Apesar do grau relativamente alto de maturidade existente na área de pesquisa de aprendizado supervisionado em lote, na qual são utilizados dados originários de problemas estacionários, muitas aplicações reais lidam com fluxos de dados cujas distribuições de probabilidade se alteram com o tempo, ocasionando mudanças de conceito. Diversas pesquisas vêm sendo realizadas nos últimos anos com o objetivo de criar modelos precisos mesmo na presença de mudanças de conceito. A maioria delas, no entanto, assume que tão logo um evento seja classificado pelo algoritmo de aprendizado, seu rótulo verdadeiro se torna conhecido. Este trabalho explora as situações complementares, com revisão dos trabalhos mais importantes publicados e análise do impacto de atraso na disponibilidade dos rótulos verdadeiros ou sua não disponibilização. Ainda, propõe um novo algoritmo que reduz drasticamente a complexidade de aplicação do teste de hipótese não-paramétrico Kolmogorov-Smirnov, tornado eficiente seu uso em algoritmos que analisem fluxos de dados. A exemplo, mostramos sua potencial aplicação em um método de detecção de mudança de conceito não-supervisionado que, em conjunto com técnicas de Aprendizado Ativo e Aprendizado por Transferência, reduz a necessidade de rótulos verdadeiros para manter boa performance de um classificador ao longo do tempo, mesmo com a ocorrência de mudanças de conceito. / Despite the relatively maturity of batch-mode supervised learning research, in which the data typifies stationary problems, many real world applications deal with data streams whose statistical distribution changes over time, causing what is known as concept drift. A large body of research has been done in the last years, with the objective of creating new models that are accurate even in the presence of concept drifts. However, most of them assume that, once the classification algorithm labels an event, its actual label become readily available. This work explores the complementary situations, with a review of the most important published works and an analysis over the impact of delayed true labeling, including no true label availability at all. Furthermore, this work proposes a new algorithm that heavily reduces the complexity of applying Kolmogorov- Smirnov non-parametric hypotheis test, turning it into an uselful tool for analysis on data streams. As an instantiation of its usefulness, we present an unsupervised drift-detection method that, along with Active Learning and Transfer Learning approaches, decreases the number of true labels that are required to keep good classification performance over time, even in the presence of concept drifts.

Aprender conceitos provenientes de fluxos de dados é uma tarefa significamente diferente do aprendizado tradicional em lote. No aprendizado em lote, existe uma premissa implicita que os conceitos a serem aprendidos são estáticos e não evoluem significamente com o tempo. Por outro lado, em fluxos de dados os conceitos a serem aprendidos podem evoluir ao longo do tempo. Esta evolução é chamada de mudança de conceito, e torna a criação de um conjunto fixo de treinamento inaplicável neste cenário. O aprendizado incremental é uma abordagem promissora para trabalhar com fluxos de dados. Contudo, na presença de mudanças de conceito, conceitos desatualizados podem causar erros na classificação de eventos. Apesar de alguns métodos incrementais baseados no modelo de misturas gaussianas terem sido propostos na literatura, nota-se que tais algoritmos não possuem uma política explicita de descarte de conceitos obsoletos. Nesse trabalho um novo algoritmo incremental para fluxos de dados com mudanças de conceito baseado no modelo de misturas gaussianas é proposto. O método proposto é comparado com vários algoritmos amplamente utilizados na literatura, e os resultados mostram que o algoritmo proposto é competitivo com os demais em vários cenários, superando-os em alguns casos. / Learning concepts from data streams differs significantly from traditional batch learning. In batch learning there is an implicit assumption that the concept to be learned is static and does not evolve significantly over time. On the other hand, in data stream learning the concepts to be learned may evolve over time. This evolution is called concept drift, and makes the creation of a fixed training set be no longer applicable. Incremental learning paradigm is a promising approach for learning in a data stream setting. However, in the presence of concept drifts, out dated concepts can cause misclassifications. Several incremental Gaussian mixture models methods have been proposed in the literature, but these algorithms lack an explicit policy to discard outdated concepts. In this work, a new incremental algorithm for data stream with concept drifts based on Gaussian Mixture Models is proposed. The proposed methodis compared to various algorithms widely used in the literature, and the results show that it is competitive with them invarious scenarios, overcoming them in some cases.

As técnicas de projeção multidimensional tornaram-se uma ferramenta de análise importante. Elas buscam mapear dados de um espaço multidimensional para um espaço visual, de menor dimensão, preservando as estruturas de distância ou de vizinhança no mapa visual produzido. Apesar dos recentes avanços, as técnicas existentes ainda apresentam deficiências que prejudicam a sua utilização como ferramentas exploratórias em certos domínios. Um exemplo está nos cenários streaming, nos quais os dados são produzidos e/ou coletados de forma contínua. Como a maioria das técnicas de projeção necessitam percorrer os dados mais de uma vez para produzir um layout final, e fluxos normalmente não podem ser carregados por completo em memória principal, a aplicação direta ou mesmo a adaptação das técnicas existentes em tais cenários é inviável. Nessa tese de doutorado é apresentado um novo modelo de projeção, chamado de Xtreaming, no qual as instâncias de dados são visitadas apenas uma vez durante o processo de projeção. Esse modelo é capaz de se adaptar a mudanças nos dados conforme eles são recebidos, atualizando o mapa visual para refletir as novas estruturas que surgem ao longo do tempo. Os resultados dos testes mostram que o Xtreaming é muito competitivo em termos de preservação de distâncias e tempo de execução se comparado com técnicas do estado-da-arte. Também é apresentada uma nova técnica de projeção multidimensional, chamada de User-assisted Projection Technique for Distance Information (UPDis), que foi projetada para permitir a intervenção do usuário exigindo apenas informações de distância entre as instâncias, e que é utilizada como parte do Xtreaming. Os resultados também mostram que a UPDis é tão rápida, precisa e flexível quanto as técnicas do estado-da-arte. / Multidimensional Projection techniques have become an important analytics tool. They map data from a multidimensional space into a visual space preserving the distance or neighborhood structures on the produced layout. Despite the recent advances, existing techniques still present drawbacks that impair their use as exploratory tools on certain domains. An example is the streaming scenario, in which data are captured or produced continuously. Since most projection techniques need to traverse the data more than once to produce a final layout, and streaming data typically cannot be completely loaded into the main memory, the direct use or even adaptation of the existing techniques in such scenarios is infeasible. In this dissertation, we present a novel projection model, called Xtreaming, wherein the data instances are visited only once during the projection process. This model is able to adapt itself to the changes in data as data is received, updating the visual layout to reflect the new structures that emerge over time. The tests show that Xtreaming is very competitive regarding distance preservation and running time when compared with state-of-the-art projection techniques. We also present a new multidimensional projection technique, called User-assisted Projection Technique for Distance Information (UPDis), that was designed to allow user intervention requiring only distance information between data instances. UPDis is used as part of the Xtreaming model. The results show that UPDis is as fast, accurate and flexible as state-of-the-art techniques.

In contemporary IT industry, Multi-tasking solutions are highly regarded as optimal solutions, because hardware is equipped with multi-core CPUs.  With Multi-Core technology, CPUs run with lower frequencies while giving same or better performance as a whole system of processing. This thesis work takes advantage of multi-threading architecture in order to run different tasks under different cores such as SIP signaling and messaging to establish one or more SIP calls, capture voice, medical data, and packetize them to be streamed over internet to other SIP agents. VoIP is designed to stream voice over IP. There is inter-protocol communication and cooperation such as between the SIP, SDP, RTP, and RTCP protocols in order to establish a SIP connection and- afterwards- stream media over the internet. We use the Microsoft COM technology in order to better the C++ component design. It allows us to design and develop code once and run it anywhere on different platforms. Using VC++ helps us reduce software design time and development time. Moreover, we follow software design standards setup by software engineers’ society. VoIP technology uses protocols such as the SIP signaling protocol to locate the user agents that communicate with each other. Pjsip is a library that allows developers to extend their design with SIP capability. We use the PJSIP library in order to sign up our own developed VoIP module to a SIP server over the Internet and locate other user agents. We implement and use the already-designed iRTP protocol instead of the RTP to stream media over the Internet. Thus, we can improve RTP packet delays and improve Quality of Service (QoS). Since medical data is critical and must not be lost, the iRTP guarantees no loss of medical data. If we want to stream voice only, we would not need iRTP, because RTP is a good protocol for voice applications. Due to the increasing Internet traffic, we need to use a reliable protocol that can detect packet loss of medical data. iRTP resolves the issue and leverages QoS. This thesis work focuses on streaming medical data and medical voice-calls using VoIP, even over small bandwidths and in high traffic periods. The main contribution of this thesis is in the parallel design of iRTP and the implementation of this very design in order to be used with Multi-Core technology. We do so via multi-threading technology to speed up the streaming of medical data and medical voice-calls. According to our tests, measurements, and result analyses, the parallel design of iRTP and the multithreaded implementation on VC++ leverage performance to a level where the average decrease in delay is 71.1% when using iRTP for audio and medical data instead of the nowadays applied case of using an RTP stream for audio and multiple TCPs streams for medical data .

Data stream processing in the industrial as well as in the academic field has gained more and more importance during the last years. Consider the monitoring of industrial processes as an example. There, sensors are mounted to gather lots of data within a short time range. Storing and post-processing these data may occasionally be useless or even impossible. On the one hand, only a small part of the monitored data is relevant. To efficiently use the storage capacity, only a preselection of the data should be considered. On the other hand, it may occur that the volume of incoming data is generally too high to be stored in time or–in other words–the technical efforts for storing the data in time would be out of scale. Processing data streams in the context of this thesis means to apply database operations to the stream in an on-the-fly manner (without explicitly storing the data). The challenges for this task lie in the limited amount of resources while data streams are potentially infinite. Furthermore, data stream processing must be fast and the results have to be disseminated as soon as possible. This thesis focuses on the latter issue. The goal is to provide a so-called Quality-of-Service (QoS) for the data stream processing task. Therefore, adequate QoS metrics like maximum output delay or minimum result data rate are defined. Thereafter, a cost model for obtaining the required processing resources from the specified QoS is presented. On that basis, the stream processing operations are scheduled. Depending on the required QoS and on the available resources, the weight can be shifted among the individual resources and QoS metrics, respectively. Calculating and scheduling resources requires a lot of expert knowledge regarding the characteristics of the stream operations and regarding the incoming data streams. Often, this knowledge is based on experience and thus, a revision of the resource calculation and reservation becomes necessary from time to time. This leads to occasional interruptions of the continuous data stream processing, of the delivery of the result, and thus, of the negotiated Quality-of-Service. The proposed robustness concept supports the user and facilitates a decrease in the number of interruptions by providing more resources.

Online supervised learning from fast-evolving imbalanced data streams has applications in many areas. That is, the development of techniques that are able to handle highly skewed class distributions (or 'class imbalance') is an important area of research in domains such as manufacturing, the environment, and health. Solutions should be able to analyze large repositories in near real-time and provide accurate models to describe rare classes that may appear infrequently or in bursts while continuously accommodating new instances. Although numerous online learning methods have been proposed to handle binary class imbalance, solutions suitable for multi-class streams with varying degrees of imbalance in evolving streams have received limited attention. To address this knowledge gap, the first contribution of this thesis introduces the Online Learning from Imbalanced Multi-Class Streams through Dynamic Sampling (DynaQ) algorithm for learning in such multi-class imbalanced settings. Our approach utilizes a queue-based learning method that dynamically creates an instance queue for each class. The number of instances is balanced by maintaining a queue threshold and removing older samples during training. In addition, new and rare classes are dynamically added to the training process as they appear. Our experimental results confirm a noticeable improvement in minority-class detection and classification performance. A comparative evaluation shows that the DynaQ algorithm outperforms the state-of-the-art approaches. Our second contribution in this thesis focuses on fairness-aware learning from imbalanced streams. Our work is motivated by the observation that the decisions made by online learning algorithms may negatively impact individuals or communities. Indeed, the development of approaches to handle these concerns is an active area of research in the machine learning community. However, most existing methods process the data in offline settings and are not directly suitable for online learning from evolving data streams. Further, these techniques fail to take the effects of class imbalance, on fairness-aware supervised learning into account. In addition, recent fairness-aware online learning supervised learning approaches focus on one sensitive attribute only, which may lead to subgroup discrimination. In a fair classification, the equality of fairness metrics across multiple overlapping groups must be considered simultaneously. In our second contribution, we thus address the combined problem of fairness-aware online learning from imbalanced evolving streams, while considering multiple sensitive attributes. To this end, we introduce the Multi-Sensitive Queue-based Online Fair Learning (MQ-OFL) algorithm, an online fairness-aware approach, which maintains valid and fair models over evolving streams. MQ-OFL changes the training distribution in an online fashion based on both stream imbalance and discriminatory behavior of the model evaluated over the historical stream. We compare our MQ-OFL method with state-of-art studies on real-world datasets and present comparative insights on the performance. Our final contribution focuses on explainability and interpretability in fairness-aware online learning. This research is guided by the concerns raised due to the black-box nature of models, concealing internal logic from users. This lack of transparency poses practical and ethical challenges, particularly when these algorithms make decisions in finance, healthcare, and marketing domains. These systems may introduce biases and prejudices during the learning phase by utilizing complex machine learning algorithms and sensitive data. Consequently, decision models trained on such data may make unfair decisions and it is important to realize such issues before deploying the models. To address this issue, we introduce techniques for interpreting the outcomes of fairness-aware online learning. Through a case study predicting income based on features such as ethnicity, biological sex, age, and education level, we demonstrate how our fairness-aware learning process (MQ-OFL) maintains a balance between accuracy and discrimination trade-off using global and local surrogate models.

[pt] Esta dissertação apresenta a solução de balanceamento de carga baseada em fatias de processamento de dados (Data Processing Slice Load Balancing solution) para permitir o balanceamento de carga dinâmico do processamento de fluxos de dados em sistemas baseados em DDS (Data Distribution Service). Um grande número de aplicações requer o processamento contínuo de alto volume de dados oriundos de várias fontes distribuídas., tais como monitoramento de rede, sistemas de engenharia de tráfego, roteamento inteligente de carros em áreas metropolitanas, redes de sensores, sistemas de telecomunicações, aplicações financeiras e meteorologia. Conceito chave da solução proposta é o Data Processing Slice, o qual é a unidade básica da carga de processamento dos dados dos nós servidores em um domínio DDS. A solução consiste de um nó balanceador, o qual é responsável por monitorar a carga atual de um conjunto de nós processadores homogêneos e quando um desbalanceamento de carga é detectado, coordenar ações para redistribuir entre os nós processadores algumas fatias de carga de trabalho de forma segura. Experimentos feitos com grandes fluxos de dados que demonstram a baixa sobrecarga, o bom desempenho e a confiabilidade da solução apresentada. / [en] This thesis presents the Data Processing Slice Load Balancing solution to enable dynamic load balancing of Data Stream Processing on DDS-based systems (Data Distribution Service). A large number of applications require continuous and timely processing of high-volume of data originated from many distributed sources, such as network monitoring, traffic engineering systems, intelligent routing of cars in metropolitan areas, sensor networks, telecommunication systems, financial applications and meteorology. The key concept of the proposed solution is the Data Processing Slice (DPS), which is the basic unit of data processing load of server nodes in a DDS Domain. The Data Processing Slice Load Balancing solution consists of a load balancer, which is responsible for monitoring the current load of a set of homogenous data processing nodes and when a load unbalance is detected, it coordinates the actions to redistribute some data processing slices among the processing nodes in a secure way. Experiments with large data stream have demonstrated the low overhead, good performance and the reliability of the proposed solution.

Aprendizado em fluxo de dados é uma área de pesquisa importante e que vem crescendo nos últimos tempos. Em muitas aplicações reais os dados são gerados em uma sequência temporal potencialmente infinita. O processamento em fluxo possui como principal característica a necessidade por respostas que atendam restrições severas de tempo e memória. Por exemplo, um classificador aplicado a um fluxo de dados deve prover uma resposta a um determinado evento antes que o próximo evento ocorra. Caso isso não ocorra, alguns eventos do fluxo podem ficar sem classificação. Muitos fluxos geram eventos em uma taxa de chegada com grande variabilidade, ou seja, o intervalo de tempo de ocorrência entre dois eventos sucessivos pode variar muito. Para que um sistema de aprendizado obtenha sucesso na aquisição de conhecimento é preciso que ele apresente duas características principais: (i) ser capaz de prover uma classificação para um novo exemplo em tempo hábil e (ii) ser capaz de adaptar o modelo de classificação de maneira a tratar mudanças de conceito, uma vez que os dados podem não apresentar uma distribuição estacionária. Algoritmos de aprendizado de máquina em lote não possuem essas propriedades, pois assumem que as distribuições são estacionárias e não estão preparados para atender restrições de memória e processamento. Para atender essas necessidades, esses algoritmos devem ser adaptados ao contexto de fluxo de dados. Uma possível adaptação é tornar o algoritmo de classificação anytime. Algoritmos anytime são capazes de serem interrompidos e prover uma resposta (classificação) aproximada a qualquer instante. Outra adaptação é tornar o algoritmo incremental, de maneira que seu modelo possa ser atualizado para novos exemplos do fluxo de dados. Neste trabalho é realizada a investigação de dois métodos capazes de realizar o aprendizado em um fluxo de dados. O primeiro é baseado no algoritmo k-vizinhos mais próximo anytime estado-da-arte, onde foi proposto um novo método de desempate para ser utilizado neste algoritmo. Os experimentos mostraram uma melhora consistente no desempenho deste algoritmo em várias bases de dados de benchmark. O segundo método proposto possui as características dos algoritmos anytime e é capaz de tratar a mudança de conceito nos dados. Este método foi chamado de Algoritmo Anytime Incremental e possui duas versões, uma baseado no algoritmo Space Saving e outra em uma Janela Deslizante. Os experimentos mostraram que em cada fluxo cada versão deste método proposto possui suas vantagens e desvantagens. Mas no geral, comparado com outros métodos baselines, ambas as versões apresentaram melhor desempenho. / Data stream learning is a very important research field that has received much attention from the scientific community. In many real-world applications, data is generated as potentially infinite temporal sequences. The main characteristic of stream processing is to provide answers observing stringent restrictions of time and memory. For example, a data stream classifier must provide an answer for each event before the next one arrives. If this does not occur, some events from the data stream may be left unclassified. Many streams generate events with highly variable output rate, i.e. the time interval between two consecutive events may vary greatly. For a learning system to be successful, two properties must be satisfied: (i) it must be able to provide a classification for a new example in a short time and (ii) it must be able to adapt the classification model to treat concept change, since the data may not follow a stationary distribution. Batch machine learning algorithms do not satisfy those properties because they assume that the distribution is stationary and they are not prepared to operate with severe memory and processing constraints. To satisfy these requirements, these algorithms must be adapted to the data stream context. One possible adaptation is to turn the algorithm into an anytime classifier. Anytime algorithms may be interrupted and still provide an approximated answer (classification) at any time. Another adaptation is to turn the algorithm into an incremental classifier so that its model may be updated with new examples from the data stream. In this work, it is performed an evaluation of two approaches for data stream learning. The first one is based on a state-of-the-art k-nearest neighbor anytime classifier. A new tiebreak approach is proposed to be used with this algorithm. Experiments show consistently better results in the performance of this algorithm in many benchmark data sets. The second proposed approach is to adapt the anytime algorithm for concept change. This approach was called Incremental Anytime Algorithm, and it was designed with two versions. One version is based on the Space Saving algorithm and the other is based in a Sliding Window. Experiments show that both versions are significantly better than baseline approaches.

Tratar grandes quantidades de dados é uma exigência dos modernos algoritmos de mineração de texto. Para algumas aplicações, documentos são constantemente publicados, o que demanda alto custo de armazenamento em longo prazo. Então, é necessário criar métodos de fácil adaptação para uma abordagem que considere documentos em fluxo, e que analise os dados em apenas um passo sem requerer alto custo de armazenamento. Outra exigência é a de que essa abordagem possa explorar heurísticas a fim de melhorar a qualidade dos resultados. Diversos modelos para a extração automática das informações latentes de uma coleção de documentos foram propostas na literatura, dentre eles destacando-se os modelos probabilísticos de tópicos. Modelos probabilísticos de tópicos apresentaram bons resultados práticos, sendo estendidos para diversos modelos com diversos tipos de informações inclusas. Entretanto, descrever corretamente esses modelos, derivá-los e em seguida obter o apropriado algoritmo de inferência são tarefas difíceis, exigindo um tratamento matemático rigoroso para as descrições das operações efetuadas no processo de descoberta das dimensões latentes. Assim, para a elaboração de um método simples e eficiente para resolver o problema da descoberta das dimensões latentes, é necessário uma apropriada representação dos dados. A hipótese desta tese é a de que, usando a representação de documentos em grafos bipartidos, é possível endereçar problemas de aprendizado de máquinas, para a descoberta de padrões latentes em relações entre objetos, por exemplo nas relações entre documentos e palavras, de forma simples e intuitiva. Para validar essa hipótese, foi desenvolvido um arcabouço baseado no algoritmo de propagação de rótulos utilizando a representação em grafos bipartidos. O arcabouço, denominado PBG (Propagation in Bipartite Graph), foi aplicado inicialmente para o contexto não supervisionado, considerando uma coleção estática de documentos. Em seguida, foi proposta uma versão semissupervisionada, que considera uma pequena quantidade de documentos rotulados para a tarefa de classificação transdutiva. E por fim, foi aplicado no contexto dinâmico, onde se considerou fluxo de documentos textuais. Análises comparativas foram realizadas, sendo que os resultados indicaram que o PBG é uma alternativa viável e competitiva para tarefas nos contextos não supervisionado e semissupervisionado. / Handling large amounts of data is a requirement for modern text mining algorithms. For some applications, documents are published constantly, which demand a high cost for long-term storage. So it is necessary easily adaptable methods for an approach that considers documents flow, and be capable of analyzing the data in one step without requiring the high cost of storage. Another requirement is that this approach can exploit heuristics in order to improve the quality of results. Several models for automatic extraction of latent information in a collection of documents have been proposed in the literature, among them probabilistic topic models are prominent. Probabilistic topic models achieve good practical results, and have been extended to several models with different types of information included. However, properly describe these models, derive them, and then get appropriate inference algorithms are difficult tasks, requiring a rigorous mathematical treatment for descriptions of operations performed in the latent dimensions discovery process. Thus, for the development of a simple and efficient method to tackle the problem of latent dimensions discovery, a proper representation of the data is required. The hypothesis of this thesis is that by using bipartite graph for representation of textual data one can address the task of latent patterns discovery, present in the relationships between documents and words, in a simple and intuitive way. For validation of this hypothesis, we have developed a framework based on label propagation algorithm using the bipartite graph representation. The framework, called PBG (Propagation in Bipartite Graph) was initially applied to the unsupervised context for a static collection of documents. Then a semi-supervised version was proposed which need only a small amount of labeled documents to the transductive classification task. Finally, it was applied in the dynamic context in which flow of textual data was considered. Comparative analyzes were performed, and the results indicated that the PBG is a viable and competitive alternative for tasks in the unsupervised and semi-supervised contexts.

This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification. Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets' correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model's prediction performance, while reducing computational complexity. Under the multi-instance paradigm, a novel SVM multiple-instance formulation and an algorithm with a bag-representative selector, named Multi-Instance Representative SVM (MIRSVM), are presented. The contribution trains the SVM based on bag-level information and is able to identify instances that highly impact classification, i.e. bag-representatives, for both positive and negative bags, while finding the optimal class separation hyperplane. Unlike other multi-instance SVM methods, this approach eliminates possible class imbalance issues by allowing both positive and negative bags to have at most one representative, which constitute as the most contributing instances to the model. Due to the shortcomings of current popular SVM solvers, especially in the context of large-scale learning, the third contribution presents a novel stochastic, i.e. online, learning algorithm for solving the L1-SVM problem in the primal domain, dubbed OnLine Learning Algorithm using Worst-Violators (OLLAWV). This algorithm, unlike other stochastic methods, provides a novel stopping criteria and eliminates the need for using a regularization term. It instead uses early stopping. Because of these characteristics, OLLAWV was proven to efficiently produce sparse models, while maintaining a competitive accuracy. OLLAWV's online nature and success for traditional classification inspired its implementation, as well as its predecessor named OnLine Learning Algorithm - List 2 (OLLA-L2), under the batch data stream classification setting. Unlike other existing methods, these two algorithms were chosen because their properties are a natural remedy for the time and memory constraints that arise from the data stream problem. OLLA-L2's low spacial complexity deals with memory constraints imposed by the data stream setting, and OLLAWV's fast run time, early self-stopping capability, as well as the ability to produce sparse models, agrees with both memory and time constraints. The preliminary results for OLLAWV showed a superior performance to its predecessor and was chosen to be used in the final set of experiments against current popular data stream methods. Rigorous experimental studies and statistical analyses over various metrics and datasets were conducted in order to comprehensively compare the proposed solutions against modern, widely-used methods from all paradigms. The experimental studies and analyses confirm that the proposals achieve better performances and more scalable solutions than the methods compared, making them competitive in their respected fields.