Recently, skyline query processing over data stream has gained a lot of attention especially from the database community owing to its own unique challenges. Skyline queries aims at pruning a search ...space of a potential large multi-dimensional set of objects by keeping only those objects that are not worse than any other. Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. In regard to this, this paper provides a comparative study on the state-of-the-art approaches over the period between 2000 and 2022 with the main aim to help readers understand the key issues which are essential to consider in relation to processing skyline queries over streaming data. Seven digital databases were reviewed in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) procedures. After applying both the inclusion and exclusion criteria, 23 primary papers were further examined. The results show that the identified skyline approaches are driven by the need to expedite the skyline query processing mainly due to the fact that data streams are time varying (time sensitive), continuous, real time, volatile, and unrepeatable. Although, these skyline approaches are tailored made for data stream with a common aim, their solutions vary to suit with the various aspects being considered, which include the type of skyline query, type of streaming data, type of sliding window, query processing technique, indexing technique as well as the data stream environment employed. In this paper, a comprehensive taxonomy is developed along with the key aspects of each reported approach, while several open issues and challenges related to the topic being reviewed are highlighted as recommendation for future research direction.
Network security has always been a concern because it remains to be an unresolved problem. Unlike signature-based methods, anomaly-based methods can detect novel attacks and thus have gained ...increasing attention over the past decades. However, as the huge and unbounded network data samples continuously arrive at an unprecedented rate and always evolve and change, building a precise network normal pattern has become extremely difficult. In this study, an evolving anomaly detection method for network streaming data is proposed. Clusters are incrementally updated as the new network samples arrive at the incremental updating phase. The outliers, which include not only the global outliers but also the local outliers, are detected using the local density and global density thresholds at the anomaly detection phase. Meanwhile, a buffer is used to store temporary outliers, which may subsequently become normal samples, to avoid normal network samples being deleted as outliers.
Three prominent streaming data (packet-based KDDCUP’99, NSL_KDD, and flow-based CIDDS-001) are used to validate the proposed algorithm. The detection rate of the proposed algorithm can achieve the best result. The result is nearly 100% on KDDCUP’99 and CIDDS-001. The false positive rate and accuracy are 0.0125 and 0.9886 on CIDDS-001, respectively. Experimental results indicate that the proposed algorithm can process real-time network anomaly detection with a much lower time and memory computational cost, and it outperforms other unsupervised anomaly detection methods and most supervised anomaly detection methods reported in the literature in terms of detection rate, false-positive rate, and detection accuracy.
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•Lightweight and flexible extension of online ensembles with abstaining classifiers.•Dynamic selection of base classifiers to exploit their underlying diversity.•Improved recovery due ...to promoting classifiers correctly anticipating concept drifts.•Increased robustness to the presence of noise in data streams.•Thorough experimental study with analysis on 12 canonical and 120 noisy streams.
Mining data streams is among most vital contemporary topics in machine learning. Such scenario requires adaptive algorithms that are able to process constantly arriving instances, adapt to potential changes in data, use limited computational resources, as well as be robust to any atypical events that may appear. Ensemble learning has proven itself to be an effective solution, as combining learners leads to an improved predictive power, more flexible drift handling, as well as ease of being implemented in high-performance computing environments. In this paper, we propose an enhancement of popular online ensembles by augmenting them with abstaining option. Instead of relying on a traditional voting, classifiers are allowed to abstain from contributing to the final decision. Their confidence level is being monitored for each incoming instance and only learners that exceed certain threshold are selected. We introduce a dynamic and self-adapting threshold that is able to adapt to changes in the data stream, by monitoring outputs of the ensemble and allowing to exploit underlying diversity in order to efficiently anticipate drifts. Additionally, we show that forcing uncertain classifiers to abstain from making a prediction is especially useful for noisy data streams. Our proposal is a lightweight enhancement that can be applied to any online ensemble method, improving its robustness to drifts and noise. Thorough experimental analysis validated through statistical tests proves the usefulness of the proposed approach.
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Most stream classifiers are designed to process data incrementally, run in resource-aware environments, and react to concept drifts, i.e., unforeseen changes of the stream’s underlying data ...distribution. Ensemble classifiers have become an established research line in this field, mainly due to their modularity which offers a natural way of adapting to changes. However, in environments where class labels are available after each example, ensembles which process instances in blocks do not react to sudden changes sufficiently quickly. On the other hand, ensembles which process streams incrementally, do not take advantage of periodical adaptation mechanisms known from block-based ensembles, which offer accurate reactions to gradual and incremental changes. In this paper, we analyze if and how the characteristics of block and incremental processing can be combined to produce new types of ensemble classifiers. We consider and experimentally evaluate three general strategies for transforming a block ensemble into an incremental learner: online component evaluation, the introduction of an incremental learner, and the use of a drift detector. Based on the results of this analysis, we put forward a new incremental ensemble classifier, called Online Accuracy Updated Ensemble, which weights component classifiers based on their error in constant time and memory. The proposed algorithm was experimentally compared with four state-of-the-art online ensembles and provided best average classification accuracy on real and synthetic datasets simulating different drift scenarios.
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Twitter is among the fastest‐growing microblogging and online social networking services. Messages posted on Twitter (tweets) have been reporting everything from daily life stories to the latest ...local and global news and events. Monitoring and analyzing this rich and continuous user‐generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. This article provides a survey of techniques for event detection from Twitter streams. These techniques aim at finding real‐world occurrences that unfold over space and time. In contrast to conventional media, event detection from Twitter streams poses new challenges. Twitter streams contain large amounts of meaningless messages and polluted content, which negatively affect the detection performance. In addition, traditional text mining techniques are not suitable, because of the short length of tweets, the large number of spelling and grammatical errors, and the frequent use of informal and mixed language. Event detection techniques presented in literature address these issues by adapting techniques from various fields to the uniqueness of Twitter. This article classifies these techniques according to the event type, detection task, and detection method and discusses commonly used features. Finally, it highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features.
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Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important ...challenges in learning from data streams is reacting to concept drift, i.e., unforeseen changes of the stream's underlying data distribution. Several classification algorithms that cope with concept drift have been put forward, however, most of them specialize in one type of change. In this paper, we propose a new data stream classifier, called the Accuracy Updated Ensemble (AUE2), which aims at reacting equally well to different types of drift. AUE2 combines accuracy-based weighting mechanisms known from block-based ensembles with the incremental nature of Hoeffding Trees. The proposed algorithm is experimentally compared with 11 state-of-the-art stream methods, including single classifiers, block-based and online ensembles, and hybrid approaches in different drift scenarios. Out of all the compared algorithms, AUE2 provided best average classification accuracy while proving to be less memory consuming than other ensemble approaches. Experimental results show that AUE2 can be considered suitable for scenarios, involving many types of drift as well as static environments.
Mode tracking using multiple data streams Bouguelia, Mohamed-Rafik; Karlsson, Alexander; Pashami, Sepideh ...
Information fusion,
09/2018, Volume:
43
Journal Article
Peer reviewed
•A method for tracking high-level conceptual modes from multiple streams is proposed.•It is based on aggregating signals into features, clustering, and Bayesian tracking.•Results show an accurate ...detection of mode transitions, enabling real-time tracking.
Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous discovery of high-level knowledge from ubiquitous data streams. This paper introduces a method for recognition and tracking of hidden conceptual modes, which are essential to fully understand the operation of complex environments, and an important step towards building truly intelligent aware systems. We consider a scenario of analyzing usage of a fleet of city buses, where the objective is to automatically discover and track modes such as highway route, heavy traffic, or aggressive driver, based on available on-board signals. The method we propose is based on aggregating the data over time, since the high-level modes are only apparent in the longer perspective. We search through different features and subsets of the data, and identify those that lead to good clusterings, interpreting those clusters as initial, rough models of the prospective modes. We utilize Bayesian tracking in order to continuously improve the parameters of those models, based on the new data, while at the same time following how the modes evolve over time. Experiments with artificial data of varying degrees of complexity, as well as on real-world datasets, prove the effectiveness of the proposed method in accurately discovering the modes and in identifying which one best explains the current observations from multiple data streams.
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