Ensuring robust cybersecurity in modern network environments is increasingly challenging due to the growing complexity and volume of network traffic data. Traditional detection systems often fail to identify stealthy and sophisticated attacks, such as Distributed Denial of Service (DDoS), ARP poisoning, and reconnaissance scans. Moreover, many existing methods lack transparency and produce reports that are difficult for analysts to interpret, slowing both threat comprehension and response. This paper addresses these challenges by introducing a novel methodology that integrates Knowledge Graphs, XAI techniques and Large Language Models (LLMs) to enhance network threat detection, classification, explainability, and automated reporting. The proposed approach employs Graph-BERT to encode complex communication patterns and semantic relationships into enriched knowledge graphs constructed from network logs. To ensure model transparency and interpretability, Local Interpretable Model-Agnostic Explanations (LIME) are incorporated, while structured prompts guide report generation using Generative AI. Experimental results obtained on benchmark datasets demonstrate that the methodology achieves a classification accuracy exceeding 84 %, outperforming existing detection techniques. Additionally, a comprehensive evaluation involving ablation analysis, LLM-based assessments, and expert reviews shows that incorporating structured knowledge and explainability significantly enhances the clarity, correctness, and informativeness of generated reports. These findings confirm the system’s effectiveness both as a detection mechanism and as a practical tool that helps analysts understand threats and craft informed responses.

Intrusion Detection and Prevention Systems (IDS/IPS) in large enterprises can generate hundreds of thousands of alerts per hour, overwhelming analysts with logs requiring rapidly evolving expertise. Conventional machine-learning detectors reduce alert volume but still yield many false positives, while standard Retrieval-Augmented Generation (RAG) pipelines often retrieve irrelevant context and fail to justify predictions. We present CyberRAG, a modular agent-based RAG framework that delivers real-time classification, explanation, and structured reporting for cyber-attacks. A central LLM agent orchestrates: (i) fine-tuned classifiers specialized by attack family; (ii) tool adapters for enrichment and alerting; and (iii) an iterative retrieval-and-reason loop that queries a domain-specific knowledge base until evidence is relevant and self-consistent. Unlike traditional RAG, CyberRAG adopts an agentic design that enables dynamic control flow and adaptive reasoning. This architecture autonomously refines threat labels and natural-language justifications, reducing false positives and enhancing interpretability. It is also extensible: new attack types can be supported by adding classifiers without retraining the core agent. CyberRAG was evaluated on SQL Injection, XSS, and SSTI, achieving over 94% accuracy per class and a final classification accuracy of 94.92% through semantic orchestration. Generated explanations reached 0.94 in BERTScore and 4.9/5 in GPT-4-based expert evaluation, with robustness preserved against adversarial and unseen payloads. These results show that agentic, specialist-oriented RAG can combine high detection accuracy with trustworthy, SOC-ready prose, offering a flexible path toward partially automated cyber-defense workflows.

Abstract In today’s digital landscape, user-generated opinions—such as online reviews, user comments, and social media posts—offer valuable insights into people’s experiences, sentiments, and concerns, influencing decisions across businesses, organizations, and public policy. Advanced machine learning techniques, particularly Large Language Models (LLMs) like BERT and GPT, facilitate the automated analysis of this vast, unstructured data to extract actionable information. However, beyond high classification accuracy, there is a growing demand for explainability to ensure transparency and trust in automated systems. Understanding why an opinion is classified in a particular way is critical for informed decision-making. This paper proposes a multidimensional, explainable framework that combines LLM-based classification across latent dimensions (e.g., sentiment, topic, emotion), interpretable AI for identifying influential words, and generative AI for producing human-readable explanations. Unlike standard explanations generated solely by models such as GPT, our method integrates Explainable AI (XAI) techniques to pinpoint influential words for each classification dimension and organizes them into structured, dimension-aware outputs—significantly enhancing interpretability and alignment with model predictions. Experimental results—based on text-level metrics, latent space representations, and qualitative assessments from both automated tools and human experts—demonstrate the effectiveness of our approach in improving transparency, interpretability, and usability in opinion analysis.

Hashtag recommendation systems have emerged as a key tool for automatically suggesting relevant hashtags and enhancing content categorization and search. However, existing static models struggle to adapt to the highly dynamic nature of social media conversations, where new hashtags constantly emerge and existing ones undergo semantic shifts. To address these challenges, this article introduces hashtag recommendation by detecting and adapting to trend shifts (H-ADAPTS), a dynamic hashtag recommendation methodology that employs a trend-aware mechanism to detect shifts in hashtag usage—reflecting evolving trends and topics within social media conversations—and triggers efficient model adaptation based on a (small) set of recent posts. Additionally, the Apache storm framework is leveraged to support scalable and fault-tolerant analysis of high-velocity social data, enabling the timely detection of trend shifts. Experimental results from two real-world case studies, including the COVID-19 pandemic and the 2020 US presidential election, demonstrate the effectiveness of H-ADAPTS in providing timely and relevant hashtag recommendations by adapting to emerging trends, significantly outperforming existing solutions.

The worldwide use of social media has generated vast volumes of user-generated content, offering valuable insights into public discourse, behavioral dynamics, and emerging trends. However, extracting meaningful topics from such data remains a significant challenge due to the informal, dynamic, and context-dependent nature of online language, where the semantics of terms and hashtags are often shaped by the specific sociocultural and temporal contexts in which they arise. To address these challenges, we propose NTM-HEC (Neural Topic Modeling via Hashtag Embedding Clustering), a novel hashtag-centric methodology for topic discovery that leverages the semantic richness encoded in hashtags, commonly used by social media users to annotate and categorize content. NTM-HEC relies on clustering low-dimensional embeddings of latent hashtag representations to uncover coherent and diverse topic structures. This enables it to fully leverage the inherently topical nature of hashtags, enhancing interpretability and improving robustness to linguistic variability and context-specificity. We evaluate the effectiveness of NTM-HEC through two case studies focused on online discourse surrounding the Russia-Ukraine conflict and the COVID-19 pandemic. In both cases, NTM-HEC outperforms competing models in topic coherence and diversity, demonstrating its ability to capture nuanced, trend-specific semantic patterns within real-world social media discussions.

Drones used for activities such as environmental monitoring and infrastructure inspection generate vast amounts of data, requiring dedicated infrastructure for efficient management. While the cloud is a widely adopted solution, it often faces limitations such as latency, bandwidth constraints, and scalability challenges. To this scope, this paper presents a novel framework that leverages edge-cloud continuum platforms to overcome these issues. By combining the immediacy of edge computing with the computational power of the cloud, the framework processes data close to its source for real-time responsiveness and efficiently distributes tasks across multiple infrastructure layers, from edge devices to regional data centers and centralized clouds. This hybrid approach enhances scalability, efficiency, and responsiveness, addressing the demands of modern monitoring systems. The paper also addresses the lack of standardized protocols in edge-cloud configurations, a key obstacle to seamless interoperability. The proposed framework supports developers in designing and deploying applications across the edge-cloud continuum in a platform-independent manner, optimizing deployment configurations and services to meet strict quality of service (QoS) requirements. A case study on fire monitoring validates the framework, demonstrating substantial improvements in latency and scalability for critical applications such as disaster management and environmental conservation. By enabling scalable, adaptable, and cross-platform applications, the framework provides a robust solution for the complex needs of real-time, mission-critical scenarios.

Abstract In the digital landscape, social media has emerged as a prevalent channel for global communication, connecting like-minded individuals worldwide. However, while facilitating information exchange, it is also susceptible to the dissemination of false information, posing a constant challenge to the reliability of online content. To address this issue, this paper introduces a novel methodology called TM-FID (Topic-oriented Multimodal False Information Detection), which combines false information detection and neural topic modeling within a semi-supervised multimodal approach. By jointly leveraging textual and visual information contained in online news, our approach provides insights into how false information influences specific discussion topics, thus enabling a comprehensive and fine-grained understanding of its spread and impact on social media conversation. Experimental evaluation carried out on a set of multimodal gossip-related news demonstrates the quality of the identified topics, assessed through a novel centroid-based metric, as well as the efficacy of the cross-attention mechanism used within TM-FID to accurately identify false information in multimodal news. Overall, the proposed methodology can enable effective strategies to counter the spread of false information, thereby fostering trust and confidence in the information shared on social media platforms.

Contemporarily, in light of the intelligent transportation systems (ITS) sector, the tendency can be observed that the solution of the multi-objective cyber-physical optimization problems with imperfect information takes an increasingly weighted role. In the present scientific work, the authors want to take these developments into account by introducing an innovative cyber-physical architectural design and corresponding the two-stage heuristic computing approach. It is utilized in synergy with the MCSA11Multi-tier Cyber-physical System Architecture and DCEx architectural principles for the workflow scheduling of Monte-Carlo simulation, which is based on the intelligent and sustainable route-order dispatching process model. Factors such as emissions, transport costs, risks, and the individual weighting of orders are reflected in the model. In particular, the authors define a stochastic ILP-based22Integer Linear Programming monte-carlo workflow model. They further propose two-stage scheduling heuristic with 6-HEFT DAG relaxation as first stage and apply state-of-the-art techniques as a part of SCIP framework to solve 2nd 1–0 ILP-based stage; evaluate the performance of the scheduling approach. The authors obtain preliminary results of the second stage behavior using a realistic heterogeneous computing scenario and corresponding constraint structures within MACS simulator engine33Modular Architecture for Complex Computing Systems Analysis. The results from the experiments illustrate moderate complexity of the approach. Scalability of the model looks promising for the applicability in various industry-related scenarios and corresponding computing environments.

Abstract Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications.

More and more in recent years, IT companies have adopted edge-cloud continuum solutions to efficiently perform analysis tasks on data generated by IoT devices. As an example, in the context of urban mobility, the use of edge solutions can be extremely effective in managing tasks that require real-time analysis and low response times, such as driver assistance, collision avoidance and traffic sign recognition. On the other hand, the integration with cloud systems can be convenient for tasks that require a lot of computing resources for accessing and analyzing big data collections, such as route calculations and targeted advertising. Designing and testing such hybrid edge-cloud architectures are still open issues due to their novelty, large scale, heterogeneity, and complexity. In this paper, we analyze how the compute continuum can be exploited for efficiently managing urban mobility tasks. In particular, we focus on a case study related to taxi fleets that need to find locations where they are more likely to find new passengers. Through a simulation-based approach, we demonstrate that these solutions turn out to be effective for this class of problems, especially as the number of connected vehicles increases.

High-level programming models can help application developers to access and use resources without the need to manage low-level architectural entities, as a parallel programming model defines a set of programming abstractions that simplify the way by which a programmer structures and expresses her/his algorithm. Early proposals of Exascale programming tools are based on the adaptation of traditional parallel programming languages and hybrid solutions. This incremental approach is too conservative, often resulting in very complex code. This paper describes the design features, the programming constructs, and the runtime mechanisms of the Data Centric programming model for Exascale systems (DCEx). DCEx is based on structuring applications into data-parallel blocks. Blocks are units of shared-and distributed-memory parallel computation, communication, and migration in the memory/storage hierarchy. Blocks and their message queues are mapped onto processes and placed in memory/storage by the DCEx runtime. Those data-parallel blocks are orchestrated by using distributed parallel patterns that simplify the development cost. DCEx aims to reach the convergence of traditional HPC programming models, mainly based on MPI, with the emerging technologies based on the data intensive paradigms. To demonstrate the potential of DCEx, we carried out an experimental evaluation developing a real-world diffusion-weighted magnetic resonance imaging data processing application in a neuroimaging research context.

The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and searched. In Twitter and other social media platforms, hashtags are exploited by users to facilitate the search, categorization, and spread of posts. Choosing the appropriate hashtags for a post is not always easy for users, and therefore posts are often published without hashtags or with hashtags not well defined. To deal with this issue, we propose a new model, called HASHET ( HAshtag recommendation using Sentence-to-Hashtag Embedding Translation ), aimed at suggesting a relevant set of hashtags for a given post. HASHET is based on two independent latent spaces for embedding the text of a post and the hashtags it contains. A mapping process based on a multi-layer perceptron is then used for learning a translation from the semantic features of the text to the latent representation of its hashtags. We evaluated the effectiveness of two language representation models for sentence embedding and tested different search strategies for semantic expansion, finding out that the combined use of BERT ( Bidirectional Encoder Representation from Transformer ) and a global expansion strategy leads to the best recommendation results. HASHET has been evaluated on two real-world case studies related to the 2016 United States presidential election and COVID-19 pandemic. The results reveal the effectiveness of HASHET in predicting one or more correct hashtags, with an average F -score up to 0.82 and a recommendation hit-rate up to 0.92. Our approach has been compared to the most relevant techniques used in the literature ( generative models , unsupervised models, and attention-based supervised models ) by achieving up to 15% improvement in F -score for the hashtag recommendation task and 9% for the topic discovery task.

Researchers and leading IT companies are increasingly proposing hybrid cloud/edge solutions, which allow to move part of the workload from the cloud to the edge nodes, by reducing the network traffic and energy consumption, but also getting low latency responses near to real time. This paper proposes a novel hybrid cloud/edge architecture for efficiently extracting Regions-of-Interest (RoI) in a large scale urban computing environment, where a huge amount of geotagged data are generated and collected through users's mobile devices. The proposal is organized in two parts: ($i$) a modeling part that defines the hybrid cloud/edge architecture capable of managing a large number of devices; (ii) a simulation part in which different design choices are evaluated to improve the performance of RoI mining algorithms in terms of processing time, network delay, task failure and computing resource utilization. Several experiments have been carried out to evaluate the performance of the proposed architecture starting from different configurations and orchestration policies. The achieved results showed that the proposed hybrid cloud/edge architecture, with the use of two novel orchestration policies (network- and utilization-based), permits to improve the exploitation of resources, also granting low network latency and task failure rate in comparison with other standard scenarios (only-edge or only-cloud).

Due to their inherent cyber-physical features and high interactivity, IoT services exhibit performances which are simultaneously impacted by different orthogonal factors. Indeed, deployment settings (e.g., Cloud- or Edge-based scenarios, network bandwidth, hardware resource availability), algorithmic aspects (e.g., the specific algorithm used to solve a problem) and data features (e.g., packet size and rate) deeply affect the overall functioning of an IoT service and its compliance with specific requirements such as reactivity, reliability and efficiency. An accurate parameter sweep based on realistic IoT simulations is a viable, yet still unexplored, solution to obtain a full-fledged overview and specific evaluations about the performance of an IoT system under development. In such a direction, in this paper we present an approach for assessing Edge analytic in complex IoT scenarios through a parameter sweep analysis conducted through a simulation-based process, enabling a fine-grained modeling of hybrid IoT systems (both Cloud and Edge) of different scales (small, medium and large). Four typical IoT use cases (autonomous vehicles, smart healthcare, gaming, and industrial IoT) are presented to show the benefits of our approach in finding the right settings for configuring and running them. Indeed, the obtained results show that our approach concretely helps IoT developers in the challenging task of tuning the parameters’ set so as to meet the given requirements, even in the case of large solution spaces and before the actual deployment phase.

Summary Geotagged data gathered from social media can be used to discover places‐of‐interest (PoIs) that have attracted many visitors. Since a PoI is generally identified by geographical coordinates of a single point, it is hard to match it with people trajectories. Therefore, we define an area, called region‐of‐interest (RoI), represented by the boundaries of a PoI. The main goal of this study is to discover RoIs from PoIs using spatial data mining techniques. In this paper, we propose a new parallel method for extracting RoIs from social media datasets. It consists of two main steps: (i) automatic keyword extraction and data grouping and (ii) parallel RoI extraction. The first step extracts keywords identifying the PoIs; these keywords are used to group social media items according to the places they refer to. The second step uses a Parallel Clustering Approach (ParCA) of spatial dataset to identify RoIs. ParCA exploits a parallel execution of DBSCAN on subsets of data to generate subclusters on each processing node and then merge overlapping subclusters to form global clusters. ParCA was implemented using the MapReduce model. Experiments performed over a set of PoIs in the city of Rome using social media data show that our approach is highly scalable and reaches an accuracy of 79% in detecting RoIs. On a parallel computer with 50 cores, we obtained a speedup of 52 by processing large datasets divided into 32 splits, compared with the execution time registered when each dataset is not partitioned.

In this special issue of Concurrency and Computation Practice and Experience, we are pleased to present eight selected papers that were previously presented at the Brazilian "XX Simpósio em Sistemas Computacionais de Alto Desempenho," WSCAD 2019. The event was held in conjunction with the 31st International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD 2019, in Campo Grande, MS, Brazil, from October 15 to 18, 2019. The WSCAD workshop has been presenting important research in the fields of computer architectures, high performance computing, and distributed systems, since the beginning of the 2000s. The scope of the current special issue is broad and representative, with different forms of contributions to our discipline: methodological papers, technology papers, application papers, and system papers. The topics covered in the papers include architecture issues, compiler optimization, performance evaluation, parallel algorithms, energy efficiency, and applications. The title of the first paper is "Structural testing for communication events into loops of message-passing parallel programs," by Diaz et al.1 In this paper, the authors propose new structural testing criteria for message-passing parallel programs, focusing on defects from communication primitives into loops. A new test model is presented to support their criteria for structural testing of MPI-applications. The testing criteria are validated through experimental studies using a tool called ValiMPI. The results show that unknown defects from communication and synchronization events can be revealed in different loop iterations, increasing the quality of message-passing parallel programs. In the second contribution, entitled "Smart selection of optimizations in dynamic compilers," Rosario et al.2 present an approach that uses machine learning to select sequences of optimization for dynamic compilation that considers both code quality and compilation overhead. Their approach starts by training a model, offline, with a knowledge bank of those sequences with low overhead and high-quality code generation capability using a genetic heuristic. Then, this bank is used to guide the smart selection of optimizations sequences for the compilation of code fragments during the emulation of an application. The proposed strategy is evaluated in two LLVM-based dynamic binary translators, namely, OI-DBT and HQEMU, showing that these two translators can achieve average speedups of 1.26× and 1.15× in MiBench and Spec Cpu benchmarks, respectively. In the third contribution, entitled "Memory allocation anomalies in high-performance computing applications: A study with numerical simulations," Gomes et al.3 propose a method for identifying, locating, characterizing, and fixing allocation anomalies, and a tool for developers to apply the method. A numerical simulator that approximates the solutions to partial differential equations using a finite element method is used in the experiments. It is shown that taming allocation anomalies in the simulator reduces both its execution time and the memory footprint of its processes, irrespective of the specific heap allocator being employed with it. They conclude that the developer of HPC applications can benefit from the method and tool during the software development cycle. The fourth contribution, entitled "Investigating memory prefetcher performance over parallel applications: From real to simulated," by Girelli et al.,4 contributes to shed light on the memory prefetcher's role in the performance of parallel high-performance computing applications, considering the prefetcher algorithms offered by both the real hardware and the simulators. The authors performed a careful experimental investigation, executing the NAS parallel benchmark (NPB) on a real Skylake machine, and as well in a simulated environment with the ZSim and Sniper simulators, taking into account the prefetcher algorithms offered by both Skylake and the simulators. The experimental results show that: (i) prefetching from the L3 to L2 cache presents better performance gains, (ii) the memory contention in the parallel execution constrains the prefetcher's effect, (iii) Skylake's parallel memory contention is poorly simulated by ZSim and Sniper, and (iv) Skylake's noninclusive L3 cache hinders the accurate simulation of NPB with the Sniper's prefetchers. In the fifth contribution, entitled "Energy efficiency and portability of oil and gas simulations on multicore and graphics processing unit architectures," Serpa et al.5 propose three optimizations for an oil and gas application, reverse time migration (RTM), which reduce the floating-point operations by changing the equation derivatives. They evaluate these optimizations in different multicore and GPU architectures, investigating the impact of different APIs on the performance, energy efficiency, and portability of the code. The experimental results show that the dedicated CUDA implementation running on the NVIDIA Volta architecture has the best performance and energy efficiency for RTM on GPUs, while the OpenMP version is the best for Intel Broadwell in the multicore. Also, the OpenACC version, which has a lower programming effort and executes on both architectures, has up to 20% better performance and energy efficiency than the nonportable ones. In the sixth paper, entitled "An open computing language-based parallel Brute Force algorithm for formal concept analysis on heterogeneous architectures," Novais et al.6 propose and evaluate an Open Computing Language (OpenCL)-based Brute Force algorithm for formal concept extraction on heterogeneous architectures (CPU + GPU and CPU + FPGA). The CPU + GPU architecture presents higher performance and scalability than other architectures when the Brute Force algorithm processes high dimensional contexts with many objects and attributes. Their parallel approach shows performance results up to 18× better than a smarter sequential algorithm called Data-Peeler. Moreover, the Brute Force algorithm running on CPU + GPU architecture has greater energy efficiency, reaching at least 1.79× more operations per energy consumption than other algorithms on different architectures explored in the work. In the seventh paper, entitled "Contextual contracts for component-oriented resource abstraction in a cloud of high performance computing services," Junior et al.7 present HPC Shelf, a cloud computing services platform to build and deploy large-scale parallel computing systems. They introduce Alite, the contextual contract system of HPC Shelf, to select component implementations according to requirements of the host application, target parallel computing platform characteristics (e.g., clusters and MPPs), quality of service (QoS) properties, and cost restrictions. It is evaluated through a small-scale case study employing two complementary component-based frameworks. The first one aims to represent components that implement linear algebra computations based on the BLAS interface. In turn, the second one aims to represent parallel computing platforms on the IaaS cloud offered by Amazon EC2 Service. The last paper in this special issue, "High-performance IO for seismic processing on the cloud" authored by Guimarães et al.,8 analyzes the main file structures currently used to store seismic data and propose a new intermediate data structure to improve IO performance while still complying with established standards. They show that, throughout a common workflow in seismic data analysis, the IO performance gain greatly surpasses the overhead of translating data to the intermediate structure. The approach enables a speedup of up to 208 times in reading time when using classical standards (e.g., SEG-Y) and the intermediate structure is up to 1.8 times more efficient than modern formats (e.g., ASDF). Considering cache-friendly applications, the speedups over the direct use of SEG-Y reach 8000 times. They also performed a cost analysis on the AWS cloud showing that HDDs can be 1.25 times more cost-effective than SSDs. The research papers presented in this special issue provide insights in fields related to high performance computing, including performance evaluation, parallel algorithms, and applications in science and engineering. We believe that the main contributions presented in the research papers are timely and important, and hope that readers can benefit from the papers and contribute to these rapidly growing areas. Many individuals contributed a great deal of time and energy toward the success of this special issue. We would like to thank all the authors who provided valuable contributions to this special issue. We are also grateful to the reviewers for their many hours of dedicated efforts, with valuable feedback to the authors. Finally, we would also like to express our gratitude to the Editor-in-Chief of CCPE, for his advice, vision, and support, making this special issue possible.

Nowadays, massive amounts of data are acquired, transferred, and analyzed nearly in real-time by utilizing a large number of computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable efficient monitoring of applications and infrastructures of such complex systems. In this paper, we introduce an Integer Linear Programming (ILP) model called M3AT for optimized assignment of monitoring agents and aggregators on large-scale computing systems. We identified a set of requirements from three representative data-intensive applications and exploited them to define the model's input parameters. We evaluated the scalability of M3AT using the Constraint Integer Programing (SCIP) solver with default configuration based on synthetic data sets. Preliminary results show that the model provides optimal assignments for subsystems composed of up to 200 monitoring agents with complex I/O policies, while keeping the number of aggregators constant and demonstrates variable sensitivity with respect to the scale of monitoring data aggregators and limitation policies imposed.

Summary File sharing is one of the leading Internet applications of P2P technology. Given the high number of computer nodes involved in peer‐to‐peer networks, reducing their aggregate energy consumption is an important challenge to be faced. In this paper, we show how the sleep‐and‐wake energy saving approach can be exploited to reduce energy consumption in BitTorrent, one of the most popular file sharing peer‐to‐peer networks. We describe BitTorrentSW, a sleep‐and‐wake approach for BitTorrent networks that allows seeders (ie, peers that hold complete files) to cyclically switch between wake and sleep modes to save energy while ensuring good file sharing performance. The decision to switch to sleep mode is taken independently by each seeder based on local information about the composition of the peer‐to‐peer network. BitTorrentSW has been evaluated through PeerSim using real BitTorrent traces. The simulation results show that, in all the configurations under analysis, the percentage of energy saved by BitTorrentSW is much higher than the percentage of increase in download time. For instance, in a network with 50% of seeders, about 20% of energy is saved using BitTorrentSW, with an increase of only 7% of the average time needed to complete a file download compared to a standard BitTorrent network in which all seeders are always powered on.

Geotagged data gathered from social media can be used to discover interesting locations visited by users called Places-of-Interest (PoIs). Since a PoI is generally identified by the geographical coordinates of a single point, it is hard to match it with user trajectories. Therefore, it is useful to define an area, called Region-of-Interest ( RoI ), to represent the boundaries of the PoI’s area. RoI mining techniques are aimed at discovering ROIs from PoIs and other data. Existing RoI mining techniques are based on three main approaches: predefined shapes, density-based clustering, and grid-based aggregation. This article proposes G-RoI , a novel RoI mining technique that exploits the indications contained in geotagged social media items to discover RoIs with a high accuracy. Experiments performed over a set of PoIs in Rome and Paris using social media geotagged data, demonstrate that G-RoI in most cases achieves better results than existing techniques. In particular, the mean F 1 score is 0.34 higher than that obtained with the well-known DBSCAN algorithm in Rome RoIs and 0.23 higher in Paris RoIs.

The extraction of useful information from data is often a complex process that can be conveniently modeled as a data analysis workflow. When very large data sets must be analyzed and/or complex data mining algorithms must be executed, data analysis workflows may take very long times to complete their execution. Therefore, efficient systems are required for the scalable execution of data analysis workflows, by exploiting the computing services of the Cloud platforms where data is increasingly being stored. The objective of the paper is to demonstrate how Cloud software technologies can be integrated to implement an effective environment for designing and executing scalable data analysis workflows. We describe the design and implementation of the Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-a-Service (SaaS) model. DMCF was designed taking into account the needs of real data mining applications, with the goal of simplifying the development of data mining applications compared to generic workflow management systems that are not specifically designed for this domain. The result is a high-level environment that, through an integrated visual workflow language, minimizes the programming effort, making easierto domain experts the use of common patterns specifically designed forthe development and the parallel execution of data mining applications. The DMCF's visual workflow language, system architecture and runtime mechanisms are presented. We also discuss several data mining workflows developed with DMCF and the scalability obtained executing such workflows on a public Cloud.

Summary As data intensive scientific computing systems become more widespread, there is a necessity of simplifying the development, deployment, and execution of complex data analysis applications for scientific discovery. The scientific workflow model is the leading approach for designing and executing data‐intensive applications in high‐performance computing infrastructures. Commonly, scientific workflows are built by a set of connected tasks arranged in a directed acyclic graph style, which communicate through storage abstractions. The Data Mining Cloud Framework (DMCF) is a system allowing users to design and execute data analysis workflows on cloud platforms, relying on cloud storage services for every I/O operation. Hercules is an in‐memory I/O solution that can be used in DMCF as an alternative to cloud storage services, providing additional performance and flexibility features. This work improves the integration between DMCF and Hercules by using a data‐aware scheduling strategy for exploiting data locality in data‐intensive workflows. This paper presents experimental results demonstrating the performance improvements achieved using the proposed data‐aware scheduling strategy in the Microsoft Azure cloud platform. In particular, with our scheduling strategy, the I/O overhead has been reduced by 55% with respect to the Azure storage, leading to a 20% reduction of the total execution time.

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

Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15min late) and they are estimated to have an annual cost of billions of dollars. This scenario makes the prediction of flight delays a primary issue for airlines and travelers. The main goal of this work is to implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled departure and arrival time) and weather conditions at origin airport and destination airport according to the flight timetable. Airline flight and weather observation datasets have been analyzed and mined using parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15min, we achieve an accuracy of 74.2% and 71.8% recall on delayed flights, while with a threshold of 60min, the accuracy is 85.8% and the delay recall is 86.9%. Furthermore, the experimental results demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks as MapReduce applications on the Cloud.

Summary Workflows are an effective paradigm to model complex data analysis processes, such as knowledge discovery in databases applications, which can be efficiently executed on distributed computing systems such as a Cloud platform. Data analysis workflows can be designed through visual programming, which is a convenient design approach for high‐level users. On the other hand, script‐based workflows are a useful alternative to visual workflows, because they allow expert users to program complex applications more effectively. In order to provide Cloud users with an effective script‐based data analysis workflow formalism, we designed the JS4Cloud language. The main benefits of JS4Cloud are as follows: (i) it extends the well‐known JavaScript language while using only its basic functions (arrays, functions, and loops); (ii) it implements both a data‐driven task parallelism that automatically spawns ready‐to‐run tasks to the Cloud resources and data parallelism through an array‐based formalism; and (iii) these two types of parallelism are exploited implicitly so that workflows can be programmed in a fully sequential way, which frees users from duties like work partitioning, synchronization, and communication. We describe how JS4Cloud has been integrated within the data mining cloud framework (DMCF), a system supporting the scalable execution of data analysis workflows on Cloud platforms. In particular, we describe how data analysis workflows modeled as JS4Cloud scripts are processed by DMCF by exploiting parallelism to enable their scalable execution on Clouds. Finally, we present some data analysis workflows developed with JS4Cloud and the performance results obtained by executing such workflows on DMCF. Copyright © 2015 John Wiley & Sons, Ltd.

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

Data mining techniques are used in many application areas to extract useful knowledge from large datasets. Very often, parameter sweeping is used in data mining applications to explore the effects produced on the data analysis result by different values of the algorithm parameters. Parameter sweeping applications can be highly computing demanding, since the number of single tasks to be executed increases with the number of swept parameters and the range of their values. Cloud technologies can be effectively exploited to provide end-users with the computing and storage resources, and the execution mechanisms needed to efficiently run this class of applications. In this paper, we present a Data Mining Cloud App framework that supports the execution of parameter sweeping data mining applications on a Cloud. The framework has been implemented using the Windows Azure platform, and evaluated through a set of parameter sweeping clustering and classification applications. The experimental results demonstrate the effectiveness of the proposed framework, as well as the scalability that can be achieved through the parallel execution of parameter sweeping applications on a pool of virtual servers.

MapReduce is a programming model widely used in data centers for processing large data sets in a highly parallel way. Current MapReduce systems are based on master-slave architectures that do not cope well with dynamic node participation, since they are mostly designed for conventional parallel computing platforms. On the contrary, in Internet-based computing environments, node churn and failures - including master failures - are likely to happen since nodes join and leave the network at an unpredictable rate. The goal of this work is enabling the use of MapReduce in dynamic distributed environments so as to combine the effectiveness of a well-established programming model with the scalability of a large-scale computing infrastructure. This paper presents an adaptive MapReduce framework, called P2P-MapReduce, which exploits a peer-to-peer model to manage intermittent node participation, master failures and job recovery in a decentralized but effective way, so as to provide a more robust MapReduce middleware that can be effectively exploited in Internet-scale dynamic distributed environments.