Soft-CsGDT

In this paper we study the problem of constructing histograms from high-speed time-changing data streams. Learning in this context requires the ability to process examples once at the rate they arrive, maintaining a histogram consistent with the most recent data, and forgetting out-date data whenever a change in the distribution is detected. To construct histogram from high-speed data streams we use the two layer structure used in the Partition Incremental Discretization (PiD) algorithm. Our contribution is a new method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference time window, that reflects the distribution observed in the past; and the current time window reflecting the distribution observed in the most recent data. We compare both distributions and signal a change whenever they are greater than a threshold value, using three different methods: the Entropy Absolute Difference, the Kullback-Leibler divergence and the Cosine Distance. The experimental results suggest that Kullback-Leibler divergence exhibit high probability in change detection, faster detection rates, with few false positives alarms.

A 28 nm CMOS Software-Defined Transceiver (SDTRX) enabling High-Speed Data (HSD) streaming, including Ultra HD TV, within home cable networks is presented. By making efficient use of available cable bandwidth, the SDTRX dynamically handles up to 1024QAM OFDM-modulated HSD streams. This paper addresses SDTRX system-level design methodology as the key driver in enabling performance optimization for achieving a wide frequency range of operation at the lowest power and area consumption. By employing an optimized architecture constructed on available state-of-the art 28 nm functional building blocks, the monolithic SDTRX consists of a mixer-based harmonic rejection RX with a DAC-based TX and a smart PLL system. It operates over a 0.4-to-1.7 GHz frequency range while consuming less than 475 mW in half-duplex mode. Moreover, by developing a simple TX-RX loopback circuit, the system is enabled to efficiently calibrate TX output power and to remove the need for a dedicated external pin. This low-cost SDTRX is embedded in various 28 nm CMOS multimedia SoCs and is, to the authors' knowledge, the first reported transceiver front end to enable true high-speed data streaming within home cable networks.

Histograms are a common technique for density estimation and they have been widely used as a tool in exploratory data analysis. Learning histograms from static and stationary data is a well known topic. Nevertheless, very few works discuss this problem when we have a continuous flow of data generated from dynamic environments. The scope of this paper is to detect changes from high-speed time-changing data streams. To address this problem, we construct histograms able to process examples once at the rate they arrive. The main goal of this work is continuously maintain a histogram consistent with the current status of the nature. We study strategies to detect changes in the distribution generating examples, and adapt the histogram to the most recent data by forgetting outdated data. We use the Partition Incremental Discretization algorithm that was designed to learn histograms from high-speed data streams. We present a method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference window, reflecting the distribution observed in the past; and the current window which receives the most recent data. The current window is cumulative and can have a fixed or an adaptive step depending on the distance between distributions. We compared both distributions using Kullback-Leibler divergence, defining a threshold for change detection decision based on the asymmetry of this measure. We evaluated our algorithm with controlled artificial data sets and compare the proposed approach with nonparametric tests. We also present results with real word data sets from industrial and medical domains. Those results suggest that an adaptive window’s step exhibit high probability in change detection and faster detection rates, with few false positives alarms.

The capacity of a colour chart and a reflectance photometer (Agtron) to accurately determine chipping quality of potato tubers was assessed using data sets taken over a 4-yr period for 17–32 cultivars. Both tests gave a high diagnostic accuracy for chipping quality regardless of sampling time from storage or the occurrence of high temperature reconditioning when evaluated by receiver operating characteristic (ROC) curve analysis. Receiver operating characteristic curve analysis showed that both tuber glucose content and chip colour provided good diagnostic performance in correctly separating processing from non-processing tubers over a range of growing and storage conditions. Identification of chipping from non-chipping tuber samples from a 13 °C storage across a range of cultivars and growing conditions occurred with a minimum chipping colour threshold range of 41–47 or a maximum glucose concentration range of 4.3–5.4 mmol L−1 of tuber cell sap. The practical value of a test can depend on such factors as prevalence of chippers in a tuber population as well as the cost of misclassifications, i.e., costs associated with false positive or false negative test results and expressed in relative terms as the unit cost ratio. An examination of Prevalence-Value-Accuracy (PVA) plots for one of the data sets indicated that total misclassification costs could increase rapidly, depending on the prevalence of chipping tubers and the relative amounts of false negative and false positive costs. Maximum costs were consistently associated with a prevalence of 50% chippers and a unit cost ratio of 0.5. In a tuber sample containing a high prevalence of chippers (50–70%) and a low unit cost ratio (<0.2), an acceptable colour threshold determined by PVA-Threshold (PVAT) plots would be approximately 40 to 50 from the Agriculture and Agri-Food Canada colour chart. However, if the colour chart was used for screening tuber samples with a low prevalence (20–40%) of chippers and a unit cost ratio >0.20, a threshold between 60 and 65 would be optimum. The latter range would be conservative and agrees with, and supports, current industry standards, which reside at 60 or better. Since a good diagnostic test should be repeatable and subject to minimal inter-observer variation, the more objective glucose or reflectance photometric tests may be preferable and provide acceptable diagnostic accuracy for processing quality. However, the present study indicates that all three test methods are acceptable for accurately separating chipping from non-chipping tubers regardless of sampling or storage protocols. Key words: Potato, colour chart, reflectance colorimetry, glucose content

Discovering interesting patterns from high-speed data streams is a challenging problem in data mining. Recently, the support metric-based frequent pattern mining from data stream has achieved a great attention. However, the occurrence frequency of a pattern may not be an appropriate criterion for discovering meaningful patterns. Temporal regularity in occurrence behavior can be a key criterion for assessing the importance of patterns in several online applications such as market basket analysis, gene data analysis, network monitoring, and stock market. A pattern can be said regular if its occurrence behavior satisfies a user-given interval in the data steam. Mining regular patterns from static databases has recently been addressed. However, even though mining regular patterns from stream data is extremely required in online applications, no such algorithm has been proposed yet. Therefore, in this paper we develop a novel tree structure called Regular Pattern Stream tree (RPS-tree), and an efficient mining technique for discovering regular patterns over data stream. Using a sliding window method the RPS-tree captures the stream content, and with an efficient tree updating mechanism it constantly processes exact stream data when the stream flows. Extensive experimental analyses show that our RPS-tree is highly efficient in discovering regular patterns from a high-speed data stream.

Distributed systems have been widely used for applications that need real-time processing over high volume and high speed data streams. This work presents the architecture and the implementation of a correlation algorithm for streaming data on a Graphic Processing Unit (GPU). The proposed system accelerates the correlation calculation of the Hayashi-Yoshida algorithm up to 10x vs. a conventional distributed system, and these performance characteristics apply to a broad category of correlation estimators. Furthermore, our system offers real-time correlation computation over high-speed streaming data and demonstrates how a batch processing algorithm can be applied to real-time streaming data. The results show that GPUs are a highly promising platform for correlation estimators as they improve significantly the volume of streaming data that can be processed in real time vs. other approaches that use “unlimited” conventional computing resources.

Outlier detection or anomaly detection is applied in numerous applications, such as fraud detection, network intrusion detection, manufacturing, and environmental monitoring. Due to the continuous and dynamic characteristics of streaming data, outlier detection over data streams becomes a very challenging task. When analyzing real-time data streams, it is typically impossible to store the entire set of data due to space limitations. Also due to the high data rates, it is necessary to produce the results in a limited amount of time. Parallel processing power of Graphics Processing Units (GPUs) can be used to accelerate the outlier detection process, and thus address the challenges of outlier detection over data streams. This paper proposes a GPU-accelerated outlier detection algorithm for continuous data streams using kernel density estimation approach. Experiments show that the proposed SOD_GPU algorithm is efficient for detecting outliers in high-dimensional, high-speed data streams, and produces results in a timely manner without compromising the outlier detection accuracy. The proposed method achieved up to 20X speedup compared to a respective multi-core CPU implementation, and the speedup increases with the number of data attributes and the input data rate.

Due to an ever-increasing demand for analyzing the large volumes of information issuing from high-speed data streams, multi-label stream classification is replacing the traditional offline multi-label classification system and has thus become a focal point in recent years. In this paper, we propose a new algorithm for multi-label stream classification that relies on an Online Dual Memory (ODM). It uses a short-term memory in order to adapt quickly to changes within the data stream, and employs a long-term memory for recurring concepts. Furthermore, via a modification of Learning Vector Quantization, ODM is not only capable of classifying efficiently but also of maintaining a low computational complexity. Experiments were conducted so as to establish a comprehensive overview of its performance, which was achieved by comparing four methods on two different kinds of datasets (nine stationary datasets and four datasets with concept drift). Our results confirm the high accuracy and low time complexity of ODM, as well as its ability to cope with concept drift.

The methods of approximate query processing have been proposed for analytics over high-speed data streams, which compact continuous streams into a space-constrained sketch and provide reliable estimates for different queries. Count-Min (CM) is the state-of-the-art sketching structure supporting many queries with error-guaranteed estimates under limited space. However, we need to create a counter table beforehand in CM according to the size of data streams, while it is usually unpredictable for dynamic data streams. In this paper, we proposed an approach, called Dynamic Count-Min sketch (DCM), which is appropriate for dynamic data set and can provide accurate estimates for point query and self-join size query. Our approach constitutes incremental CM sketches and allocates space in a pay-as-you-go manner. Our mathematical analysis and substantial experiments both show that our approach is appropriate for data sets with dynamic or skewed inputs and can provide error-guaranteed estimates with less space compared to CM.

It is widely realized that the integration of information retrieval (IR) and database (DB) techniques provides users with a broad range of high quality services. A new challenging issue along the same direction is IR-styled m-keyword query processing in a RDBMS framework over an open-ended relational data stream. The capability of supporting m-keyword queries over a relational data stream makes it possible for users to monitor events, that are implicitly interrelated, over a relational data stream in a timely manner. In brief, the problem is to find all connected trees whose size is less than or equal to a user-given threshold in terms of number of nodes for a m-keyword query, {k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , middot middot middot , k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> }, over a relational data stream on a database schema GS. The difficulty of the problem is related to the number of costly joins to be processed over time, which is affected by the parameters such as the number of keywords (m), the maximum size of connected trees (Tmax), as well as the complexity of the database schema when it is viewed as a schema graph (G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> ). In this paper, we propose a new demand-driven approach to process such a query over a high speed data stream. We show that we can significantly reduce the number of intermediate results when processing joins over a data stream, and therefore can achieve high efficiency.

Objectives: Prediction of cochlear implantation (CI) outcome is often difficult because outcomes vary among patients. Though the brain plasticity across modalities during deafness is associated with individual CI outcomes, longitudinal observations in multiple patients are scarce. Therefore, we sought a prediction system based on cross-modal plasticity in a longitudinal study with multiple patients.METHODS: Classification of CI outcomes between excellent or poor was tested based on the features of brain cross-modal plasticity, measured using event-related responses and their corresponding electromagnetic sources. A machine learning estimation model was applied to 13 datasets from 3 patients based on linear supervised training. Classification efficiency was evaluated comparing prediction accuracy, sensitivity/specificity, total mis-classification cost, and training time among feature set conditions.Results:Combined feature sets with the sensor and source levels dramatically improved classification accuracy between excellent and poor outcomes. Specifically, the tactile feature set best explained CI outcome (accuracy, 98.83 ± 2.57%; sensitivity, 98.00 ± 0.01%; specificity, 98.15 ± 4.26%; total misclassification cost, 0.17 ± 0.38; training time, 0.51 ± 0.09 sec), followed by the visual feature (accuracy, 93.50 ± 4.89%; sensitivity, 89.17 ± 8.16%; specificity, 98.00 ± 0.01%; total misclassification cost, 0.65 ± 0.49; training time, 0.38 ± 0.50 sec).Conclusion:Individual tactile and visual processing in the brain best classified the current status when classified by combined sensor–source level features. Our results suggest that cross-modal brain plasticity due to deafness may provide a basis for classifying the status. We expect this novel method to contribute to the evaluation and prediction of CI outcomes.

Incremental learning has been used extensively for data stream classification. Most attention on the data stream classification paid on non-evolutionary methods. In this paper, we introduce new incremental learning algorithms based on harmony search. We first propose a new classification algorithm for the classification of batch data called harmony-based classifier and then give its incremental version for classification of data streams called incremental harmony-based classifier. Finally, we improve it to reduce its computational overhead in absence of drifts and increase its robustness in presence of noise. This improved version is called improved incremental harmony-based classifier. The proposed methods are evaluated on some real world and synthetic data sets. Experimental results show that the proposed batch classifier outperforms some batch classifiers and also the proposed incremental methods can effectively address the issues usually encountered in the data stream environments. Improved incremental harmony-based classifier has significantly better speed and accuracy on capturing concept drifts than the non-incremental harmony based method and its accuracy is comparable to non-evolutionary algorithms. The experimental results also show the robustness of improved incremental harmony-based classifier.

Real-time transient stability status prediction (RTSSP) is very important to maintain the safety and stability of electrical power systems, where any unstable contingency will be likely to cause large-scale blackout. Most of machine learning methods used for RTSSP attempt to attain a low classification error, which implies that the misclassification costs of different categories are the same. However, misclassifying an unstable case as stable one usually leads to much higher costs than misclassifying a stable case as unstable one. In this paper, a new RTSSP method based on cost-sensitive extreme learning machine (CELM) is proposed, which recognizes the RTSSP as a cost-sensitive classification problem. The CELM is constructed pursuing the minimum misclassification costs, and its detailed implementation procedures for RSSTP are also researched in this work. The proposed method is implemented on the New England 39-bus electrical power system. Compared with three cost-blind methods (ELM, SVM and DT) and two cost-sensitive methods (cost-sensitive DT, cost-sensitive SVM), the simulation results have proved that the lower total misclassification costs and false dismissal rate with low computational complexity can be achieved by the proposed method, which meets the demands for the computation speed and the reliability of RTSSP.

In data streams, persistence characterizes items that appear repeatedly across multiple non-overlapping time windows. Addressing persistence-based tasks, such as detecting highly persistent items and estimating persistence, is crucial for applications like recommendation systems and anomaly detection in high-velocity data streams. However, these tasks are challenging due to stringent requirements for rapid processing and limited memory resources. Existing methods often struggle with accuracy, especially given highly skewed data distributions and tight fastest memory budgets, where hash collisions are severe. In this paper, we introduce Pandora, a novel approximate data structure designed to tackle these challenges efficiently. Our approach incorporates the insight that items absent for extended periods are likely non-persistent, increasing their probability of eviction to accommodate potential persistent items more effectively. We validate this insight empirically and integrate it into our update strategy, providing better protection for persistent items. We formally analyze Pandora's error bounds to validate its theoretical soundness. Through extensive trace-driven tests, we demonstrate that Pandora achieves superior accuracy and processing speed compared to state-of-the-art methods across various persistence-based tasks. Additionally, we further accelerate Pandora's update speed using Single Instruction Multiple Data (SIMD) instructions, enhancing its efficiency in high-speed data stream environments. The code for our method is open-sourced.

Clustering data streams has become a hot topic in the era of big data. Driven by the ever increasing volume, velocity and variety of data, more efficient algorithms for clustering large-scale complex data streams are needed. In this paper, we present a parallel algorithm called PaStream, which is based on advanced Graphics Processing Unit (GPU) and follows the online-offline framework of CluStream. Our approach can achieve hundreds of times speedup on high-speed and high-dimensional data streams compared with CluStream. It can also discover clusters with arbitrary shapes and handle outliers properly. The efficiency and scalability of PaStream are demonstrated through comprehensive experiments on synthetic and standard benchmark datasets with various problem factors.