geographic constraints in social media. User preference and social ties are learned from the visited location history also accounting venues proximity to previous check-ins. A framework integrating sequential mobility and user preference is proposed. The framework formulates the recommendation task as a similarity problem among the visiting and mobility profiles of users, accounting the mobility sequentiality in the patterns. Two ranking metrics are introduced to predict places the user could like. The metrics are then combined into an overall recommendation ranking function. The candidate locations are then ranked according to the two similarity measures. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending unseen locations, outperforming representative state-of-the-art approaches.

On line social networks (e.g., Facebook, Twitter) allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of tweets manifest the spatial-temporal movements of people in real life. The paper presents an approach to recommend travel routes to social media users exploiting historic mobility data, social features of users and geographic characteristics of locations. Travel routes recommendation is formulated as a ranking problem aiming at minimg the top interesting locations and travel sequences among them, and exploit such information to recommend the most suitable travel routes to a target user.

Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. A Clinical Decision Support System (CDS) can greatly help in identifying diseases and methods of treatment. In this paper we propose a CDS framework that can integrate heterogeneous health data from different sources, such as laboratory test results, basic information of patients, and health records. Using the electronic health medical data so collected, innovative machine learning and deep learning approaches are employed to implement a set of services to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients health issues more efficiently.

Analysis of people trajectories is key for implementing effective urban computing applications. Nowadays, social media represent one of the main sources of information concerning human dynamics within urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to predict human mobility by exploiting Twitter data. The prediction method is based on a hybrid approach combining frequent pattern mining, trajectory similarity and supervised classification. The trajectory pattern similarity allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. If none of the patterns satisfies the similarity threshold, a set of spatio-temporal features characterizing locations and movements among them are combined into a supervised learning approach based on M5 model trees. The experimental results obtained by using a real-world dataset show that the proposed method is effective in predicting the user's next places achieving a remarkable accuracy and prediction rate.

Social media represent one of the main sources of information concerning human dynamics within an urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to recommend new unseen locations to social media users exploiting historic mobility data, social features of users and geographic characteristics of locations. The location recommendation problem is formulated as a ranking task so that the recommended locations to be visited will be ranked at the highest position in the prediction set. A ranking function that exploits users' similarity in visiting locations and in travelling along mobility paths is used to predict places the user could like. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending unseen locations achieving remarkable precision and recall rates.

Social media are playing an increasingly important role in reporting major events happening in the world. However, detecting events and topics of interest from social media is a challenging task due to the huge magnitude of the data and the complex semantics of the language being processed. The paper proposes an online algorithm to discover topics that incrementally groups short text by incorporating the textual content with latent feature vector representations of words appearing in the text, trained on very large corpora to improve the check-in topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, the approach obtains significant improvements with respect to classical topic detection methods.

Social media, in recent years, have become an invaluable source of information for both public and private organizations to enhance the comprehension of people interests and the onset of new events. Twitter, especially, allows a fast spread of news and events happening real time that can contribute to situation awareness during emergency situations, but also to understand trending topics of a period. The article proposes an online algorithm that incrementally groups tweet streams into clusters. The approach summarizes the examined tweets into the cluster centroid by maintaining a number of textual and temporal features that allow the method to effectively discover groups of interest on particular themes. Experiments on messages posted by users addressing different issues, and a comparison with state-of-the-art approaches show that the method is capable to detect discussions regarding topics of interest, but also to distinguish bursty events revealed by a sudden spreading of attention on messages published by users.

Improving seasonal influenza forecasting combining official data sources with web search and social media is a recent research topic which can enhance situational awareness of healthcare organizations when monitoring the outbreak of seasonal flu. In this paper, a prediction model based on autoregression that combines data coming from official influenza surveillance system, with data from web search and social media regarding influenza is proposed. The model is evaluated on the two influenza seasons 2016-2017 and 2017-2018, restricted to Italy. The results show that by using Web-based social data, like Google search queries and tweets, we can obtain accurate weekly influenza predictions up to four weeks in advance. The proposed approach improves real-time influenza forecast compared to traditional surveillance systems based on data from sentinel doctors: the prediction error is reduced up to 47%, while the Pearson's correlation is improved of about 24%.

Influenza surveillance through social media data is becoming an important research topic because it could enhance the capabilities of official surveillance systems in monitoring the outbreak of seasonal flu, by providing healthcare organization with improved situational awareness. In this paper, the two influenza seasons 2016-2017 and 2017-2018, restricted to Italy, are investigated by analyzing the tweets posted by users regarding influenza-like illness. Two types of analysis are performed. The first studies the correlation between the tweets containing the most frequent flu related words with the data provided by the Italian InfluNet surveillance system. The second one examines the sentiment of people on the medicines used to heal flu. We show that there is a strict correlation between the reports published on the InfluNet system, and the contents posted by Twitter users about their symptoms and health state. Moreover, we found that the sentiment expressed by people regarding the treatment, in terms of medicines, taken to heal seems rather negative.

The widespread use of location-based social networks is making such social media one of the major sources of information about people activities and costumes within urban context, allowing to capture and enhance the comprehension of people behaviour, including human mobility regularities. In that sense, the present work describes a novel approach to predict human mobility by using Twitter data. The approach predict the future location of an individual based on her recent mobility history (like individuals typical mobility routines) and on global mobility in the considered geographic area (e.g., mobility routines of all the Twitter users). The prediction approach is based on a novel trajectory pattern similarity measure that allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. If none of the patterns satisfies the similarity threshold, a set of spatio-temporal features characterizing locations and movements among them are combined in a supervised learning approach based on decision trees. The experimental evaluation, performed on a real-world dataset of tweets posted in London, shows the effectiveness and efficiency of the approach in predicting the user's next places, achieving a remarkable accuracy and precision.

On line social networks (e.g., Facebook, Twitter) allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of tweets manifest the spatial-temporal movements of people in real life. This paper aims to analyze such movements to predict the next location of an individual based on the observations of his mobility behavior over some period of time and the recent locations that he has visited. To this end, we defined a prediction methodology based on a set of spatio-temporal features characterizing locations and movements among them. We then combined the features in a supervised learning approach based on M5 model trees. The experimental results obtained by using a real-world dataset show that the supervised method is effective in predicting the users next places achieving a remarkable accuracy.

Social networking services like Twitter and Instagram are a valuable sources of information to find out what happened or what is happening in a geographic area. This paper presents a method to catch and understand relevant events and happenings from social geo-tagged data. The proposed method consists in two main phases: (i) extraction of space-time features from social data and their modelization as time series, (ii) peak detection from time series, for identifying deviation from user normal behavior. Results of the experimental evaluation, performed over a real-word dataset of tweets, show that the proposed approach is able to accurately detect several relevant events, bounded to a geographic location and of varying importance and character, like exhibitions, festivals, competitions, and terrorist attacks such as that done at the Charlie Hebdo offices.We achieve a space accuracy up to 90%, and a time accuracy up to 95%.

Summary The continuous advances in wireless networking and mobile computing technologies have paved the way to the spreading of new classes of distributed applications running on networks of small devices such as smartphones and tablets. An issue that still prevents a wider implementation of distributed applications in wireless networks is the lack of task allocation strategies addressing both the energy constraints of small devices and the decentralized nature of wireless networks. In this paper, we focus on this twofold issue by proposing an energy‐aware scheduling strategy for allocating computational tasks over a wireless network of small devices in a decentralized but effective way. The main design principle of our scheduling strategy is finding a task allocation that prolongs the total lifetime of the network and maximizes the number of alive devices by balancing the energy load among them. A simulation analysis has been performed to assess the performance of the proposed strategy in different network and application scenarios. The results show that by using the proposed energy‐aware task allocation approach, the network lifetime is extended and the number of alive devices is significantly higher compared with alternative scheduling strategies while meeting application‐level performance constraints. Copyright © 2016 John Wiley & Sons, Ltd.

The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, and sensors, allows for the collections of large amounts of movement data. This amount of data can be analyzed to extract descriptive and predictive models that can be properly exploited to improve urban life. From a technological viewpoint, Cloud computing can play an essential role by helping city administrators to quickly acquire new capabilities and reducing initial capital costs by means of a comprehensive pay-as-you-go solution. This paper presents a workflow-based parallel approach for discovering patterns and rules from trajectory data, in a Cloud-based framework. Experimental evaluation has been carried out on both real-world and synthetic trajectory data, up to one million of trajectories. The results show that, due to the high complexity and large volumes of data involved in the application scenario, the trajectory pattern mining process takes advantage from the scalable execution environment offered by a Cloud architecture in terms of both execution time, speed-up and scale-up.

Microblogs have become an important origin of information regarding events happening in a location during a time period. Analyzing and clustering these streams of short textual messages is an important research activity which is attracting the interest of both public and private organizations, since the extracted knowledge can be exploited to enhance the comprehension of people behavior and the onset of emergency situations. Clustering these streams requires efficient algorithms capable of analyzing this continuos deluge of data. The paper proposes an online algorithm that incrementally groups tweet streams into clusters. The approach summarizes the examined tweets into the cluster centroids generated so far. The assignment of a tweet to a centroid uses a similarity measure that takes into account both the cluster age and the terms occurring in the tweet. Experiments on messages posted by users in the Manhattan area show that the method is able to extract events effectively taking place in the examined period.

The pervasive availability of increasingly powerful mobile computing devices like PDAs, smartphones and wearable sensors, is widening their use in complex applications such as collaborative analysis, information sharing, and data mining in a mobile context. Energy characterization plays a critical role in determining the requirements of data-intensive applications that can be efficiently executed over mobile devices. This paper presents an experimental study of the energy consumption behaviour of representative data mining algorithms running on mobile devices. Our study reveals that, although data mining algorithms are compute- and memory-intensive, by appropriate tuning of a few parameters associated to data (e.g., data set size, number of attributes, size of produced results) those algorithms can be efficiently executed on mobile devices by saving energy and, thus, prolonging devices lifetime. Based on the outcome of this study we also proposed a machine learning approach to predict energy consumption of mobile data-intensive algorithms. Results show that a considerable accuracy is achieved when the predictor is trained with specific-algorithm features.

The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, etc., allows for the collections of large amounts of movement data. This amount of information can be analyzed to extract descriptive and predictive models that can be profitable exploited to improve urban life. This paper presents an integrated Cloud based framework for efficiently managing and analyzing socio-environmental data in the urban context of cities. As case study, we introduce a parallel approach for discovering patterns and rules from trajectory data. Experimental evaluation shows that the trajectory pattern mining process can take advantage from a scalable execution environment offered by a Cloud architecture.

In this paper we present an Energy-Aware Scheduling strategy that assigns computational tasks over a network of mobile devices optimizing the energy usage. The main design principle of our scheduler is to find a task allocation that prolongs network lifetime by balancing the energy load among the devices. We have evaluated the scheduler using a prototype of the system that includes smart phones and Android emulators. Experimental results show that significant energy savings can be achieved by using our energy-aware scheduler compared to classical time-based scheduler, while meeting the specified performance constraints.

Abstract The rise in availability of web‐based data sources has led to new challenges in data integration systems for obtaining decentralized, wide‐scale sharing of data preserving semantics. In this paper, we present a framework for integrating heterogeneous XML data sources distributed over a large‐scale, highly dynamic network of autonomous nodes. We highlight a query reformulation algorithm to combine and query‐distributed XML databases through a decentralized point‐to‐point mediation process among the different data sources by using P2P schema‐mappings. More precisely, our integration model is based on path‐to‐path mappings, using the XPath language. We demonstrate the usefulness and scalability of our ideas and algorithms with a detailed set of experiments. Finally, we discuss our experience implementing the above‐cited query reformulation algorithm as a Web service within the GDIS system, a service‐based Grid architecture. We have evaluated GDIS on several real‐world schemas with promising results. Copyright © 2010 John Wiley & Sons, Ltd.

This paper presents a technique that builds a layer of virtual sensors over a sensor network. The virtual sensors are able to infer and provide data for the physical sensors that do not work. The key assumption of our approach is that the physical quantities sensed by the sensors are related. The relations among sensors are unknown, but during a learning phase the layer of virtual sensors infers an approximation of them by means of fuzzy rules. The inferred fuzzy rules capture these relations in a simple way even when the corresponding mathematical models are complex. The set of fuzzy rules inferred for a node can be used to obtain virtual values when the real ones are not available. In order to develop our technique we improved the Tree Routing Protocol in charge to deliver data from the nodes to the base station and used Snlog, a Datalog-like language that supports the implementation of distributed algorithms for Wireless Sensor Network in a declarative way. We developed a system prototype and performed preliminary experiments that prove the validity of our approach.

DHT-based structured P2P systems have been proposed to index and retrieve many types of contents, including distributed collections of XML documents. During the query processing, a DHT can be used to efficiently identify all nodes storing relevant documents.

Service-oriented Grid frameworks offer resources and facilities to support the design and execution of distributed applications in different domains, ranging from scientific applications and public computing projects to commercial and industrial applications. A critical issue in such a context is the management of the heterogeneity of resources and services offered by a Grid, including computers, data, and software tools provided by different organizations. This paper presents a general architecture of a service-oriented information system, which exploits the characteristics of a multi-domain and semantically enriched metadata model. The main objective of the information system is to uniformly manage service-oriented applications and basic resources by assuring metadata persistence through an XML distributed database, without merely relying on the functionalities of persistent Grid services. The information system has been implemented on the basic services of the WSRF-based Globus Toolkit 4 and its performance has been evaluated in a testbed.

Data Grids provide transparent access to heterogeneous and autonomous data resources. The main contribution of this paper is the presentation of a data sharing system that (i) is tailored to data grids, (ii) supports well established and widely spread relational DBMSs, and (iii) adopts a hybrid architecture by relying on a peer model for query reformulation for retrieving semantically equivalent expressions, and on a wrapper-mediator integration model for accessing and querying distributed data sources. The system builds upon the infrastructure provided by the OGSA-DQP distributed query processor and the XMAP query reformulation algorithm. The paper discusses the implementation methodology, and also presents empirical evaluation results.

Today data sources are pervasive and their number is growing tremendously. Current tools are not prepared to exploit this unprecedented amount of information and to cope with this highly heterogeneous, autonomous and dynamic environment. In this paper, we propose a novel semantic overlay network architecture, PARIS, aimed at addressing these issues. In PARIS, the combination of decentralized semantic data integration with gossip-based (unstructured) overlay topology management and (structured) distributed hash tables provides the required level of flexibility, adaptability and scalability, and still allows to perform rich queries on a number of autonomous data sources. We describe the logical model that supports the architecture and show how its original topology is constructed. We present the usage of the system in detail, in particular, the algorithms used to let new peers join the network and to execute queries on top of it and show simulation results that assess the scalability and robustness of the architecture.