Massive Online Analysis Research Articles

An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift.

A challenge to many real-world data streams is imbalance with concept drift, which is one of the most critical tasks in anomaly detection. Learning nonstationary data streams for anomaly detection has been well studied in recent years. However, most of the researches assume that the class of data streams is relatively balanced. Only a few approaches tackle the joint issue of imbalance and concept drift. To overcome this joint issue, we propose an ensemble learning method with generative adversarial network-based sampling and consistency check (EGSCC) in this paper. First, we design a comprehensive anomaly detection framework that includes an oversampling module by generative adversarial network, an ensemble classifier, and a consistency check module. Next, we introduce double encoders into GAN to better capture the distribution characteristics of imbalanced data for oversampling. Then, we apply the stacking ensemble learning to deal with concept drift. Four base classifiers of SVM, KNN, DT and RF are used in the first layer, and LR is used as meta classifier in second layer. Last but not least, we take consistency check of the incremental instance and check set to determine whether it is anormal by statistical learning, instead of threshold-based method. And the validation set is dynamic updated according to the consistency check result. Finally, three artificial data sets obtained from Massive Online Analysis platform and two real data sets are used to verify the performance of the proposed method from four aspects: detection performance, parameter sensitivity, algorithm cost and anti-noise ability. Experimental results show that the proposed method has significant advantages in anomaly detection of imbalanced data streams with concept drift.

A Comparative Analysis of Data Stream Clustering Algorithms

This study compares the performance of stream clustering algorithms (DenStream, CluStream, ClusTree) on Massive Online Analysis (MOA) using synthetic and real-world datasets. The algorithms are compared in the presence on noise level [0%, 10%, 30%] on the synthetic data. DenStream epsilon parameter was tune to 0.01 and 0.03 to improve its performance. We use the performance evaluation metrics CMM, F1-P, F1-R, Purity, Silhouette Coefficient, and Rand statistic. On synthetic data, our results show that ClusTree outperformed CluStream and DenStream on the almost all the metrics except in Purity and Silhouette were DenStream performs better at noise levels (10% and 30%). ClusTree outperform CluStream and DenStream on Forest Cover type dataset on metrics CMM, F1-P, F1-R, Silhouette Coefficient, and Rand statistic with 90%, 74%, 77% and 89% respectively. However, the tune DenStream epsilon parameter shows some improvements. On electricity data, DenStream outperform CluStream and ClusTree at epsilon parameter (0.03 and 0.05) on metrics F1-P, F1-R, and Purity. The investigation of DenStream epsilon parameter (0.03 and 0.05) on RandomBRF Generator with noise level [0%, 10%, 30%] shows that DenStream with epsilon 0.03 outperform other parameter adjustment.

Proactive Ransomware Detection Using Extremely Fast Decision Tree (EFDT) Algorithm: A Case Study

Several malware variants have attacked systems and data over time. Ransomware is among the most harmful malware since it causes huge losses. In order to get a ransom, ransomware is software that locks the victim’s machine or encrypts his personal information. Numerous research has been conducted to stop and quickly recognize ransomware attacks. For proactive forecasting, artificial intelligence (AI) techniques are used. Traditional machine learning/deep learning (ML/DL) techniques, however, take a lot of time and decrease the accuracy and latency performance of network monitoring. In this study, we utilized the Hoeffding trees classifier as one of the stream data mining classification techniques to detect and prevent ransomware attacks. Three Hoeffding trees classifier algorithms are selected to be applied to the Resilient Information Systems Security (RISS) research group dataset. After configuration, Massive Online Analysis (MOA) software is utilized as a testing framework. The results of Hoeffding tree classifier algorithms are then assessed to choose the enhanced model with the highest accuracy and latency performance. In conclusion, the 99.41% classification accuracy was the highest result achieved by the EFDT algorithm in 66 ms.

An online course about cosmetics improves skin care practices and skin health.

BackgroundThe incidence of cosmetics-associated dermatoses is on the rise recently while the awareness and knowledge about proper utilization of cosmetics are insufficient in both the public and specialists due to limited education about cosmetics.MethodsOur aim was to build and enhance the public's ability to select appropriate cosmetics, manage possible dermatoses and improve skin-care practices and skin health by offering access to medical information via free online courses. Consequently, we launched a massive open online course (MOOC), Appreciation and Analysis of Cosmetics. An online questionnaire was also sent to evaluate the effectiveness of the course.ResultsNearly 540,000 learners were enrolled in the course since 2014. In the discussion forum, there were 8,383 posts and 73,014 replies in total, where learners were mostly concerned about topics of sun protection, cleansing and proper utilization of cosmetics in some skin diseases. 645 learners answered the questionnaire with 88.84% of the them changed skin care practices and 50.39% reported improvement in skin health. Moreover, participants who completed the course reported better understanding and utilization of cosmetics-related knowledge (p values < 0.05). 72.09% of respondents were willing to recommend this course to others.ConclusionsFree online public courses are feasible for conducting public health education campaigns related to cosmetics and associated dermatology to lower the incidence of cosmetics-associated dermatoses.

Adversarial Attack by Inducing Drift in Streaming Data

Due to the innovation in technology, various techniques and tools are used to generate, collect, and store the data arriving at high speed and in huge volume. Stream data is the well-ordered sequence of items that arrive in a timely manner. The various sources of stream data are mobile devices, sensor nodes, network monitoring, traffic management, weblogs, etc. This paper aims at mining data stream with and without concept drift using the supervised machine learning technique in massive online analysis (MOA) frame work. The existing challenges in handling the stream data are improving the efficiency of the learners, effective memory management, reducing time taken and the way of handling the drifted data. The intrusion datasets are streamed using MOA framework and classification is performed using Naive Bayes, hoeffding tree, and ensemble algorithms, with and without drifted data. It is found that ensemble algorithm outperforms the other similar algorithms.

Testing of Streaming Data Clustering Algorithm Effectiveness

This article describes the task of streaming data clustering. The task of streaming data processing becomes more and more urgent with the device number increase that produces and process new data. Such devices create endless streams of data at tremendous speed. This article gives the examples of such data streams and the rationale for their processingneed. Cluster flow analysis algorithms differ from classical algorithms due to RAM limitations of a computing device. Both artificial data sets and experimental observations were chosen for stream algorithm testing. The data of chemical gas sensors, as well as information about network connections in the local network, were chosen as such observations. Means and tools were chosen for comparisons between the algorithms. For these purposes, the WEKA and Massive Online Analysis software packages were selected. The article describes the process of working with this software. The data preprocessing process is demonstrated using WEKA. Several algorithms have been tested working with data streams. Clustering results were evaluated using an external quality measure. At the end of the work, they presentedthe graphs of this indicator changes during flow clustering.

Using spectral entropy and bernoulli map to handle concept drift

Data stream mining is a relevant task to extract information from large amounts of data that continuously evolve over time. In this context, learning algorithms may combine a classifier and a drift detector to identify changes in the distribution of the predictions error in order to rapidly adapt or replace the predictive model. Several proposals have been presented in the literature for the detection of concept changes based on the error rate of the predictive models. In general, the error rate distribution grounds most of the approaches based on sequential analysis and statistical process control, or by monitoring distributions using sliding windows, which assume the prediction errors are generated independently. However, empirical studies have shown that the error rate can be influenced by temporal dependence. In addition, new approaches considering dynamical system tools have been proposed for concept drift detection in unsupervised scenarios containing temporal dependencies. Motivated by these approaches, this article proposes the Spectral Entropy Drift Detector (SEDD), which is based on Spectral Entropy, Bernoulli Map and on the surrogate stability concept. Experimental results using abrupt and gradual concept drift versions of different dataset generators as well as real-world data streams, run in the Massive Online Analysis (MOA) framework, suggest that SEDD was competitive with the state-of-the-art methods, especially when considering accuracy and false alarms.

RETRACTED ARTICLE: Comprehensive analysis for class imbalance data with concept drift using ensemble based classification

In many information system applications, the environment is dynamic and tremendous amount of streaming data is generated. This scenario enforces additional computational demand on the algorithm to process incoming instances incrementally using restricted memory and time compared to static data mining. Moreover, when the streams of data are collected from different sources, it may exhibit concept drift, which means the variation in the distribution of data and it can have a high degree of class imbalance. The problem of class imbalance occurs when there is a much lower number of an example representing one class than those of the other class. Concept drift and imbalanced streaming data are commonly found in real-world applications such as fraud detection, intrusion detection, decision support system and disease prediction. In this paper, the different concept drift detectors and handling approaches are analysed when dealing with imbalance data. A comparative analysis of concept drift is performed on various data sets like SEA synthetic data stream and real world datasets. Massive Online Analysis (MOA) tool is used to make the comparative study about different learners in a concept drifting environment. The performance measure such as Accuracy, Precision, Recall, F1-score and Kappa statistic has been used to evaluate the performance of the various learners on SEA synthetic data stream and real world dataset. Ensemble classifiers and single learners are employed and tested on the data samples of SEA synthetic data stream, electrical and KDD intrusion data set. The ensemble classifiers provide better accuracy when compared to the single classifier and ensemble based methods has shown good performance compared to strong single learners when dealing with concept drift and class imbalance data.

Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems.

With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments.

MOAManager: A tool to support data stream experiments

SummaryThis short communication describes Massive Online Analysis Manager Hypertext Preprocessor framework (MOAManager), an open‐source web tool that assists in the creation, execution, extraction, and formatting of experiments in online environments using the MOA framework. MOAManager also brings together a set of features for data analysis, including the calculation of metrics and statistical tests for performance evaluation. The purpose of the tool is to provide a structure that automates several repetitive experimental steps, helping the users to customize, run, and analyze the experiments quickly and reliably, enabling gains in productivity.

MOAFS: A Massive Online Analysis library for feature selection in data streams

MOAFS: A Massive Online Analysis library for feature selection in data streams

Mining big data streams using business analytics tools: a bird's eye view on MOA and SAMOA

Big data evolves as the prominent field in modern computing era. Big data analytics and its impact on extracting business intelligence is becoming indispensable for plethora of applications. The non-proprietary software revolution paved the way for illustrious evolution of tools like Weka, rapid miner, orange and R. Traditional data mining techniques hardly adapts to the requirements of rapid data analysis. The data stream processing algorithms that handle multitude of data endow with greater challenge in real time. Big data mining requires further improvisation in traditional tools to address the challenges of massive data processing. This paper highlights the importance of data stream mining and explores two important open source frameworks, namely massive online analysis (MOA) and scalable advanced massive online analysis (SAMOA). The implications of both the tools augurs well for further deliberations in big data research community. Business information system (BIS) models can reach unprecedented heights with the proliferation of these business analytics tools.

Estimation of regression-based model with bulk noisy data

&lt;span&gt;The bulk noise has been provoking a contributed data due to a communication network with a tremendously low signal to noise ratio. An appreciated method for revising massive noise of individuals through information theory is widely discussed. One of the practical applications of this approach for bulk noise estimation is analyzed using intelligent automation and machine learning tools, dealing the case of bulk noise existence or nonexistence. A regression-based model is employed for the investigation and experiment. Estimation for the practical case with bulk noisy datasets is proposed. The proposed method applies slice-and-dice technique to partition a body of datasets down into slighter portions so that it can be carried out. The average error, correlation, absolute error and mean square error are computed to validate the estimation. Results from massive online analysis will be verified with data collected in the following period. In many cases, the prediction with bulk noisy data through MOA simulation reveals Random Imputation minimizes the average error.&lt;/span&gt;

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

&lt;span&gt;Multiple Regression-Based Prediction (MRBP) is an emerging calculation to or analysis technique cope with the future by compiling the history of data. The MRBP characteristic will include an approximation for the associations between physical observations and predictions. MRBP is a predictive model, which will be an important source of knowledge in terms of an interesting trend to be followed in the future. However, there is impairment in the MRBP dataset, wherein each form of missing and noisy data has caused an error and is unavailable further analysis. To overcome this unavailability, so that the data analytics can be moved on, two treatment approaches are introduced. First, the given dataset is denoised; next, listwise deletion (LD) is proposed to handle the missing data. The performance of the proposed technique will be investigated by dealing with datasets that cannot be executed. Employing the Massive Online Analysis (MOA) software, the proposed model is investigated, and the results are summarized. Performance metrics, such as mean squared error (MSE), correlation coefficient (COEF), mean absolute error (MAE), root mean squared error (RMSE), and the average error percentage, are used to validate the proposed mechanism. The proposed LD projection is confirmed through actual values. The proposed LD outperforms other treatments as it only requires less state space, which reflects low computation cost, and proves its capability to overcome the limitation of analysis.&lt;/span&gt;

Use of Sentiment Analysis for Predicting Public Opinion on Referendum: A Feasibility Study

ABSTRACTThis study explored the potential of using sentiment analysis of tweets to predict referendum choices (Brexit). The feasibility of using StreamKM++ in the massive online analysis framework was examined over five categories, ranging from strongly agree to strongly disagree (to exit). A Naïve Bayes classifier was used to classify people’s opinions according to these categories. The prediction model resulted in high accuracy (97.98%), making it possible to use it in predicting opinions about public events and issues. The findings from this study may help practitioners, and policymakers understand the importance of sentiment analysis of social media in assessing public opinion and, accordingly, making certain voting predictions.

An Approximation Method of Parallel Processing System with Big Data

Big data has been amassed and observed at bursting dimensions. The immense capacity of unstructured data from digital society has intensified at an unimaginable bound, motivating processing power and data pre-processing. The conventional simulations to manipulate gigantic data can be problematic as they cannot be practically used. The decision-maker is experiencing these problems from all activities in the workplace community, enterprise, and scientific experiments. The troubles arise from the process of data collection, the way to prepare a suitable storage space, and the improvement of processing speed. These burdens are growing for big data processing. Besides, there are some points such as data conflict, severance, confidentiality, extensibility, and time-series. Parallel processing system is a key to quantify metrics, and to investigate time-series. Recently developed simulation, so called Massive Online Analysis, gives results from multiple datasets and the results align with ones from the approximation. Cost-effective and speedup metrics are examined to confirm the performance of parallel processing system.

Mining Big data streams using Business analytics tools: A bird

Big data evolves as the prominent field in modern computing era. Big data analytics and its impact on extracting business intelligence is becoming indispensable for plethora of applications. The non-proprietary software revolution paved the way for illustrious evolution of tools like Weka, rapid miner, orange and R. Traditional data mining techniques hardly adapts to the requirements of rapid data analysis. The data stream processing algorithms that handle multitude of data endow with greater challenge in real time. Big data mining requires further improvisation in traditional tools to address the challenges of massive data processing. This paper highlights the importance of data stream mining and explores two important open source frameworks, namely massive online analysis (MOA) and scalable advanced massive online analysis (SAMOA). The implications of both the tools augurs well for further deliberations in big data research community. Business information system (BIS) models can reach unprecedented heights with the proliferation of these business analytics tools.

GOOWE

Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is a well-known solution. It is possible that a subset of classifiers in the ensemble outperforms others in a time-varying fashion. However, optimum weight assignment for component classifiers is a problem, which is not yet fully addressed in online evolving environments. We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances. We map vote scores of individual classifiers and true class labels into a spatial environment. Based on the Euclidean distance between vote scores and ideal-points, and using the linear least squares (LSQ) solution, we present a novel, dynamic, and online weighting approach. While LSQ is used for batch mode ensemble classifiers, it is the first time that we adapt and use it for online environments by providing a spatial modeling of online ensembles. In order to show the robustness of the proposed algorithm, we use real-world datasets and synthetic data generators using the Massive Online Analysis (MOA) libraries. First, we analyze the impact of our weighting system on prediction accuracy through two scenarios. Second, we compare GOOWE with eight state-of-the-art ensemble classifiers in a comprehensive experimental environment. Our experiments show that GOOWE provides improved reactions to different types of concept drift compared to our baselines. The statistical tests indicate a significant improvement in accuracy, with conservative time and memory requirements.

Towards the adaptation of SDC methods to stream mining

Most of the existing statistical disclosure control (SDC) standards, such as k-anonymity or l-diversity, were initially designed for static data. Therefore, they cannot be directly applied to stream data which is continuous, transient, and usually unbounded. Moreover, in streaming applications, there is a need to offer strong guarantees on the maximum allowed delay between incoming data and its corresponding anonymous output. In order to full-fill with these requirements, in this paper, we present a set of modifications to the most standard SDC methods, efficiently implemented within the Massive Online Analysis (MOA) stream mining framework. Besides, we have also developed a set of performance metrics to evaluate Information Loss and Disclosure Risk values continuously. Finally, we also show the efficiency of our new methods with a large set of experiments.

Stream Data Mining: Platforms, Algorithms, Performance Evaluators and Research Trends

Streaming data are potentially infinite sequence of incoming data at very high speed and may evolve over the time. This causes several challenges in mining large scale high speed data streams in real time. Hence, this field has gained a lot of attention of researchers in previous years. This paper discusses various challenges associated with mining such data streams. Several available stream data mining algorithms of classification and clustering are specified along with their key features and significance. Also, the significant performance evaluation measures relevant in streaming data classification and clustering are explained and their comparative significance is discussed. The paper illustrates various streaming data computation platforms that are developed and discusses each of them chronologically along with their major capabilities. This paper clearly specifies the potential research directions open in high speed large scale data stream mining from algorithmic, evolving nature and performance evaluation measurement point of view. Finally, Massive Online Analysis (MOA) framework is used as a use case to show the result of key streaming data classification and clustering algorithms on the sample benchmark dataset and their performances are critically compared and analyzed based on the performance evaluation parameters specific to streaming data mining.