The rapid growth of Cloud Computing and the expansion of large data centers have led to significant increases in energy consumption for hardware and cooling systems. This surge in power usage has raised operational costs, making energy efficiency a pressing issue for data center management. Virtual Machines (VMs) consolidation is a widely studied strategy to mitigate these costs by minimizing the number of active physical servers while maintaining user Service Level Agreements (SLAs). However, the success of consolidation largely depends on accurately forecasting VM resource needs. This paper presents the design and development of an energy-aware VMs allocation system that leverages predictive regression models to forecast future computational demands (CPU) of each VM. By anticipating these needs, the system optimally allocates VMs to available servers, effectively balancing energy savings with performance. The experimental evaluation, based on regression modeling exploited to forecast CPU and energy usage, shows a comparative analysis among several approaches and demonstrates the effectiveness of machine learning-driven allocations of VMs in optimizing resource utilization and reducing energy consumption.

The rapid growth of Cloud Computing and the expansion of large data centers have led to significant increases in energy consumption for hardware and cooling systems. This surge in power usage has raised operational costs, making energy efficiency a pressing issue for data center management. Virtual Machines (VMs) consolidation is a widely studied strategy to mitigate these costs by minimizing the number of active physical servers while maintaining user Service Level Agreements (SLAs). However, the success of consolidation largely depends on accurately forecasting VM resource needs. This paper presents the design and development of an energy-aware VMs allocation system that leverages predictive machine learning models to forecast future computational demands (CPU) of each VM. By anticipating these needs, the system optimally allocates VMs to available servers, effectively balancing energy savings with performance. A preliminary experimental evaluation, conducted on synthetic data, leverages predictive modeling within a server simulation to assess CPU and energy usage, demonstrating the effectiveness of energy-aware VMs allocation in optimizing resource utilization and reducing energy consumption.

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

Several parallel and distributed data mining algorithms have been proposed in literature to perform large scale data analysis, overcoming the bottleneck of traditional methods on a single machine. However, although the master-worker approach greatly simplifies the synchronization of all nodes since only the master is in charge to do that, it also presents several problematic issues for large-scale data analysis tasks (involving thousands or millions of nodes). This paper presents a hierarchical (or multi-level) master-worker framework for iterative parallel data analysis algorithms, to overcome the scalability issues affecting classic master-worker solutions. Specifically, the framework is composed of (more than one) merger and worker nodes organized in a k-tree structure, in which the workers are on the leaves and the mergers are on the root and the internal nodes in the tree.

Following a seismic event, tsunami early warning systems (TEWSs) try to provide precise forecasts of the maximum height of incoming waves at designated target points along the coast. This information is crucial to trigger early warnings in areas where the impact of tsunami waves is predicted to be dangerous (or potentially cause destruction), to help the management of the potential impact of a tsunami as well as reduce environmental destruction and losses of human lives. For such a reason, it is crucial that TEWSs produce predictions with short computation time while maintaining a high prediction accuracy. This paper presents a parallel machine learning approach, based on regression trees, to discover tsunami predictive models from simulation data. In order to achieve the results in a short time, the proposed approach relies on the parallelization of the most time consuming tasks and on incremental learning executions, in order to achieve higher performances in terms of execution time, efficiency and scalability. The experimental evaluation, performed on two real tsunami cases occurred in the Western and Eastern Mediterranean basin in 2003 and 2017, shows reasonable advantages in terms of scalability and execution time, which is an important benefit in a urgent-computing scenarios.

In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.

Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker's decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.

Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, traffic flows. In particular, the detection of city hotspots is becoming a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are valuable for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents a study about how density-based clustering algorithms are suitable for discovering urban hotspots in a city, by showing a comparative analysis of single-density and multi-density clustering on both state-of-the-art data and real-world data. The experimental evaluation shows that, in an urban scenario, multi-density clustering achieves higher quality hotspots than a single-density approach.

The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.

Summary Recent research efforts in the field of urban computing aim to develop innovative services for citizens through the application of ubiquitous and pervasive computing paradigms in urban spaces. Smart city applications need to cope with a large number of involved users and devices. Since data and objects are strictly related to the territory on which they are defined and used, it is preferable, when possible, to perform computation locally through the adoption of dispersed computing nodes such as CPU‐equipped sensors. In this context, the computation related to smart city applications can be profitably and efficiently parallelized by partitioning the territory into regions and assigning the computation related to each single region to a local node. Nevertheless, the adoption of parallel computing models poses several communication and synchronization issues, especially when the number of nodes is large and the time constraints of applications are compelling. This paper presents and analyzes a parallel computing model for smart city applications in which each node needs to exchange information only with a subset of neighbor nodes, allowing the synchronization overhead to be significantly reduced. As sample application, we consider the analysis and prediction of internet traffic generated by vehicle and pedestrian devices moving on a smart avenue equipped with distributed computing nodes. This work offers a detailed performance evaluation in a number of scenarios, including uniform and nonuniform user distribution and different types of user mobility behavior. The results show that the presented computation model offers notable advantages in terms of computation efficiency and speedup, with respect to a classical all–to‐all synchronization paradigm, in which the nodes need to coordinate with a central entity.

The steadily increasing success of Cloud Computing is causing a huge rise in its electrical power consumption, contributing to higher energy costs, as well as to the greenhouse effect and the global warming. One of the most common key strategies to reduce the power consumption of data centers is the consolidation of virtual machines, whose effectiveness strongly depends on a reliable forecasting of future computational resource needs. In fact, servers are typically configured to handle peak workload conditions even if they are often under-utilized, that results in a wastefulness of resources and inefficient energy consumption. Motivated by these issues, this paper describes a data-driven approach based on auto-regressive models to dynamically forecast virtual machine workloads, for energy-aware allocations of virtual machines on Cloud physical nodes. Virtual machine migrations across physical servers are periodically done on the basis of the estimated virtual machine demands, by minimizing the number of active servers. Experimental results show encouraging benefits in terms of energy saving, while satisfying performance constraints and service level agreement established with users.

Consolidation of virtual machines is one of the key strategies used to reduce the power consumption of Cloud data centers. For this reason it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible, while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of virtual machine resource needs. To this aim, data-driven predictive models can be exploited to develop intelligent consolidation policies. This paper describes a comparative analysis of consolidation strategies of virtual machines in Cloud systems, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. Experimental results, performed on data of a real Cloud data center, shows several insights in terms of energy saving and most efficient consolidation strategies.

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.

Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason it is extensively studied. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. Experimental results, performed on data of a real Cloud data centre, show encouraging benefits in terms of energy saving.

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.

SUMMARY In several scientific and business domains, very large data repositories are generated. To find interesting and useful information in those repositories, efficient data mining techniques and knowledge discovery processes must be used. The exploitation of data mining techniques in science helps scientists in hypothesis formation and gives them a support on their scientific practices, whereas in industrial processes, data mining can exploit existing data sources as a real value for companies that can take advantage from the knowledge that can be extracted from their large data sources. Data mining tasks are often composed by multiple stages that may be linked to each other to form various execution flows. Moreover, data mining tasks are often distributed because they involve data and tools located over geographically distributed environments. Therefore, it is fundamental to exploit effective paradigms, such as services and workflows, to model data mining tasks that are both multi‐staged and distributed. This paper discusses data mining services and workflows for analyzing scientific data in high‐performance distributed environments such as Grids and Clouds. We discuss how it is possible to define basic and complex services for supporting distributed data mining tasks in Grids. We also present a workflow formalism and a service‐oriented programming framework, named DIS3GNO, for designing and running distributed knowledge discovery processes in the Knowledge Grid system. DIS3GNO supports all the phases of a knowledge discovery process, including composition, execution, and results visualization. After introducing DIS3GNO, some relevant use cases implemented by it and a performance evaluation of the system are discussed.Copyright © 2012 John Wiley & Sons, Ltd.

The formalization and manipulation of complex and not yet assessed rules by clinicians are critical for Decision Support Systems (DSSs) performance in supporting remote monitoring of chronic patients. Sometimes, structural anomalies, such as inconsistency and redundancy, can occur. This work presents a novel system, named Consistency Checker, aimed at verifying the reliability of conditionaction clinical rules in knowledge-based DSSs. This system allows the verification of very complex rules having in their antecedent parts not only simple logical conditions, but also arithmetical expressions. Moreover, the Consistency Checker provides a new classification of the detected anomalies, aimed at fully describing the rule verification results, as well as a suitable knowledge representation formalism to encode condition-action rules in a general way. The system has been designed according to the Service Oriented Architecture and implemented as a Web Service within the CHRONIOUS Project.

Fault tolerance is an important issue in service oriented architectures like Grid and Cloud systems, where many and heterogeneous machines are used. In this paper we present a flexible failure handling framework which extends a service-oriented architecture for Distributed Data Mining previously proposed, addressing the requirements for handling fault tolerance in service-oriented Grids. In particular, two different solutions are described and experimentally evaluated on a real Grid setting, aimed at assessing their effectiveness and performance.

SUMMARY Distributed data mining implements techniques for analyzing data on distributed computing systems by exploiting data distribution and parallel algorithms. The grid is a computing infrastructure for implementing distributed high‐performance applications and solving complex problems, offering effective support to the implementation and use of data mining and knowledge discovery systems. The Web Services Resource Framework has become the standard for the implementation of grid services and applications, and it can be exploited for developing high‐level services for distributed data mining applications. This paper describes how distributed data mining patterns, such as collective learning, ensemble learning, and meta‐learning models, can be implemented as Web Services Resource Framework mining services by exploiting the grid infrastructure. The goal of this work was to design a distributed architectural model that can be exploited for different distributed mining patterns deployed as grid services for the analysis of dispersed data sources. In order to validate such an approach, we presented also the implementation of two clustering algorithms on the developed architecture. In particular, the distributed k‐means and distributed expectation maximization were exploited as pilot examples to show the suitability of the implemented service‐oriented framework. An extensive evaluation of its performance was provided. Copyright © 2011 John Wiley & Sons, Ltd.

This paper presents the design and the implementation of an architecture for the analysis of data streams in distributed environments. In particular, data stream analysis has been carried out for the computation of items and item sets that exceed a frequency threshold. The mining approach is hybrid, that is, frequent items are calculated with a single pass, using a sketch algorithm, while frequent item sets are calculated by a further multi-pass analysis. The architecture combines parallel and distributed processing to keep the pace with the rate of distributed data streams. In order to keep computation close to data, miners are distributed among the domains where data streams are generated. The paper also reports the experimental results obtained with a prototype of the architecture, tested on a Grid composed of two domains handling two different data streams.

Fault tolerance is an important issue in Grid computing, where many and heterogenous machines are used. In this paper we present a flexible failure handling framework which extends a service-oriented architecture for Distributed Data Mining previously proposed, addressing the requirements for fault tolerance in the Grid. The framework allows users to achieve failure recovery whenever a crash can occur on a Grid node involved in the computation. The implemented framework has been evaluated on a real Grid setting to assess its effectiveness and performance.

Abstract In today's Grids, files are usually managed by Grid data management systems that are superimposed on existing file and storage systems. In this paper, we analyze this predominant approach and argue that object‐based file systems can be an alternative when adapted to the characteristics of a Grid environment. We describe how we are solving the challenge of extending the object‐based storage architecture for the Grid in XtreemFS, an object‐based file system for federated infrastructures. Copyright © 2008 John Wiley & Sons, Ltd.

A parameter-free, fully-automatic approach to clustering high-dimensional categorical data is proposed. The technique is based on a two-phase iterative procedure, which attempts to improve the overall quality of the whole partition. In the first phase, cluster assignments are given, and a new cluster is added to the partition by identifying and splitting a low-quality cluster. In the second phase, the number of clusters is fixed, and an attempt to optimize cluster assignments is done. On the basis of such features, the algorithm attempts to improve the overall quality of the whole partition and finds clusters in the data, whose number is naturally established on the basis of the inherent features of the underlying data set rather than being previously specified. Furthermore, the approach is parametric to the notion of cluster quality: Here, a cluster is defined as a set of tuples exhibiting a sort of homogeneity. We show how a suitable notion of cluster homogeneity can be defined in the context of high-dimensional categorical data, from which an effective instance of the proposed clustering scheme immediately follows. Experiments on both synthetic and real data prove that the devised algorithm scales linearly and achieves nearly optimal results in terms of compactness and separation.

A co-clustering algorithm for large sparse binary data matrices, based on a greedy technique and enriched with a local search strategy to escape poor local maxima, is proposed. The algorithm starts with an initial random solution and searches for a locally optimal solution by successive transformations that improve a quality function which combines row and column means together with the size of the co-cluster. Experimental results on synthetic and real data sets show that the method is able to find significant co-clusters

We propose an incremental algorithm for clustering duplicate tuples in large databases, which allows to assign any new tuple t to the cluster containing the database tuples which are most similar to t (and hence are likely to refer to the same real-world entity t is associated with). The core of the approach is a hash-based indexing technique that tends to assign highly similar objects to the same buckets. Empirical evaluation proves that the proposed method allows to gain considerable efficiency improvement over a state-of-art index structure for proximity searches in metric spaces.