Internet of things e integrazione nel web: Web of things

Internet of Things (IoT): tre parole che sintetizzano al meglio come la tecnologia abbia pervaso quasi ogni ambito della nostra vita. In questa tesi andrò a esplorare le soluzioni hardware e soprattutto software che si celano dietro allo sviluppo di questa nuova frontiera tecnologica, dalla cui combinazione con il web nasce il Web of Things, ovvero una visione globale, accessibile da qualsiasi utente attraverso i comuni mezzi di navigazione, dei servizi che ogni singolo smart device può offrire. Sarà seguito un percorso bottom-up partendo dalla descrizione fisica dei device e delle tecnologie abilitanti alla comunicazione thing to thing ed i protocolli che instaurano fra i device le connessioni. Proseguendo per l’introduzione di concetti quali middleware e smart gateway, sarà illustrata l’integrazione nel web 2.0 di tali device menzionando durante il percorso quali saranno gli scenari applicativi e le prospettive di sviluppo auspicabili.

Deployment automatico di applicazioni specificate in linguaggio ABS

Da quando è iniziata l'era del Cloud Computing molte cose sono cambiate, ora è possibile ottenere un server in tempo reale e usare strumenti automatizzati per installarvi applicazioni. In questa tesi verrà descritto lo strumento MODDE (Model-Driven Deployment Engine), usato per il deployment automatico, partendo dal linguaggio ABS. ABS è un linguaggio a oggetti che permette di descrivere le classi in una maniera astratta. Ogni componente dichiarato in questo linguaggio ha dei valori e delle dipendenze. Poi si procede alla descrizione del linguaggio di specifica DDLang, col quale vengono espressi tutti i vincoli e le configurazioni finali. In seguito viene spiegata l’architettura di MODDE. Esso usa degli script che integrano i tool Zephyrus e Metis e crea un main ABS dai tre file passati in input, che serve per effettuare l’allocazione delle macchine in un Cloud. Inoltre verranno introdotti i due sotto-strumenti usati da MODDE: Zephyrus e Metis. Il primo si occupa di scegliere quali servizi installare tenendo conto di tutte le loro dipendenze, cercando di ottimizzare il risultato. Il secondo gestisce l’ordine con cui installarli tenendo conto dei loro stati interni e delle dipendenze. Con la collaborazione di questi componenti si ottiene una installazione automatica piuttosto efficace. Infine dopo aver spiegato il funzionamento di MODDE viene spiegato come integrarlo in un servizio web per renderlo disponibile agli utenti. Esso viene installato su un server HTTP Apache all’interno di un container di Docker.

Un'infrastruttura distribuita per l'acquisizione e la visualizzazione interattiva dei dati dell'LHC

Questo progetto di tesi è lo sviluppo di un sistema distribuito di acquisizione e visualizzazione interattiva di dati. Tale sistema è utilizzato al CERN (Organizzazione Europea per la Ricerca Nucleare) al fine di raccogliere i dati relativi al funzionamento dell'LHC (Large Hadron Collider, infrastruttura ove avvengono la maggior parte degli esperimenti condotti al CERN) e renderli disponibili al pubblico in tempo reale tramite una dashboard web user-friendly. L'infrastruttura sviluppata è basata su di un prototipo progettato ed implementato al CERN nel 2013. Questo prototipo è nato perché, dato che negli ultimi anni il CERN è diventato sempre più popolare presso il grande pubblico, si è sentita la necessità di rendere disponibili in tempo reale, ad un numero sempre maggiore di utenti esterni allo staff tecnico-scientifico, i dati relativi agli esperimenti effettuati e all'andamento dell'LHC. Le problematiche da affrontare per realizzare ciò riguardano sia i produttori dei dati, ovvero i dispositivi dell'LHC, sia i consumatori degli stessi, ovvero i client che vogliono accedere ai dati. Da un lato, i dispositivi di cui vogliamo esporre i dati sono sistemi critici che non devono essere sovraccaricati di richieste, che risiedono in una rete protetta ad accesso limitato ed utilizzano protocolli di comunicazione e formati dati eterogenei. Dall'altro lato, è necessario che l'accesso ai dati da parte degli utenti possa avvenire tramite un'interfaccia web (o dashboard web) ricca, interattiva, ma contemporaneamente semplice e leggera, fruibile anche da dispositivi mobili. Il sistema da noi sviluppato apporta miglioramenti significativi rispetto alle soluzioni precedentemente proposte per affrontare i problemi suddetti. In particolare presenta un'interfaccia utente costituita da diversi widget configurabili, riuitilizzabili che permettono di esportare i dati sia presentati graficamente sia in formato "machine readable". Un'alta novità introdotta è l'architettura dell'infrastruttura da noi sviluppata. Essa, dato che è basata su Hazelcast, è un'infrastruttura distribuita modulare e scalabile orizzontalmente. È infatti possibile inserire o rimuovere agenti per interfacciarsi con i dispositivi dell'LHC e web server per interfacciarsi con gli utenti in modo del tutto trasparente al sistema. Oltre a queste nuove funzionalità e possbilità, il nostro sistema, come si può leggere nella trattazione, fornisce molteplici spunti per interessanti sviluppi futuri.

A QoS Controller Framework Compliant with the ETSI Network Function Virtualization Specification

The 5th generation of mobile networking introduces the concept of “Network slicing”, the network will be “sliced” horizontally, each slice will be compliant with different requirements in terms of network parameters such as bandwidth, latency. This technology is built on logical instead of physical resources, relies on virtual network as main concept to retrieve a logical resource. The Network Function Virtualisation provides the concept of logical resources for a virtual network function, enabling the concept virtual network; it relies on the Software Defined Networking as main technology to realize the virtual network as resource, it also define the concept of virtual network infrastructure with all components needed to enable the network slicing requirements. SDN itself uses cloud computing technology to realize the virtual network infrastructure, NFV uses also the virtual computing resources to enable the deployment of virtual network function instead of having custom hardware and software for each network function. The key of network slicing is the differentiation of slice in terms of Quality of Services parameters, which relies on the possibility to enable QoS management in cloud computing environment. The QoS in cloud computing denotes level of performances, reliability and availability offered. QoS is fundamental for cloud users, who expect providers to deliver the advertised quality characteristics, and for cloud providers, who need to find the right tradeoff between QoS levels that has possible to offer and operational costs. While QoS properties has received constant attention before the advent of cloud computing, performance heterogeneity and resource isolation mechanisms of cloud platforms have significantly complicated QoS analysis and deploying, prediction, and assurance. This is prompting several researchers to investigate automated QoS management methods that can leverage the high programmability of hardware and software resources in the cloud.

Design, implementation and performance evaluation of an anonymous distributed simulator

La simulazione è definita come la rappresentazione del comportamento di un sistema o di un processo per mezzo del funzionamento di un altro o, alternativamente, dall'etimologia del verbo “simulare”, come la riproduzione di qualcosa di fittizio, irreale, come se in realtà, lo fosse. La simulazione ci permette di modellare la realtà ed esplorare soluzioni differenti e valutare sistemi che non possono essere realizzati per varie ragioni e, inoltre, effettuare differenti valutazioni, dinamiche per quanto concerne la variabilità delle condizioni. I modelli di simulazione possono raggiungere un grado di espressività estremamente elevato, difficilmente un solo calcolatore potrà soddisfare in tempi accettabili i risultati attesi. Una possibile soluzione, viste le tendenze tecnologiche dei nostri giorni, è incrementare la capacità computazionale tramite un’architettura distribuita (sfruttando, ad esempio, le possibilità offerte dal cloud computing). Questa tesi si concentrerà su questo ambito, correlandolo ad un altro argomento che sta guadagnando, giorno dopo giorno, sempre più rilevanza: l’anonimato online. I recenti fatti di cronaca hanno dimostrato quanto una rete pubblica, intrinsecamente insicura come l’attuale Internet, non sia adatta a mantenere il rispetto di confidenzialità, integrità ed, in alcuni, disponibilità degli asset da noi utilizzati: nell’ambito della distribuzione di risorse computazionali interagenti tra loro, non possiamo ignorare i concreti e molteplici rischi; in alcuni sensibili contesti di simulazione (e.g., simulazione militare, ricerca scientifica, etc.) non possiamo permetterci la diffusione non controllata dei nostri dati o, ancor peggio, la possibilità di subire un attacco alla disponibilità delle risorse coinvolte. Essere anonimi implica un aspetto estremamente rilevante: essere meno attaccabili, in quanto non identificabili.

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter.

Matching form with content

Successful computer-supported distance education requires that its enabling technologies are accessible and usable anywhere. They should work seamlessly inside and outside the information superhighway, wherever the target learners are located, without obtruding on the learning activity. It has long been recognised that the usability of interactive computer systems is inversely related to the visibility of the implementing technologies. Reducing the visibility of technology is especially challenging in the area of online language learning systems, which require high levels of interactivity and communication along multiple dimensions such as speaking, listening, reading and writing. In this article, the authors review the concept of invisibility as it applies to the design of interactive technologies and appliances. They describe a specialised appliance matched to the requirements for distance second language learning, and report on a successful multi-phase evaluation process, including initial field testing at a Thai open university.

Matching form with content

Successful computer-supported distance education requires that its enabling technologies are accessible and usable anywhere. They should work seamlessly inside and outside the information superhighway, wherever the target learners are located, without obtruding on the learning activity. It has long been recognised that the usability of interactive computer systems is inversely related to the visibility of the implementing technologies. Reducing the visibility of technology is especially challenging in the area of online language learning systems, which require high levels of interactivity and communication along multiple dimensions such as speaking, listening, reading and writing. In this article, the authors review the concept of invisibility as it applies to the design of interactive technologies and appliances. They describe a specialised appliance matched to the requirements for distance second language learning, and report on a successful multi-phase evaluation process, including initial field testing at a Thai open university.

Dynamic SLA management with forecasting using multi-objective optimization

Cost-efficient operation while satisfying performance and availability guarantees in Service Level Agreements (SLAs) is a challenge for Cloud Computing, as these are potentially conflicting objectives. We present a framework for SLA management based on multi-objective optimization. The framework features a forecasting model for determining the best virtual machine-to-host allocation given the need to minimize SLA violations, energy consumption and resource wasting. A comprehensive SLA management solution is proposed that uses event processing for monitoring and enables dynamic provisioning of virtual machines onto the physical infrastructure. We validated our implementation against serveral standard heuristics and were able to show that our approach is significantly better.

Mobility and bandwidth prediction in virtualized LTE systems: Architecture and challenges

Long Term Evolution (LTE) represents the fourth generation (4G) technology which is capable of providing high data rates as well as support of high speed mobility. The EU FP7 Mobile Cloud Networking (MCN) project integrates the use of cloud computing concepts in LTE mobile networks in order to increase LTE's performance. In this way a shared distributed virtualized LTE mobile network is built that can optimize the utilization of virtualized computing, storage and network resources and minimize communication delays. Two important features that can be used in such a virtualized system to improve its performance are the user mobility and bandwidth prediction. This paper introduces the architecture and challenges that are associated with user mobility and bandwidth prediction approaches in virtualized LTE systems.

Videoconference capacity Leasing on Hybrid Clouds.

This paper proposes a new methodology focused on implementing cost effective architectures on Cloud Computing systems. With this methodology the paper presents some disadvantages of systems that are based on single Cloud architectures and gives some advices for taking into account in the development of hybrid systems. The work also includes a validation of these ideas implemented in a complete videoconference service developed with our research group. This service allows a great number of users per conference, multiple simultaneous conferences, different client software (requiring transcodification of audio and video flows) and provides a service like automatic recording. Furthermore it offers different kinds of connectivity including SIP clients and a client based on Web 2.0. The ideas proposed in this article are intended to be a useful resource for any researcher or developer who wants to implement cost effective systems on several Clouds

A semantic scheduler architecture for federated hybrid clouds

Cloud computing is one the most relevant computing paradigms available nowadays. Its adoption has increased during last years due to the large investment and research from business enterprises and academia institutions. Among all the services cloud providers usually offer, Infrastructure as a Service has reached its momentum for solving HPC problems in a more dynamic way without the need of expensive investments. The integration of a large number of providers is a major goal as it enables the improvement of the quality of the selected resources in terms of pricing, speed, redundancy, etc. In this paper, we propose a system architecture, based on semantic solutions, to build an interoperable scheduler for federated clouds that works with several IaaS (Infrastructure as a Service) providers in a uniform way. Based on this architecture we implement a proof-of-concept prototype and test it with two different cloud solutions to provide some experimental results about the viability of our approach.

CumuloNimbo: Una plataforma como servicio con procesamiento transaccional altamente escalable.

El modelo de computaci¿on en la nube (cloud computing) ha ganado mucha popularidad en los últimos años, prueba de ello es la cantidad de productos que distintas empresas han lanzado para ofrecer software, capacidad de procesamiento y servicios en la nube. Para una empresa el mover sus aplicaciones a la nube, con el fin de garantizar disponibilidad y escalabilidad de las mismas y un ahorro de costes, no es una tarea fácil. El principal problema es que las aplicaciones tienen que ser rediseñadas porque las plataformas de computaci¿on en la nube presentan restricciones que no tienen los entornos tradicionales. En este artículo presentamos CumuloNimbo, una plataforma para computación en la nube que permite la ejecución y migración de manera transparente de aplicaciones multi-capa en la nube. Una de las principales características de CumuloNimbo es la gestión de transacciones altamente escalable y coherente. El artículo describe la arquitectura del sistema, así como una evaluaci¿on de la escalabilidad del mismo.

Valuation: Developer support for by-references to by-value type conversion.

Modern object oriented languages like C# and JAVA enable developers to build complex application in less time. These languages are based on selecting heap allocated pass-by-reference objects for user defined data structures. This simplifies programming by automatically managing memory allocation and deallocation in conjunction with automated garbage collection. This simplification of programming comes at the cost of performance. Using pass-by-reference objects instead of lighter weight pass-by value structs can have memory impact in some cases. These costs can be critical when these application runs on limited resource environments such as mobile devices and cloud computing systems. We explore the problem by using the simple and uniform memory model to improve the performance. In this work we address this problem by providing an automated and sounds static conversion analysis which identifies if a by reference type can be safely converted to a by value type where the conversion may result in performance improvements. This works focus on C# programs. Our approach is based on a combination of syntactic and semantic checks to identify classes that are safe to convert. We evaluate the effectiveness of our work in identifying convertible types and impact of this transformation. The result shows that the transformation of reference type to value type can have substantial performance impact in practice. In our case studies we optimize the performance in Barnes-Hut program which shows total memory allocation decreased by 93% and execution time also reduced by 15%.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.