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Hard disk drive failures are one of the most common causes of service downtime in data centers. Predictive maintenance techniques have been adopted to extend the Remaining Useful Life (RUL) of these drives, and minimize service shortage and data loss. Several approaches based on machine and deep learning techniques have been proposed to address these issues, mostly exploiting models based on Self-Monitoring analysis and Reporting Technology (SMART) attributes. While these models have proven to be reliable, their performance is affected by the lack of information about the proximity of disk failure in time. Moreover, many of these techniques are sensitive to the highly unbalanced nature of existing data-sets, in terms of good to failed hard disk ratio. In this paper, we propose a LSTM based model combining SMART attributes and temporal analysis for estimating a hard drive health status according to its time to failure. Our approach outperforms state-of-the-art methods when evaluated on two data-sets, one containing hourly samples from 23,395 disks and the other reporting daily samples from 29,878 disks. Experimental results showed that our approach is well suited to data-sets with different sampling periods, being able to predict hard drive health status up to 45 days before failure.

Social Networks represent a cornerstone of our daily life, where the so-called social reviewing systems (SRSs) play a key role in our daily lives and are used to access data typically in the form of reviews. Due to their importance, social networks must be trustworthy and secure, so that their shared information can be used by the people without any concerns, and must be protected against possible attacks and misuses. One of the most critical attacks against the reputation system is represented by mendacious reviews. As this kind of attacks can be conducted by legitimate users of the network, a particularly powerful solution is to exploit trust management, by assigning a trust degree to users, so that people can weigh the gathered data based on such trust degrees. Trust management within the context of SRSs is particularly challenging, as determining incorrect behaviors is subjective and hard to be fully automatized. Several attempts in the current literature have been proposed; however, such an issue is still far from been completely resolved. In this study, we propose a solution against mendacious reviews that combines fuzzy logic and the theory of evidence by modeling trust management as a multicriteria multiexpert decision making and exploiting the novel concept of time-dependent and content-dependent crown consensus. We empirically proved that our approach outperforms the main related works approaches, also in dealing with sockpuppet attacks.

In this paper we describe a novel algorithm based on Game Theory for Community Detection in Online Social Networks. Extending several Game Theoretic approaches that are well established in the literature, we propose a novel algorithm that outperforms the previous ones in terms of computational complexity and efficiency of results, as proved by a number of experiments on standard data sets.

Recommender systems are increasingly playing an important role in our life, enabling users to find “what they need” within large data collections and supporting a variety of applications, from e-commerce to e-tourism. In this paper, we present a Big Data architecture supporting typical cultural heritage applications. On the top of querying, browsing, and analyzing cultural contents coming from distributed and heterogeneous repositories, we propose a novel user-centered recommendation strategy for cultural items suggestion. Despite centralizing the processing operations within the cloud, the vision of edge intelligence has been exploited by having a mobile app ( Smart Search Museum ) to perform semantic searches and machine-learning-based inference so as to be capable of suggesting museums, together with other items of interest, to users when they are visiting a city, exploiting jointly recommendation techniques and edge artificial intelligence facilities. Experimental results on accuracy and user satisfaction show the goodness of the proposed application.

When considering popular On-line Social Networks (OSN) containing heterogeneous multimedia data sources, the complexity of the underlying processing systems becomes challenging, and requires to implement application-specific but still comprehensive benchmarking. The variety of big data architectures (and of their possible realization) for both batch and streaming processing in a huge number of application domains, makes the benchmarking of these systems critical for both academic and industrial communities. In this work, we evaluate the performance of two state-of-art big data architectures, namely Lambda and Kappa, considering OSN data analysis as reference task. In more details, we have implemented and deployed an influence analysis algorithm on the Microsoft Azure public cloud platform to investigate the impact of a number of factors on the performance obtained by cloud users. These factors comprise the type of the implemented architecture, the volume of the data to analyze, the size of the cluster of nodes realizing the architectures and their characteristics, the deployment costs, as well as the quality of the output when the analysis is subjected to strict temporal deadlines. Experimental campaigns have been carried out on the Yahoo Flickr Creative Commons 100 Million (YFCC100M). Reported results and discussions show that Lambda outperforms Kappa architecture for the class of problems investigated. Providing a variety of analyses – e.g., also investigating the impact of dataset size, scaling, cost – this paper provides useful insights on the performance of these state-of-art big data architectures that are helpful to both experts and newcomers interested in deploying big data architectures leveraging cloud platforms.

Nowadays, recommender systems have become essential to users for finding “what they need” within large collections of items. Meanwhile, recent studies have demonstrated as user personality can effectively provide a more valuable information to significantly improve recommenders’ performance, especially considering behavioral data captured from social network logs. In this work, we describe a novel music recommendation technique based on the identification of personality traits, moods, and emotions of a single user, starting from solid psychological observations recognized by the analysis of user behavior within a social environment. In particular, users’ personality and mood have been embedded within a content-based filtering approach to obtain more accurate and dynamic results. Several experiments are then reported to show effectiveness of user personality and mood recognition recommendation, thus, encouraging research in this direction.

With Artificial Intelligence becoming part of a rapidly increasing number of industrial applications, more and more requirements about their transparency and trustworthiness are being demanded to AI systems, especially in military, medical and financial domains, where decisions have a huge impact on lives. In this paper, we propose a novel model-agnostic Explainable AI (XAI) technique, named Cluster-aided Space Transformation for Local Explanation (CASTLE), able to provide rule-based explanations based on both the local and global model’s workings, i.e. its detailed ”knowledge” in the neighborhood of the target instance and its general knowledge on the training dataset, respectively. The framework has been evaluated on six datasets in terms of temporal efficiency, cluster quality and model significance. Eventually, we asked 36 users to evaluate the explainability of the framework, getting as result an increase of interpretability of 6 % with respect to another state-of-the-art technique, named Anchors.

With the recent COVID-19 outbreak, we have assisted to the development of new epidemic models or the application of existing methodologies to predict the virus spread and to analyze how the different lock-down strategies can effectively influence the epidemic diffusion. In this paper, we propose a novel machine learning based framework able to estimate the parameters of any epidemiological model, such as contact rates and recovery rates, based on static and dynamic features of places. In particular, we model mobility data through a graph series whose spatial and temporal features are investigated by combining Graph Convolutional Neural Networks (GCNs) and Long short-term memories (LSTMs) in order to infer the parameters of SIR and SIRD models. We evaluate the proposed approach using data related to the COVID-19 dynamics in Italy and we compare the forecasts of the trained model with available data about the epidemic spread.

The widespread use of Online Social Networks has also involved the scientific field in which researchers interact each other by publishing or citing a given paper. The huge amount of information about scientific research documents has been described through the term Big Scholarly Data. In this paper we propose a framework, namely Discovery Information using COmmunity detection (DICO), for identifying overlapped communities of authors from Big Scholarly Data by modeling authors' interactions through a novel graph-based data model combining jointly document metadata with semantic information. In particular, DICO presents three distinctive characteristics:i) the co-authorship network has been built from publication records using a novel approach for estimating relationships weight between users;ii) a new community detection algorithm based on Node Location Analysis has been developed to identify overlapped communities;iii) some built-in queries are provided to browse the generated network, though any graph-traversal query can be implemented through the Cypher query language. The experimental evaluation has been carried out to evaluate the efficacy of the proposed community detection algorithm on benchmark networks.Finally, DICO has been tested on a real-world Big Scholarly Dataset to show its usefulness working on the DBLP+AMiner dataset, that contains 1.7M+ distinct authors, 3M+ papers, handling 25M+ citation relationships.

The exponential growth of connected devices (i.e. laptops, smartphones or tablets) has radically changed communications means, also making it faster and impersonal by using On-line Social Networks and Instant messaging through several apps. In this paper we discuss about the cyberbullying problem, focusing on the analysis of the state-of-the-art approaches that can be classified in four different tasks (Binary Classification, Role Identification, Severity Score Computation and Incident prediction). In particular, the first task aims to predict if a particular action is aggressive or not based on the analysis of different features. In turn, the second and the third task investigate the cyberbullying problem by identifying users’ role in the exchanged message or assigning a severity score to a given users or session respectively. Nevertheless, information heterogeneity, due to different multimedia contents (i.e. text, emojis, stickers or gifs), and the use of datasets, which are typically unlabeled or manually labelled, create continuous challenges in addressing the cyberbullying problem.

In this paper we challenge the issue of detecting anomalous events in computer systems log files, through a novel graph mining approach. The basic idea is to model log temporal sequences as a particular graph and event detection as a particular path finding problem. Thus, anomalous sequences correspond to log parts that can not be “explained” by any path in the graph. We propose a novel Iterative Partitioning Log Mining technique to parse any kind of logs and to model their temporal sequence as a probabilistic penalty graph. The approach has been implemented in a framework supporting both real time and batch processing realized on the top of the Apache Spark analytics engine for large-scale data processing. Experimental results show the advantages of the proposed framework in terms of effectiveness for different system configurations.

Social media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system – designed for big data applications – is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem.

Past research has shown that over 8 months may elapse between the time a network is compromised and the time the attack is discovered. During this long gap, attackers can steal valuable intellectual property from the victim. The recent FORGE system suggests that generating fake-but-believable-versions of documents can delay the attacker, cost him money, and increase his uncertainty. However, FORGE only modifies the textual component of the document. But in the real world, documents consist of many non-textual components such as charts, equations, formulas, diagrams, and tables. We propose the concept of a Probabilistic Logic Graph (PLG) and show that PLGs provide a single, unified framework within which the different parts of a document can be expressed. We then define the problem of generating, for a given PLG representation of a document, a set of fake yet highly believable PLGs (i.e., documents), so that an attacker looking at them (the original and the fake ones) cannot easily identify the original document. We show that the problem of generating fake PLGs is intractable-but we propose an approximation algorithm solving it efficiently. We evaluate the use of PLGs over a corpus of patents and show our fakes can effectively deceive an adversary.

Researchers and companies are making great efforts to create new ways of interaction with a increasing number and types of electronic devices, with particular attention to the conversational interface, whether spoken or written. The cultural heritage domain can bring many benefits from the great effort in this field, as a smart conversational system can provide both room/hotel/apartment information and information related to the area, natural or cultural points of interest, tourist routes, social events, history etc. The paper’s main goal is to present a quick and effective service to the tourists visiting a city for the first time. Learning the cultural preferences of the users can enhance customer satisfaction, since a smart system can propose them customised tours in order to reach point of interests according to the expressed preferences. The interaction with the user is simplified by a conversational interface (chatbot). © Springer Nature Switzerland AG 2021.

In this paper we propose an entertainment Chatbot based on the sequence to sequence model according to the Enconder-Decoder framework based on GRU cells for supporting user's cultural heritage path. A preliminary evaluation about the efficiency of the proposed approach has been performed asking to 10 users to interact with the Chatbot on cultural items of Campania Region.

The uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features [10]. In this paper, we present our framework for fake news detection and we discuss in detail an approach based on deep learning that we implemented by using Google Bert features. Our experiments conducted on two well-known and widely used real-world datasets suggest that our method can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.

The uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features [11]. In this paper, we present our framework for fake news detection and we discuss in detail a solution based on deep learning methodologies we implemented by leveraging Google Bert features. Our experiments conducted on two well-known and widely used real-world datasets suggest that our method can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.

Nowadays, the continuous technological advances allow designing novel Integrated Vehicle Health Management (IVHM) systems to deal with strict safety regulations in the automotive field with the aim at improving efficiency and reliability of automotive components. However, challenging issue, which arises in this domain, is handling a huge amount of data that are useful for prognostic. To this aim, in this paper we propose a cloud-based infrastructure, namely Automotive predicTOr Maintenance In Cloud (ATOMIC), for prognostic analysis that leverages Big Data technologies and mathematical models of both nominal and faulty behaviour of the automotive components to estimate on-line the End-Of-Life (EOL) and Remaining Useful Life (EUL) indicators for the automotive systems under investigation. A case study based on the Delphi DFG1596 fuel pump has been presented to evaluate the proposed prognostic method. Finally, we perform a benchmark analysis of the deployment configurations of ATOMIC architecture in terms of scalability and cost.

The technology improvement has radically changed how tourists perform their journey offering new services to enhance their cultural experience and to easily retrieve required information. In this paper we propose a novel framework, that models intangible and tangible cultural objects into a unified data model, for supporting tourists journey. In particular, a Micro-service architecture has been designed to provide several services whose tourists can access through a conversational agent based on the Seq2Seq model. Furthermore, we propose also an Enterprise Service Bus to ingest events automatically from promotional website or manually from public and/or private organization. We have evaluated the proposed framework according to the following two experiments: (i) the efficiency and efficacy of the Chatbot engine, showing that the use of GRU cells allows to obtain better results in terms of loss and accuracy with respect to LSTM one and (ii) the efficacy of the proposed framework according to the NASA-TLX asking to 50 users to interact with the Chatbot, demonstrating that the proposed approach outperforms the state-of-the art ones combining natural processing language, user profile information and historical activities.

In modern Online Social Networks (OSNs), the need to detect users’ communities based on their interests and social connections has became a more and more important challenge in literature. Community Detection supports and make more effective and efficient several Social Network Analysis (SNA) applications: the diffusion of a new idea or technologies can be maximized by identifying of people group interested about a given topic, the recommendation suggestion can be improved taking in account also how the social ties can be influenced the user chooses and the behaviors of people in the same communities, expert finding tasks could be more accurate if users are earlier subdivided into thematic groups, and so on. This paper presents a survey that provides a comprehensive and comparative study of all the different community detection techniques applicable to the various models proposed for OSNs. In particular, the most diffused approaches based on game theory, artificial intelligence and fuzzy strategies are detailed and compared, highlighting the related pros and cons. In addition, the problem of their applicability on the different OSN models is discussed, focusing on complex networks. Finally, the main open issues and challenges for the community detection problem are reported to address the futures work concerning this topic.

In this paper we describe a novel algorithm based on Game Theory for Community Detection in Social Networks. Extending several Game Theoretic approaches that are well established in the literature, we propose a novel algorithm that outperforms the previous ones in terms of computational complexity and efficiency of results, as proved by a number of experiments on standard data sets. © 2019 IEEE.

Community Detection in On Line Social Networks is a classic feature in networked systems, from the fields of biology, economics, politics and computer science, as well. This paper describes a novel Community Detection method based on a deep learning approach, facing the challenging problems related to the dimensions of the involved data structures, and proposing a novel convolutional technique particularly useful for sparse matrices. Several experiments have been reported and discussed in real scenarios.

In this paper, we propose a novel data model for Multimedia Social Networks, i.e. particular social media networks that combine information on users belonging to one or more social communities together with the content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and represent in a simple way all the different kinds of relationships that are typical of social media networks, and in particular among users and multimedia content. We also introduce some user and multimedia ranking functions to enable different applications. Finally, some experiments concerning effectiveness of the approach for supporting relevant information retrieval activities are reported and discussed.

Nowadays, the tremendous usage of multimedia data within Online Social Networks (OSNs) has led the born of a new generation of OSNs, called Multimedia Social Networks (MSNs). They represent particular social media networks – particularly interesting for Social Network Analysis (SNA) applications – that combine information on users, belonging to one or more social communities, together with all the multimedia contents that can be generated and used in the related environments. In this work, we present a novel expert finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several preliminary experiments on Last.fm show the effectiveness of the proposed approach, encouraging the future work in this direction.

From the Social Networks Analysis (SNA) perspective, Viral Marketing has the aim to maximize the number of people that become aware of a given product/service by identifying a few number of individuals, considered more "influential" that can be promoted products or services. In this paper we propose a novel concept of influence graph that can be easily derived by querying social network modelled as a graph. Furthermore, spread diffusion model has been defined as a Combinatorial Multi-Armed Bandit (CMAB) problem for retrieving the most influential users, without any kind of preliminary knowledge.

Nowadays, every facet of people’s lifestyle is impacted by the continuous use of technologies and in particular the Internet. Social interactions have been radically changed since new technologies have removed communication barriers. The obstacles of time and space have been overcome, letting people from different places and cultures communicate. People tend to become part of a dense social network, where the distribution of information becomes an almost immediate process. This is the reason why companies, public institutions and business activities have opted for social networks as communication medium. As a consequence, people seem to incorporate information gained from the social networks into their decision-making processes.

Online social network (OSN) is quickly becoming a promising field of interest for big data analytics and for a number of real applications, ranging from marketing to user profiling and recommendation. Anyway, although OSNs are naturally formed by heterogeneous data, in the recent past, only a few works have considered an explicit use of multimedia in their models. In this paper, we explicitly take into account the intrinsic characteristics of multimedia, having the awareness that in this way, both the models and the analysis algorithms will enormously benefit from such kind of information. In particular, we describe a multimedia data model for OSN, in order to provide, in a unique framework, novel mechanisms for effective management of multimedia information supporting several classic applications, such as influence analysis and maximization. Experiments have been carried out on Flickr Creative Commons Y FCC100M dataset containing about 100 million images, showing that the proposed approach combines both time and efficacy performances.

Credit risk assessment plays a key role for correctly supporting financial institutes in defining their bank policies and commercial strategies. Over the last decade, the emerging of social lending platforms has disrupted traditional services for credit risk assessment. Through these platforms, lenders and borrowers can easily interact among them without any involvement of financial institutes. In particular, they support borrowers in the fundraising process, enabling the participation of any number and size of lenders. However, the lack of lenders’ experience and missing or uncertain information about borrower’s credit history can increase risks in social lending platforms, requiring an accurate credit risk scoring. To overcome such issues, the credit risk assessment problem of financial operations is usually modeled as a binary problem on the basis of debt’s repayment and proper machine learning techniques can be consequently exploited. In this paper, we propose a benchmarking study of some of the most used credit risk scoring models to predict if a loan will be repaid in a P2P platform. We deal with a class imbalance problem and leverage several classifiers among the most used in the literature, which are based on different sampling techniques. A real social lending platform (Lending Club) data-set, composed by 877,956 samples, has been used to perform the experimental analysis considering different evaluation metrics (i.e. AUC, Sensitivity, Specificity), also comparing the obtained outcomes with respect to the state-of-the-art approaches. Finally, the three best approaches have also been evaluated in terms of their explainability by means of different eXplainable Artificial Intelligence (XAI) tools.

In this paper the authors define a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and to represent in a simple way all the different kinds of relationships that are typical of social networks and multimedia sharing systems, and in particular between multimedia contents, among users and multimedia content and among users themselves. Different applications (e.g. influence analysis, multimedia recommendation) can be then built on the top of the introduce data model thanks to the introduction of proper user and multimedia ranking functions. In addition, the authors provide a strategy for hypergraph learning from social data. Some preliminary experiments concerning efficiency and effectiveness of the proposed approach for analysis of Last.fm network are reported and discussed.

Social media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system – designed for big data applications – is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem.

The paper aims at proposing an original summarization technique from Online Social Networks (OSNs) for multimedia stories’ creation. In particular, for each Multimedia Social Network (MuSN) – i.e. an OSN focusing on the management and sharing of multimedia information – we leverage a graph-based modeling approach and exploit influence analysis methodologies to detect the most important multimedia objects related to one or more topics of interest. Consecutively, from the list of candidate objects we obtain a multimedia summary on the basis of a novel summarization model and some heuristics, whose purpose is to generate multimedia stories with priority (w.r.t. some user keywords), continuity, variety and not receptiveness features. The effectiveness of the proposed approach is shown by the performed experiments on Flickr.

Nowadays, Social Network Analysis is considered a hot research topic. In this paper, we propose a novel data model for Multimedia Social Networks, i.e. particular social media networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and represent in a simple way all the different kinds of relationships that are typical of social media networks, and in particular among multimedia contents, users and multimedia content and users themselves.

In this work, we propose a novel multimedia summarization technique from Online Social Networks (OSNs). In particular, we model each Multimedia Social Network (MSN)—i.e. an OSN focusing on the management and sharing of multimedia information—using an hypergraph based approach and exploit influence analysis methodologies to determine the most important multimedia objects with respect to one or more topics of interest. Successively, we obtain from the list of candidate objects a multimedia summary using a summarization model together with an heuristics that aims to generate summaries with priority (with respect to some user keywords), continuity, variety and not receptiveness features. The performed experiments on Flickr shows the effectiveness of proposed approach.

Despite the great amount of research done in the Online Social Networks (OSNs) field, only few works have investigated the use of multimedia data in such realm. Instead, it is the authors’ opinion that a novel data model that takes into account the intrinsic characteristics of multimedia may be of great help in managing Multimedia OSNs for providing more effective algorithms. In this paper, we describe a novel OSN data model that supports easy management of multimedia content in a unique framework, providing a more effective and efficient mechanism for data and information management in a variety of applications, especially for Influence Analysis aims.

In this paper, we propose and describe a novel recommender system for big data applications that provides recommendations on the base of the interactions among users and generated multimedia contents in one or more social media networks, leveraging a collaborative and user-centered approach. Preliminary experiments using data of several online social networks show how our approach obtains very promising results.

In the last few years, the diffusion of the Internet of Things (IoT) architectures is at the basis of the explosive growth of data and information useful to build adaptive, intelligent, autonomous and non-intrusively systems. In this work, we explore the Cultural Heritage domain and propose an innovative methodology for adaptive and context-aware system design, presenting a semantic model of intelligent environments and rule-based application behavior.

In this paper, we describe a novel data model for online social networks based on hypergraphs. We show how an influence analysis problem can be properly faced leveraging the introduced network structure. In particular, we implemented a bio-inspired maximization algorithm on the top of the hypergraph model, exploiting the concept of influential path. Preliminary experiments using data of several social networks show how our approach obtains very promising results and encourage the research in this direction.

In this paper we present a novel user-centered recommendation approach for multimedia art collections. In particular, preferences (usually coded in the shape of items’ metadata), opinions (textual comments to which it is possible to associate a particular sentiment), behavior (in the majority of cases logs of past items’ observations and actions made by users in the environment), and feedbacks (usually expressed in the form of ratings) are considered and integrated together with items’ features and context information within a general and unique recommendation framework that can support an intelligent browsing of any multimedia repository. Preliminary experiments show the utility of the proposed strategy to perform different browsing tasks.

Social Network Analysis has been introduced to study the properties of Online Social Networks for a wide range of real life applications. In this paper, we propose a novel methodology for solving the Influence Maximization problem, i.e. the problem of finding a small subset of actors in a social network that could maximize the spread of influence. In particular, we define a novel influence diffusion model that, learning recurrent user behaviours from past logs, estimates the probability that a given user can influence the other ones, basically exploiting user to content actions. A greedy maximization algorithm is then adopted to determine the final set of influentials in the network. Preliminary experimental results shows the goodness of the proposed approach, especially in terms of efficiency, and encourage future research in such direction.

In this paper, inspired by hypergraph-based approaches, we propose a novel data model for social media networks: it allows to represent in a simple way all the different kinds of relationships that are typical of these environments (among multimedia contents, among users and multimedia content and among users themselves) and to enable several kinds of analytics and applications. From the other hand, we have tested several influence maximization algorithms leveraging the introduced network structure in order to show the advantages to consider also “user-to-multimedia” relationships (in addition to the “user-to-user” ones) in the influence analysis problem. Preliminary experiments using data of several social media networks shows how our approach obtains very promising results.

One of the most important research area that during the last decade has taken advantage by the multimedia technologies is certainly the Cultural Heritage Information Management. In this paper, we present KIRA (Knowledge-based Information Retrieval from Art collections), a system to query, browse and analyze cultural digital contents from a set of distributed and heterogeneous multimedia repositories. KIRA provides all the necessary retrieval and presentation functionalities to search information of interest and present it to the users in a suitable format and according to their needs. By means of a set of ad-hoc APIs, our system can also support several applications: mobile multimedia guides for cultural environments, web portals to promote the cultural heritage of a given organization, multimedia recommender and storytelling systems and so on. We describe the main ideas that support the system, showing its use for several applications.

In this work, we propose a novel data model that integrates and combines information on users belonging to one or more heterogeneous Online Social Networks (OSNs), together with the content that is generated, shared and used within the related environments, using an hypergraph-based approach. Then, we discuss how the most diffused centrality measures – that have been defined over the introduced model – can be efficiently applied for a number of data privacy issues, such as lurkers detection, especially in “interest-based” social networks. Some preliminary experiments using the Yelp dataset are finally presented.

Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach&rsquo, s effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on.

In this paper we present a preliminary work concerning the definition of a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within a related environment. The proposed model is based on the hypergraph structure and allows us to represent in a simple way all the different kinds of relationships that are typical of a social network, in particular between multimedia content, between users and multimedia content and between users themselves, at the same time supporting several kinds of applications by means of the introduction of several ranking functions. In addition, we provide a strategy for mapping the proposed model into an object relational data model, for efficiently storing the related data. We also describe a first prototype that can store and integrate information from different OSNs (Facebook, Twitter) and multimedia sharing systems (Flickr, Lastfm), providing some facilities for browsing the entire hypergraph.

In the last decade, the use of Online Social Networks (OSNs) has been rapidly growing allowing people, that lives in different places, to make friends and to share, comment and observe different types of multimedia content, producing a large amount of data showing Big Data features, mainly due to their high change rate, their large volume and intrinsic heterogeneity. In this paper we present a preliminary work concerning the definition of a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within a related environment. The proposed model is based on the hypergraph structure and allows us to represent in a simple way all the different kinds of relationships that are typical of a social network, in particular between multimedia content, between users and multimedia content and between users themselves, at the same time supporting several kinds of applications by means of the introduction of several ranking functions. We provide a strategy for mapping the proposed model into an object relational data model, for efficiently storing the related data. In addition, we report an application of the proposed model in a case of study, declined in the biomedical domain, in which the model is exploited for analyzing the spread of a given topic on a social network.

Summary Nowadays, social networks provide users an interactive platform to create and share heterogeneous content for a lot of different purposes (eg, to comment events and facts, and express and share personal opinions on specific topics), allowing millions of individuals to create online profiles and share personal information with vast networks known and sometimes also unknown people. Knowledge about users, content, and relationships in a social network may be used for an adversary attack of some victims easily. Although a number of works have been done for data privacy preservation on relational data, they cannot be applied in social networks and in general for big data analytics. In this paper, we first propose a novel data model that integrates and combines information on users belonging to 1 or more heterogeneous online social networks, together with the content that is generated, shared, and used within the related environments, using an hypergraph data structure; then we implemented the most diffused centrality measures and also introduced a new centrality measure—based on the concept of “neighborhood” among users—that may be efficiently applied for a number of data privacy issues, such as lurkers and neighborhood attack prevention, especially in “interest-based” social networks. Some experiments using the Yelp dataset are discussed.

In this paper, we present a general framework for retrieving relevant information from news papers that exploits a novel summarization algorithm based on a deep semantic analysis of texts. In particular, we extract from each Web document a set of triples (subject, predicate, object) that are then used to build a summary through an unsupervised clustering algorithm exploiting the notion of semantic similarity. Finally, we leverage the centroids of clusters to determine the most significant summary sentences using some heuristics. Several experiments are carried out using the standard DUC methodology and ROUGE software and show how the proposed method outperforms several summarizer systems in terms of recall and readability.