Wireless networks are increasingly targeted by sophisticated cyber threats, demanding anomaly detection systems that are both accurate and interpretable. In this paper, we present a modular and explainable anomaly detection pipeline tailored for WiFi environments. Our approach is based on the Isolation Forest algorithm, enhanced with two novel extensions: DIFFI, a depth-based feature importance metric for instance-level interpretability, and BS-iForest, a statistically-informed sampling strategy that improves model stability. To enable rigorous and repeatable experimentation, we develop a synthetic dataset generator that simulates realistic WiFi traffic with controllable anomalies, avoiding the legal and ethical challenges associated with real-world data. We benchmark our system against widely used unsupervised methods, including One-Class SVM, Local Outlier Factor, and Autoencoders. Experimental results show that our enhanced framework achieves state-of-the-art detection performance.

The state of health of workers is becoming increasingly important both for the performance of the company and for the lives of the workers. This paper focuses on the experiences of Antur SRL. This company implemented a fiveyear protocol (2020-2024) with the aim of improving the wellbeing of employees and the performance of the company. This was achieved by integrating a series of tailored nutritional strategies, postural exercises, music therapy and stress reduction techniques into the workplace. The project involved over 12, 000 employees from 40 companies across a range of industries and roles. Data was collected through bio-impedance analysis (BIA) and detailed medical and nutritional histories. A Self-Organising Map (SOM) network was then used to cluster employees and analyse health trends over time. The results showed significant improvements, including reduced absenteeism, increased productivity and improved environmental, social and governance (ESG) sustainability, therefore underlining the value of comprehensive workplace health initiatives.

Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (NetworkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security.

Abstract Predictive Process Monitoring (PPM) techniques are emerging as part of the more general research scenario of Process Mining (PM). They play a crucial role in the continuously evolving process of digital transformation by constantly supporting the organizational decision-making processes providing (accurate) predictions on the future behavior of processes. The state of the art of PPM application methodologies is mainly focused on Single ID Event Logs, commonly known as Traditional Event Logs or Classical Event Logs. As a matter of fact, the importance of Object-Centric Event Logs (OCEL) is being increasingly recognized as many emerging PPM approaches benefited of the usage of OCEL by obtaining a significative increase of the prediction accuracy. This survey aims to explore the current proposals in the context of OCEL-based PPM approaches. More in detail, we contribute to the state of the art by adding new classification features by differentiating between the approaches based on the input Event Log (Traditional or OCEL). We also analyzed the existing literature considering the prediction task addressed, the methodology used, the specific contribution area they addressed and the application domain.

In Data Science, data modeling (including relational databases and big data) has traditionally used Entity-Relationship (ER) diagrams to represent structural characteristics of data. However, ER diagrams lack the capability to capture the flow and transformation of data through analytic pipelines, which are essential to manage modern data science workflows. On the other hand, flowcharts have been used for decades to describe the processing. Based on this motivation, this paper provides a historical perspective identifying the limitations of employing ER diagrams and Data Flow Diagrams (DFDs), separately, emphasizing the need to integrate both solutions. We examine established diagram notations and design models, including traditional models such as UML, ER, DFD, BPMN, and FLOWER. Our literature analysis suggests that integrative diagram approaches can provide a more intuitive and comprehensive understanding of data collection, data integration, and data transformation for big data analytics in the future.

In this research paper, we explore the essential role of High Performance Computing (HPC) in the current techno-logical era, highlighting its extensive use in various sectors, while also considering growing alarm over its environmental footprint. High performance computing systems are essential for managing large data sets and solving complex challenges. However, their significant contribution to escalating energy consumption and carbon emissions in the information and communication technology (ICT) sector cannot be ignored. Our study identifies the increasing energy demands and environmental challenges associated with HPC activities, including resource use, electrical waste and greenhouse gas emissions. It highlights the importance of understanding these environmental impacts in detail. It also contributes to the ongoing dialogue on sustainable computing, promoting a harmonious future where technological progress and environmental sustainability can coexist in unison.

"COVID-19 has far-reaching global impacts on health, economics, society, education, politics, and the environment. While vaccines are crucial, the threat persists. Our paper introduces a sentiment-based COVID-19 vaccine recommender system using Twitter data. By applying preprocessing and leveraging a novel CT-BERT_CONVLayerFusion model within a random forest ensemble, we classify tweets into seven sentiment categories. Additionally, we perform aspect-based review categorization. Comparative analysis demonstrates that our approach surpasses state-of-the-art models, achieving superior accuracy, recall, precision, and F1-measure. This advanced method aids in combatting COVID-19 and mitigating vaccine hesitancy by offering personalized vaccine recommendations based on individual concerns."

The COVID-19 pandemic has dramatically transformed healthcare systems globally, therefore improving health information technology sector. From the moment that pandemic has broken out, the use of information and communication technologies (ICT) has become absolutely necessary for the continuation of healthcare services. Furthermore, data digitization has enabled the extraction of meaningful insights through big data analytics. E-Health, encompassing a wide range of ICTs used in healthcare, has become a critical component in addressing the challenges posed by the pandemic. The main goal of this paper is to examine the impact of COVID-19 on health information technology and explores the rapid growth of e-Health and big data-driven innovation in healthcare processes. Through the analysis of the major tools, techniques, and innovative processes that have emerged in response to the pandemic, this paper has the aim of highlight their potential to improve system efficiency and enhance citizen health. We explore the current state of e-Health and big data in healthcare and discuss the future implications of these technologies for the sector. Our analysis underscores the need for continued investments in health information technology and highlights the role of policymakers, healthcare providers, and researchers in fostering innovation and driving positive change in the sector.

In most decision-making scenarios, the decision relies upon a multiplicity of factors/criteria which need to be selected and prioritized counting on the knowledge of a group of experts, with the aim of reaching a global decision. Through judgment aggregation models we seek a rational mechanism to reconcile individual judgments into a collective prioritization of requirements. In this paper we present a fuzzy multi-criteria group decision-making model, and experiment its application in credit rating determination, applying Majority Judgment as a method to consolidate the evaluations of a bank's board of direction into a shared and rational decision on SME rating.

The current increase in cybercrime is demanding more effective and efficient data exploration and analysis solutions that can help analysts to detect cyberattacks. However, the huge amount of data generated continuously confronts a number of technological difficulties and classical algorithms must be often redesigned to be able to deal with this seemingly endless stream of information coming from past activity logs and real-time data. In this paper, we propose a novel methodology able to identify security threats in activity logs. The contribution of the paper is twofold: we propose an encoding technique, based on prime numbers, that can be used to represent in a compact way a set of activities, we then describe an outlier detection algorithm which, based on the encoded activities, is able to detect malicious behavior. The extensive experimental analysis proved the effectiveness of the proposed methodology.

The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people's sentiment for the vaccine campaign. Today, social media is the rich source of data where people share their opinions and experiences by their posts, comments or tweets. In this study, we have used the twitter data of vaccines of COVID and analysed them using methods of artificial intelligence and geo-spatial methods. We found the polarity of the tweets using the TextBlob() function and categorized them. Then, we designed the word clouds and classified the sentiments using the BERT model. We then performed the geo-coding and visualized the feature points over the world map. We found the correlation between the feature points geographically and then applied hotspot analysis and kernel density estimation to highlight the regions of positive, negative or neutral sentiments. We used precision, recall and F score to evaluate our model and compare our results with the state-of-the-art methods. The results showed that our model achieved 55% & 54% precision, 69% & 85% recall and 58% & 64% F score for positive class and negative class respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines.

Abstract Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people’s hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people’s sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731–5780, 2022. https://doi.org/10.1007/s10462-022-10144-1 ) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people’s sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM ) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people’s reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people’s feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers.

The coronavirus disease pandemic which broke out in 2019 heavily affected world wide health systems which found themselves unprepared for managing this wide-ranging event and were therefore forced to reorganize and review the entire system management. In this scenario, the use of information technology in health has played a fundamental role; in fact, e-Health, which indicates all the information and communication technologies (ICT) supporting the health system, has allowed not only the continuation of activities otherwise not executable, but has also left a strong legacy both to the health system in terms of improvements in efficiency and effectiveness and towards the health of the citizen. Covid-19 has pushed towards a rapid growth of e-Health by modifying the operation of the health system, deeply changing the vision of management processes, and favoring data digitization from which meaningful information and insights can be extracted. The aim of this article is to analyze the impact of Covid on the acceleration of the digital transformation of the healthcare system, the innovative processes, the tools used and the best adapted Big Data techniques.

Making decisions quickly and efficiently is essential in all areas of existence; when the decision concerns a fair number of voters and options to vote, it is sometimes appropriate to choose a voting system that represents the voting population well, without excluding the preferences of minorities. In this paper we present a voting system that aims to be an enhancement of Majority Judgment through unsupervised machine learning techniques, in particular the cluster and, in addition, a criterion for obtaining a multiwinner result has also been added. After exposing its functioning, a case study is presented to test its applicability, which leads to multiple fields of interest and it is not limited exclusively to purely political occasions.

The existence of big data, social media interactions, and digital globalization has changed the way people make decisions either in their life or those of collective importance. Computational Social Choice (COMSOC), as an emerged field, has tried to join various social fields (social choice theory) and technical fields (computer science, mathematics, economics and logic). In the last few decades, expert rating was used to select the winner in the contest or competition, that was, later, merged with crowd voting. However, the results of voting based on aggregation of crowd opinion was not considered satisfied. The majority judgement is a new method of election. It is the consequence of a new theory of social choice where voters judge candidates instead of ranking them. In this research, we used Eurovision song contest data of 2021 final round. Eurovision song contest is held annually, in which almost 40 countries participate. We applied majority judgement on the Eurovision song contest data and found that Italy got highest position, followed by Croatia and Australia acquiring second and third positions respectively in competition of 2021.

The rise of cyber crime observed in recent years calls for more efficient and effective data exploration and analysis tools. In this respect, the need to support advanced analytics on activity logs and real time data is driving data scientist’ interest in designing and implementing scalable cyber security solutions. However, when data science algorithms are leveraged for huge amounts of data, their fully scalable deployment faces a number of technical challenges that grow with the complexity of the algorithms involved and the task to be tackled. Thus algorithms, that were originally designed for classical scenarios, need to be redesigned in order to be effectively used for cyber security purposes. In this paper, we explore these problems and then propose a solution which has proven to be very effective in identifying malicious activities.

Coronavirus has emerged as challenge for the whole mankind causing illness worldwide. To eradicate the disease, global efforts are put increasing to develop its vaccine. In order to achieve the immunity against the virus, wide provision of vaccine is necessary. To make sure the distribution of vaccines, the sentiments of people for vaccines must be analyzed. Now-a-days, people share their thoughts, feelings and feedback about anything they experience on social media platforms. In this study, high performance approaches have been used for the analysis of the sentiments of people about vaccines. In this study, we have used the freely available data and applied pre-processing over it. We found out the polarity values of the tweets using TextBlob() function of Python and drew the wordclouds for positive, negative and neutral tweets. We used BERT model for understanding the people’s feelings and feedback about vaccines. The model evaluation was performed using precision, recall and F measure. The BERT model achieved achieved 55 % & 54 % precision, 69 % & 85 % recall and 58 % & 64 % F score for positive class and negative class respectively. Therefore, the use of artificial intelligence in social media analysis produce fruitful results while determining the people’s attitude towards ant new trend, topic and any emergency situation. These methods helps to grow the vaccines campaigns timely by solving the people’s concerns about vaccines.

Nowadays, really huge volumes of fake news are continuously posted by malicious users with fraudulent goals thus leading to very negative social effects on individuals and society and causing continuous threats to democracy, justice, and public trust. This is particularly relevant in social media platforms (e.g., Facebook, Twitter, Snapchat), due to their intrinsic uncontrolled publishing mechanisms. 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. Unfortunately, the availability of information about news propagation is limited. In this paper, we provided a benchmark framework in order to analyze and discuss the most widely used and promising machine/deep learning techniques for fake news detection, also exploiting different features combinations w.r.t. the ones proposed in the literature. Experiments conducted on well-known and widely used real-world datasets show advantages and drawbacks in terms of accuracy and efficiency for the considered approaches, even in the case of limited content information.

Today, the whole world is facing a biggest challenge in the form of coronavirus. The spread of COVID has caused health concerns worldwide. Considering this, there is an increase in the global efforts for the development of the COVID. The widespread provision of the vaccine is the major requirement in achieving the immunity against coronavirus. For this purpose, the public sentiments towards the vaccine campaign must be analysed. With the help of social media services, people are freely sharing their feelings and sentiments through posts, reviews or tweets. In this research, we have used advanced artificial intelligence methods for analysing the public sentiments towards vaccine campaigns. For this purpose, we used twitter data freely available on the Kaggle website and performed basic preprocessing steps. We used natural language processing (NLP) techniques such as TextBlob() and word cloud in order to find the polarity of the tweets to categorize them in seven different classes and find the most frequent keywords respectively. We used BERT model for sentimental analysis to understand the people’s mental state by studying their opinion and behaviour towards vaccines. Hence, the artificial intelligence based social network analysis must be considered for performing and analyzing the public sentiments towards any trending topic, pandemic or any other worldwide or local issue. Such methods can help to develop the trust of people towards vaccine campaigns timely and help to provide the proper administration of vaccines at large scale.

The present work describes the analysis conducted on the diagnoses made during the general physical examinations in the decade 2010–2020, starting from the DB of the EMR previously implemented in the University Veterinary Teaching Hospital at Federico II University of Naples. A decision tree algorithm was implemented to work out a predictive model for an effective recognition of neoplastic diseases and zoonoses for cats and dogs from Campania Region. The results achievable by data mining techniques for what concerns computer aided disease diagnosis and exploration of risk factors and their relations to diseases, show the increasing importance of Veterinary Informatics within the wider field of Biomedical and Health Informatics, and in particular its capacity to point out the existing connections between humans, animals, and surrounding environment, according to the One (Digital) Health perspective specifics.

The social media and electronic media has a vast amount of user-generated data such as people’ comment and reviews about different product, diseases, government policies etc. Sentimental analysis is the emerging field in text mining where people’s feeling and emotions are extracted using different techniques. COVID-19 has declared as pandemic and effected people’s lives all over the globe. It caused the feelings of fear, anxiety, anger, depression and many other psychological issues. In this survey paper, the sentimental analysis applications and methods which are used for COVID-19 research are briefly presented. The comparison of thirty primary studies shows that Naive Bayes and SVM are the widely used algorithms of sentimental analysis for COVID-19 research. The applications of sentimental analysis during COVID includes the analysis of people’s sentiments specially students, reopening sentiments, analysis of restaurants reviews and analysis of vaccine sentiments.

In this paper, a new architecture, named Sigma (as Sigma uppercase usually denote a sum and this architecture is a summarization of Lambda and Kappa), is proposed to provide a solution for build a complete Big Data System, interactive and scalable, using a variety of tools and techniques. The architecture have been tested in a real life scenario and the early results obtained are quite encouraging both on scalability and high performance.

The rise of cyber crime observed in recent years calls for more efficient and effective Data Exploration and analysis tools. In this respect, the need to support advanced analytics on activity logs and real time data is driving data scientist' interest in order to design and implement scalable cyber security solutions. However, when data science algorithms are leveraged for huge amount of data, their fully scalable deployment faces a number of technical challenges that grow with the complexity of the algorithms involved and the task to be tackled. Thus algorithms, that were originally designed for classical scenarios, must often be redesigned in order to be effectively used for cyber security purposes. In this paper, we explore these problems and then propose a solution which has proven to be very effective for compressing suspicious activities in smaller and larger cyber environment in order to make the intelligent analysis and simulation of threats more efficient.

The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V's).

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.

In this paper we propose an architecture specifically devoted to the analysis of huge natural language biomedical textual collections, with the purpose of searching for semantic similarity in order to obtain useful hints for effective simulation that could help physicians in diagnosis tasks. We leverage Word Embedding models trained with word2vec algorithm and a Big Data architecture for their processing and management. We performed some preliminary analyses using a dataset extracted from the whole PubMed library and we developed a web front-end to show the usability of this methodology in a real context.

In this paper we address the problem of analyzing biomedical data collection with the purpose of searching for semantic similarity among textual documents. In details, we leverage Word Embeddings models obtained by word2vec algorithm and a specific Big Data architecture for their management, defining an approach able to permit the retrieving of semantic similar texts among a huge biomedical text corpus. The proposed architecture has been developed with the purpose of improving a previous implementation, lowering the computational time and allowing in this way the use of the whole PubMed library as dataset, proving also the usability of this methodology in a real context.

Digital content piracy is nowadays really burden-some both for companies and professionals. To overcome this problem, digital data producers usually protect their documents from illegal distributions by leveraging digital fingerprinting techniques for marking each copy of their documents in order to uniquely identify it. Unfortunately, in the case that multiple malicious users jointly collaborate for illegally sharing a document copy, they may forge the embedded fingerprint by simply comparing their copies and modifying the fingerprint's bits that differ in their copies. In [1] an optimal fingerprinting schema has been defined for accusing a guilty user with very high probability under the assumption that the fingerprinting code is sufficiently long. To remove this assumption, several joint-decoding techniques have been recently proposed [2], [3]. In this work, we perform a deep experimental analysis of the Metropolis-Hastings based joint-decoding approach defined in [3], with the aim of assessing the sensitivity to various parameters of the model, thus making it usable in practical contexts.

The data posting framework introduced in [8] adapts the well-known Data Exchange techniques to the new Big Data management and analysis challenges that can be found in real world scenarios. Although it is expressive enough, it requires the ability of using count constraints and may be difficult for a non expert user. Moreover, the data posting problem is NP-complete under the data complexity in the general case, then the use of the non-deterministic variables is performed. Indeed, identifying the conditions that guarantee polynomial-time execution in the presence of non-deterministic choices is very important for practical purposes. In this paper, we present a simplified version of data posting framework, based on the use of the smart mapping rules, that integrate the simple mapping description with some parameters, avoiding the complex specifications with count constraints. We show that the data posting problem in the new setting is NP- complete and identify the conditions under which this problem becomes polynomial even in the presence of non-deterministic choices.

The need to support advanced analytics on Big Data is driving data scientist' interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization.However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a number of technical challenges that grow with the complexity of the algorithms involved.Thus algorithms, that were originally designed for a sequential nature, must often be redesigned in order to effectively use the distributed computational resources.In this paper, we explore these problems, and then propose a solution which has proven to be very effective on the complex hierarchical clustering algorithm CLUBS+.By using four stages of successive refinements, CLUBS+ delivers high-quality clusters of data grouped around their centroids, working in a totally unsupervised fashion.Experimental results confirm the accuracy and scalability of CLUBS+ on Map-Reduce platforms.

The need to support advanced analytics on Big Data is driving data scientist' interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a number of technical challenges that grow with the complexity of the algorithms involved. Thus algorithms, that were originally designed for a sequential nature, must often be redesigned in order to effectively use the distributed computational resources. In this paper, we explore these problems, and then propose a solution which has proven to be very effective on the complex hierarchical clustering algorithm CLUBS+. By using four stages of successive refinements, CLUBS+ delivers high-quality clusters of data grouped around their centroids, working in a totally unsupervised fashion. Experimental results confirm the accuracy and scalability of CLUBS+.

The need to support advanced analytics on Big Data is driving data scientist' interest toward massively parallel distributed systems and software platforms, such as Map- Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a number of technical challenges that grow with the complexity of the algorithms involved. Thus algorithms, that were originally designed for a sequential nature, must often be redesigned in order to effectively use the distributed computational resources. In this paper, we explore these problems, and then propose a solution which has proven to be very effective on the complex hierarchical clustering algorithm CLUBS+. We present a parallel version of CLUBS+ named CLUBS-P with an ad-hoc implementation based on message passing: CLUBS-MP.

Big Data rise made traditional data management techniques inadequate in many real life scenarios. In particular, the availability of huge amounts of data pertaining to user suggestions and searches calls for advanced analysis strategies in order to profitably leverage these data. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch. In this paper, we describe our proposal for analysing the way user searches and suggestions influence their social environment in order to quickly identify users able to spread their influence across the network. It is worth noting that, gathering information about user preferences is crucial in several scenarios like viral marketing, tourism promotion and food education.

Social Networks analysis is driving both research and industrial effort as the outcomes of this activity are relevant both from a merely theoretical point of view and for the potential market advantages they can provide to companies. Indeed, there is a growing number of applications that call for user (social) intervention with the aim of helping each other in solving complex tasks or rating other users work. The topic is even more intriguing when a reward is given to users that properly complete their tasks. In this paper, we focus on the analysis of user mutual rankings in a collaborative network where they contribute to the solution of complex tasks. We leverage Exponential Random Graph to model user interaction rankings and we evaluate our approach in a real life scenario.

research-article Share on WFinger: a joint-decoder for very short Tardos fingerprinting codes Authors: Bettina Fazzinga ICAR-CNR, Rende (CS), Italy ICAR-CNR, Rende (CS), ItalyView Profile , Sergio Flesca DIMES, University of Calabria, Rende (CS), Italy DIMES, University of Calabria, Rende (CS), ItalyView Profile , Filippo Furfaro DIMES, University of Calabria, Rende (CS), Italy DIMES, University of Calabria, Rende (CS), ItalyView Profile , Elio Masciari ICAR-CNR, Rende (CS), Italy ICAR-CNR, Rende (CS), ItalyView Profile Authors Info & Claims IDEAS '17: Proceedings of the 21st International Database Engineering & Applications SymposiumJuly 2017 Pages 176–183https://doi.org/10.1145/3105831.3105860Published:12 July 2017Publication History 1citation36DownloadsMetricsTotal Citations1Total Downloads36Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Nowadays, many applications call for collaborative solutions in order to accomplish complex projects requiring huge amounts of computing resources, e.g., physical science simulation. Many approaches have been proposed in order to design a task partitioning strategy able to assign pieces of execution to the appropriate workers. In this paper, we describe our peer to peer solution for solving complex works by using the idling computational resources of users connected to our network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. By doing this, users help each others by asking the network available computational resources when they face high computing demanding tasks. Differently from many proposal available for volunteer computing, users providing their resources are rewarded with tangible credits, i.e., they can redeem their credits by asking computation power to solve their own task or/and they can redeem them earning coins. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead).

Due to the emerging Big Data paradigm, driven by the increasing availability of intelligent services easily accessible by a large number of users (e.g., social networks), traditional data management techniques are inadequate in many real-life scenarios. In particular, the availability of huge amounts of data pertaining to user social interactions, user preferences, and opinions calls for advanced analysis strategies to understand potentially interesting social dynamics. Furthermore, heterogeneity and high speed of user-generated data require suitable data storage and management tools to be designed from scratch. This article presents a framework tailored for analyzing user interactions with intelligent systems while seeking some domain-specific information (e.g., choosing a good restaurant in a visited area). The framework enhances a user's quest for information by exploiting previous knowledge about their social environment, the extent of influence the users are potentially subject to, and the influence they may exert on other users. User influence spread across the network is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. Such features are the result of data exchange activity (called data posting) that enriches information sources with additional background information and knowledge derived from experiences and behavioral properties of domain experts and users. The approach is tested in an important application scenario such as tourist recommendation, but it can be profitably exploited in several other contexts, for example, viral marketing and food education.

Information management in healthcare is nowadays experiencing a great revolution. After the impressive progress in digitizing medical data by private organizations, also the federal government and other public stakeholders have also started to make use of healthcare data for data analysis purposes in order to extract actionable knowledge. In this paper, we propose an architecture for supporting interoperability in healthcare systems by exploiting Big Data techniques. In particular, we describe a proposal based on big data techniques to implement a nationwide system able to improve EHR data access efficiency and reduce costs.

Log analysis and querying recently received a renewed interest from the research community, as the effective understanding of process behavior is crucial for improving business process management. Indeed, currently available log querying tools are not completely satisfactory, especially from the viewpoint of easiness of use. As a matter of fact, there is no framework which meets the requirements of easiness of use, flexibility and efficiency of query evaluation. In this paper, we propose a framework for graphical querying of (process) log data that makes the log analysis task quite easy and efficient, adopting a very general model of process log data which guarantees a high level of flexibility. We implemented our framework by using a flexible storage architecture and a user-friendly data analysis interface, based on an intuitive and yet expressive graph-based query language. Experiments performed on real data confirm the validity of the approach.

Big data paradigm is currently the leading paradigm for data production and management. As a matter of fact, new information are generated at high rates in specialized fields (e.g., cybersecurity scenario). This may cause that the events to be studied occur at rates that are too fast to be effectively analyzed in real time. For example, in order to detect possible security threats, millions of records in a high-speed flow stream must be screened. To ameliorate this problem, a viable solution is the use of data compression for reducing the amount of data to be analyzed. In this paper we propose the use of privacy-preserving histograms, that provide approximate answers to 'safe' queries, for analyzing data in the cybersecurity scenario without compromising individuals' privacy, and we describe our system that has been used in a real life scenario.

Due to the emerging Big Data paradigm traditional data management techniques result inadequate in many real life scenarios. In particular, OLAP techniques require substantial changes in order to offer useful analysis due to huge amount of data to be analyzed and their velocity and variety. In this paper, we describe an approach for dynamic Big Data searching that based on data collected by a suitable storage system, enriches data in order to guide users through data exploration in an efficient and effective way.

Predicting the output power of renewable energy production plants distributed on a wide territory is a really valuable goal, both for marketing and energy management purposes. Vi-POC (Virtual Power Operating Center) project aims at designing and implementing a prototype which is able to achieve this goal. Due to the heterogeneity and the high volume of data, it is necessary to exploit suitable Big Data analysis techniques in order to perform a quick and secure access to data that cannot be obtained with traditional approaches for data management. In this paper, we describe Vi-POC -- a distributed system for storing huge amounts of data, gathered from energy production plants and weather prediction services. We use HBase over Hadoop framework on a cluster of commodity servers in order to provide a system that can be used as a basis for running machine learning algorithms. Indeed, we perform one-day ahead forecast of PV energy production based on Artificial Neural Networks in two learning settings, that is, structured and non-structured output prediction. Preliminary experimental results confirm the validity of the approach, also when compared with a baseline approach.

The increasing availability of large process log repositories calls for efficient solutions for their analysis. In this regard, a novel specialized compression technique for process logs is proposed, that builds a synopsis supporting a fast estimation of aggregate queries, which are of crucial importance in exploratory and high-level analysis tasks. The synopsis is constructed by progressively merging the original log-tuples, which represent single activity executions within the process instances, into aggregate tuples, summarizing sets of activity executions. The compression strategy is guided by a heuristic aiming at limiting the loss of information caused by summarization, while guaranteeing that no information is lost on the set of activities performed within the process instances and on the order among their executions. The selection conditions in an aggregate query are specified in terms of a graph pattern, that allows precedence relationships over activity executions to be expressed, along with conditions on their starting times, durations, and executors. The efficacy of the compression technique, in terms of capability of reducing the size of the log and of accuracy of the estimates retrieved from the synopsis, has been experimentally validated.

The pervasive diffusion of new generation devices like smart phones and tablets along with the widespread use of social networks causes the generation of massive data flows containing heterogeneous information generated at different rates and having different formats. These data are referred as \emph{Big Data} and require new storage and analysis approaches to be investigated for managing them. In this paper we will describe a system for dealing with massive tourism flows that we exploited for the analysis of tourist behavior in Italy. We defined a framework that exploits a NoSQL approach for data management and map reduce for improving the analysis of the data gathered from different sources

The problem of accurately predicting the energy production from renewable sources has recently received an increasing attention from both the industrial and the research communities. It presents several challenges, such as facing with the rate data are provided by sensors, the heterogeneity of the data collected, power plants efficiency, as well as uncontrollable factors, such as weather conditions and user consumption profiles. In this paper we describe Vi-POC (Virtual Power Operating Center), a project conceived to assist energy producers and decision makers in the energy market. In this paper we present the Vi-POC project and how we face with challenges posed by the specific application. The solutions we propose have roots both in big data management and in stream data mining.

In this paper, we study the problem of mining for frequent trajectories, which is crucial in many application scenarios, such as vehicle traffic management, hand-off in cellular networks, supply chain management. We approach this problem as that of mining for frequent sequential patterns. Our approach consists of a partitioning strategy for incoming streams of trajectories in order to reduce the trajectory size and represent trajectories as strings. We mine frequent trajectories using a sliding windows approach combined with a counting algorithm that allows us to promptly update the frequency of patterns. In order to make counting really efficient, we represent frequent trajectories by prime numbers, whereby the Chinese reminder theorem can then be used to expedite the computation.

RFID-based systems for object tracking and supply chain management have been emerging since the RFID technology proved effective in monitoring movements of objects. The monitoring activity typically results in huge numbers of readings, thus making the problem of efficiently retrieving aggregate information from the collected data a challenging issue. In fact, tackling this problem is of crucial importance, as fast answers to aggregate queries are often mandatory to support the decision making process. In this regard, a compression technique for RFID data is proposed, and used as the core of a system supporting the efficient estimation of aggregate queries. Specifically, this technique aims at constructing a lossy synopsis of the data over which aggregate queries can be estimated, without accessing the original data. Owing to the lossy nature of the compression, query estimates are approximate, and are returned along with intervals that are guaranteed to contain the exact query answers. The effectiveness of the proposed approach has been experimentally validated, showing a remarkable trade-off between the efficiency and the accuracy of the query estimation.

In this paper, we address the problem of trajectory data streams warehousing and querying, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose an end to end framework in order to make the querying step quite effective. We performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.

The recent advances in computing technology lead to the availability of a huge number of computational resources that can be easily connected through network infrastructures. Indeed, a really small fraction of the available computing power is fully exploited for performing effective computation of user tasks. On the contrary, there are several research projects that require a lot of computing power to reach their goals, but they usually lack adequate resources thus making the project activities quite hard to be completed. In this paper we describe D.E.A. (Distributed Execution Agent), a framework for sharing computational resources. We will exploit D.E.A. framework to tame the high computational demanding problem of hash MD5 reversing. We performed several experiments that confirmed the validity of our approach.

XML (eXtensible Markup Language) became in recent years the new standard for data representation and exchange on the WWW. This has resulted in a great need for data cleaning techniques in order to identify outlying data. In this paper, we present a technique for outlier detection that singles out anomalies with respect to a relevant group of objects. We exploit a suitable encoding of XML documents that are encoded as signals of fixed frequency that can be transformed using Fourier Transforms. Outliers are identified by simply looking at the signal spectra. The results show the effectiveness of our approach.

XML (eXtensible Markup Language) became in recent years the new standard for data representation and exchange on the WWW. This has resulted in a great need for data cleaning techniques in order to identify outlying data. In this paper, we present a technique for outlier detection that singles out anomalies with respect to a relevant group of objects. We exploit a suitable encoding of XML documents that are encoded as signals of fixed frequency that can be transformed using Fourier Transforms. Outliers are identified by simply looking at the signal spectra. The results show the effectiveness of our approach.

Trajectory data streams are huge amounts of data pertaining to time and position of moving objects. They are continuously generated by different sources exploiting a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data poses interesting challenges both for their proper definition and acquisition, thus making the mining process harder than for classical point data. In this paper, we address the problem of trajectory data outlier detection, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose a complete framework starting from data preparation task that allows us to make the mining step quite effective. Since the validation of data mining approaches has to be experimental we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed technique.

Trajectory data streams are huge amounts of data pertaining to time and position of moving objects generated by different sources continuously using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data streams poses interesting challenges both for their proper definition and acquisition, thus making the mining process harder than for classical point data. In this paper, we address the problem of trajectory data streams On Line Analytical Processing, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose an end to end framework in order to make the querying step quite effective. We performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.

The PDF format represents the de facto standard for print-oriented documents. In this paper, we address the problem of wrapping PDF documents, which raises new challenges in several contexts of text data management. Our proposal is based on a novel bottom-up hierarchical wrapping approach that exploits fuzzy logic to handle the “uncertainty” which is intrinsic to the structure and presentation of PDF documents. A PDF wrapper is defined by specifying a set of group type definitions that impose a target structure to groups of tokens containing the required information. Constraints on token groupings are formulated as fuzzy conditions, which are defined on spatial and content predicates of tokens. We define a formal semantics for PDF wrappers and propose an algorithm for wrapper evaluation working in polynomial time with respect to the size of a PDF document. The proposed approach has been implemented in a wrapper generation system that offers visual capabilities to assist the designer in specifying and evaluating a PDF wrapper. Experimental results have shown good accuracy and applicability of our system to PDF documents of various domains.

The increasing availability of huge amounts of data pertaining to time and position of moving objects generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatial data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data poses interesting challenges both for their proper definition and acquisition, thus making the mining process harder than for classical point data. In this paper, we address the problem of trajectory clustering, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose a complete framework starting from data preparation task that allows us to make the mining step quite effective. Since the validation of data mining approaches has to be experimental we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.

The increasing availability of huge amounts of thin data, i.e. data pertaining to time and positions generated by different sources with a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatio-temporal data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. In this paper, we address the clustering of spatial trajectories. In the context of trajectory data, this problem is even more challenging than in the classical transactions, as here we deal with data (trajectories) in which the order of items is relevant. We propose a novel approach based on a suitable regioning strategy and an efficient clustering technique based on edit distance. Experiments performed on real world datasets have confirmed the efficiency and effectiveness of the proposed techniques.

Radio Frequency Identification (RFID) applications are emerging as key components in object tracking and supply chain management systems since in the next future almost every major retailer will use RFID systems to track the shipment of products from suppliers to warehouses. Due to the streaming nature of RFID readings, large amounts of data are generated by these devices at high production rates. This phenomenon is even more relevant since RFIDs are so cheap that every individual item can be tagged thus leaving a "trail" of data as it moves across different locations. This scenario raises new challenges in effectively and efficiently exploiting such large amounts of data. In this paper we address the problem of compressing RFID data in order to enable devices with limited amount of available memory (such as PDAs) to issue queries on RFID warehouses. In particular, we designed a lossy strategy for collapsing tuples carrying information about items being delivered at different location of the supply chain.

XPath expressions define navigational queries on XML data and are issued on XML documents to select sets of element nodes. Due to the wide use of XPath, which is embedded into several languages for querying and manipulating XML data, the problem of efficiently answering XPath queries has received increasing attention from the research community. As the efficiency of computing the answer of an XPath query depends on its size, replacing XPath expressions with equivalent ones having the smallest size is a crucial issue in this direction. This article investigates the minimization problem for a wide fragment of XPath (namely X P [✶] ), where the use of the most common operators (child, descendant, wildcard and branching) is allowed with some syntactic restrictions. The examined fragment consists of expressions which have not been specifically studied in the relational setting before: neither are they mere conjunctive queries (as the combination of “//” and “*” enables an implicit form of disjunction to be expressed) nor do they coincide with disjunctive ones (as the latter are more expressive). Three main contributions are provided. The “global minimality” property is shown to hold: the minimization of a given XPath expression can be accomplished by removing pieces of the expression, without having to re-formulate it (as for “general” disjunctive queries). Then, the complexity of the minimization problem is characterized, showing that it is the same as the containment problem. Finally, specific forms of XPath expressions are identified, which can be minimized in polynomial time.

Radio frequency identification (RFID) applications are emerging as key components in object tracking and supply chain management systems. In next future almost every major retailer will use RFID systems to track the shipment of products from suppliers to warehouses. Due to RFID readings features this will result in a huge amount of information generated by such systems when costs will be at a level such that each individual item could be tagged thus leaving a trail of data as it moves through different locations. We define a technique for efficiently detecting anomalous data in order to prevent problems related to inefficient shipment or fraudulent actions. Since items usually move together in large groups through distribution centers and only in stores do they move in smaller groups we exploit such a feature in order to design our technique. The preliminary experiments show the effectiveness of our approach.

Because of the widespread diffusion of semistructured data in XML format, much research effort is currently devoted to support the storage and retrieval of large collections of such documents. XML documents can be compared as to their structural similarity, in order to group them into clusters so that different storage, retrieval, and processing techniques can be effectively exploited. In this scenario, an efficient and effective similarity function is the key of a successful data management process. We present an approach for detecting structural similarity between XML documents which significantly differs from standard methods based on graph-matching algorithms, and allows a significant reduction of the required computation costs. Our proposal roughly consists of linearizing the structure of each XML document, by representing it as a numerical sequence and, then, comparing such sequences through the analysis of their frequencies. First, some basic strategies for encoding a document are proposed, which can focus on diverse structural facets. Moreover, the theory of discrete Fourier transform is exploited to effectively and efficiently compare the encoded documents (i.e., signals) in the domain of frequencies. Experimental results reveal the effectiveness of the approach, also in comparison with standard methods.

We introduce a technique based on data mining algorithms for classifying incoming messages, as a basis for an overall architecture for maintenance and management of e-mail messages. We exploit clustering techniques for grouping structured and unstructured information extracted from e-mail messages in an unsupervised way, and exploit the resulting algorithm in the process of folder creation (and maintenance) and e-mail redirection. Some initial experimental results show the effectiveness of the technique, both from an efficiency and a quality-of-results viewpoint.

In this paper we propose the combined use of different methods to improve the data analysis process. This is obtained by combining inductive and deductive techniques. Inductive techniques are used for generating hypotheses from data whereas deductive techniques are used to derive knowledge and to verify hypotheses. In order to guide users in the the analysis process, we have developed a system which integrates deductive tools, data mining tools (such as classification algorithms and features selection algorithms), visualization tools and tools for the easy manipulation of data sets. The system developed is currently used in a large project whose aim is the integration of information sources containing data concerning the socio-economic aspects of Calabria and the analysis of the integrated data. Several experiments on socio-economic indicators of Calabrian cities have shown that the combined use of different techniques improves both the comprehensibility and the accuracy of models. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.