Numerical Data Streams Research Articles

Process-Oriented heterogeneous graph learning in GNN-Based ICS anomalous pattern recognition

Over the past few years, massive penetrations targeting an Industrial Control System (ICS) network intend to compromise its core industrial processes. So far, numerous advanced methods have been proposed to detect anomalous patterns in the numeric data streams with respect to the heterogeneous field devices involved in the industrial processes. These methods, despite reporting decent results, usually conduct system-wise detection instead of fine-grained anomalous pattern recognition at the device level. Furthermore, lacking explicit consideration of the exclusive process-related features with respect to each differentiated device, the fitness of their application in specified industrial processes is undermined. To tackle these issues, a GNN-based Attributed Heterogeneous Graph Analyzer (the AHGA) is designed to perform device-wise anomalous pattern detection via in-depth process-oriented associativity learning. The AHGA’s framework is constructed with four building blocks: a graph processor, a feature analyzer, a link inference decoder, and an anomaly detector. Its performance is assessed and compared against multiple link inference and anomaly detection baselines over 2 popular ICS datasets (SWaT and WADI). Comparative results demonstrate the AHGA’s reliability in capturing sophisticated process-oriented relations among heterogeneous devices as well as its effectiveness in boosting the performance of anomalous pattern recognition at device-level granularity.

FHC-NDS: Fuzzy Hierarchical Clustering of Multiple Nominal Data Streams

The need of fuzzy clustering arises in many real-world applications such as clumping the users based on their web browsing behavior where the behavior of a user can be similar to two different sets of users at the same instance. The aptness of fuzzy clustering for data streams is further intensified given their concept evolving nature. Data streams can be clustered either by following clustering-by-variable approach or clustering-by-example approach. Most of the existing fuzzy clustering-by-variable methods are applicable to numeric data streams only. In this article, a fuzzy hierarchical clustering method is proposed for clustering multiple nominal data streams using clustering-by-variable approach. The fuzzy affinity of data streams to different clusters is calculated using normalized cosine similarity to the cluster centroids. It handles the concept evolution by updating the hierarchical clustering structure by either merging and/or splitting the nodes depending on the extent to which the node entropy changes. The performance of the proposed method is analyzed and compared to hierarchical clustering for multiple nominal data streams (HCND), semifuzzy online divisive-agglomerative clustering, and nTreeClus on synthetic as well as real-world web-browsing dataset where it has outperformed all three in terms of cluster quality as quantified by Dunn index, modified Hubert <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Gamma$</tex-math></inline-formula> statistic, and adjusted rand index. Furthermore, the experimental results show that the proposed method is highly promising with regard to capturing fuzzy clusters as indicated by Xie-Beni index, partition coefficient, and partition entropy.

Study on Cyber Common Operational Picture Framework for Cyber Situational Awareness

The remarkable development of the Internet has made our lives very convenient, such as through the ability to instantaneously transmit individual pictures. As a result, cyber-attacks are also being developed and increasing, and the computer/mobile devices we use can become infected with viruses in an instant. Rapid cyber situational awareness is essential to prepare for such cyber-attacks. Accelerating cyber situational awareness requires Cyber Common Operational Pictures, which integrate and contextualize numerous data streams and data points. Therefore, we propose a Cyber Common Operational Pictures framework and criteria for rapid cyber situation awareness. First, the system reaction speed based on the user’s request and the standard for easily recognizing the object shown on the screen are presented. Second, standards and frameworks for five types of visualization screens that can directly recognize and respond to cyber-attacks are presented. Third, we show how a system was constructed based on the proposed framework, as well as the results of an experiment on the response time of each visualization screen. As a result of the experiment, the response speed of the 5 visualization screens was about 0.11 s on average for inquiry (simple) and 1.07 s on average for inquiry (complex). This is consistent with the typical response times of the studies investigated in this paper. If CyCOP is developed in compliance with the framework items (UI, object symbol, object size, response speed) presented in this paper, rapid situational awareness is possible. This research can be used in cyber-attack and defense training in the military field. In the private sector, it can be used in cyber and network control.

Read the News, Not the Books: Forecasting Firms’ Long-term Financial Performance via Deep Text Mining

In this paper, we show textual data from firm-related events in news articles can effectively predict various firm financial ratios, with or without historical financial ratios. We exploit state-of-the-art neural architectures, including pseudo-event embeddings, Long Short-Term Memory Networks, and attention mechanisms. Our news-powered deep learning models are shown to outperform standard econometric models operating on precise accounting historical data. We also observe forecasting quality improvement when integrating textual and numerical data streams. In addition, we provide in-depth case studies for model explainability and transparency. Our forecasting models, model attention maps, and firm embeddings benefit various stakeholders with quality predictions and explainable insights. Our proposed models can be applied both when numerically historical data is or is not available.

Optimal transmit antenna selection using hybrid algorithm for massive MIMO technology

SummaryMassive multiple input multiple output (M‐MIMO) methods make reference to a useful method for using multipath propagation to communicate and receive multiple data signals at once over a single radio channel. To simultaneously transfer numerous data streams, it makes use of various antennas. The quantity of power used grows as the quantity of antennas rises. As a result, choosing the best transmit antennas, which is a major difficulty in M‐MIMO systems, becomes important. In this research, “Hybrid Sea Lion‐Whale Algorithm (HS‐WA)” is introduced by choosing a best transmit antenna while taking into account several objectives. This method optimizes overall capacity and efficiency. The chosen method combines the “Whale Optimization Algorithm (WOA) and Sea Lion Optimization Algorithm (SLnO)” that determines which antenna should be chosen while also optimizing the antenna quantity. Finally, energy efficiency (EE) and capacity analysis results demonstrate that the provided approach is superior to all other models.

Situation-Aware Orchestration of Resource Allocation and Task Scheduling for Collaborative Rendering in IoT Visualization

Three dimensional rendering enabled IoT visualization provides an immersive operation view across large physical environments by contextually aggregating and visualizing numerous data streams from various systems. The massive resource demand to support real-time and high-quality rendering services can be fulfilled by collaborative rendering among resource-constrained wireless devices. To deliver reliable performance, one main challenge is to achieve reliable and sustainable collaboration in a dynamic IoT system with heterogeneous resource capacity and changing user intent. To overcome such issues, we propose a situation-aware orchestration mechanism of resource allocation and task scheduling. The proposed technique achieves objective-driven exploration of collaboration opportunity among heterogeneous resource by three steps: recognizing dynamic condition of resource and task, including resource reliability and computational demand; understanding the mutual impact of resource condition and task performance in the aspect of energy consumption and latency; precise alignment of resource capacity and task demands via a redundant task scheduling scheme. The proposed task scheduling problem is formulated as an optimization model with the objective of maximizing collaboration utility. A genetic algorithm (GA) with adaptive mating-distance is designed to tackle the NP-hard problem, which improves the optimal solution in simulation by approximately 25% and 30% compared to conventional GA and Greedy algorithm, respectively.

Visible light communication performance using MIMO‐ADO‐OFDM system

AbstractVisible light communications (VLC) and optical orthogonal frequency division multiplexing (O‐OFDM) have recently been identified as hopeful wireless techniques. Asymmetrically clipped DC‐biased O‐OFDM (ADO‐OFDM) is a modern performance enhancement technology used in VLC. This article proposes a performance improvement model using a novel approach that collaborates with ADO‐OFDM and multiple‐input multiple‐output (MIMO) in different design scenarios. In the first scenario, the bit‐error‐rate (BER) performance improvement is examined using different setups of MIMO‐ADO‐OFDM with maximal ratio combining (MRC). 8 × 8 MIMO good performance can be attained with about 15 dB signal‐to‐noise ratio for BER = 10−4. Another scenario is to boost the spectral efficiency of MIMO‐ADO‐OFDM by employing spatial multiplexing (SMX). However, 8 × 8 MIMO with SMX can improve spectral efficiency eight times that of single‐input single‐output ADO‐OFDM by fragmenting source data into numerous independent data streams. In the last scenario, multi‐user MIMO is developed by adopting ADO‐OFDM with zero‐forcing equalisers, allowing each user to consume all available bandwidth. The proposed technologies are evaluated by comparison with other MIMO systems, different O‐OFDM, and under a non‐linear LED model.

Hierarchical clustering for multiple nominal data streams with evolving behaviour

Over the decade, a number of attempts have been made towards data stream clustering, but most of the works fall under clustering by example approach. There are a number of applications where clustering by variable approach is required which involves clustering of multiple data streams as opposed to clustering data examples in a data stream. Furthermore, a few works have been presented for clustering multiple data streams and these are applicable to numeric data streams only. Hence, this research gap has motivated current research work. In the present work, a hierarchical clustering technique has been proposed to cluster multiple data streams where data are nominal. To address the concept changes in the data streams splitting and merging of the clusters in the hierarchical structure are performed. The decision to split or merge is based on the entropy measure, representing the cluster’s degree of disparity. The performance of the proposed technique has been analysed and compared to Agglomerative Nesting clustering technique on synthetic as well as a real-world dataset in terms of Dunn Index, Modified Hubert varGamma statistic, Cophenetic Correlation Coefficient, and Purity. The proposed technique outperforms Agglomerative Nesting clustering technique for concept evolving data streams. Furthermore, the effect of concept evolution on clustering structure and average entropy has been visualised for detailed analysis and understanding.

Handling missing data in near real-time environmental monitoring: A system and a review of selected methods

High-frequency water quality monitoring systems provide valuable measurements for predicting the trend of water quality, warning of abnormal activities or operating hydrological models. However, missing values are prevalent due to network miscommunication, device replacement or failure. Applying datasets with missing values can lead to biased results in statistical analysis or hydrological modelling work. We develop a cloud-based data processing system combining advanced algorithms to impute monitoring data in near real-time. The system provides high compatibility for supporting different water quality variables, imputation algorithms and extensive scalability to support numerous data streams. Based on the proposed approach, we review various imputation methods which can be applied to water quality data. Overall, this work provides a systematic design of a water quality data imputation system, explores the advantages and limitations of selected data imputation methods and analyses the imputation performance of two real-time water quality monitoring systems located in both the USA and Australia. The results provide practical guidelines for data imputation applications in water quality data.

Optimal Rule-Based Granular Systems From Data Streams

We introduce an incremental learning method for the optimal construction of rule-based granular systems from numerical data streams. The method is developed within a multiobjective optimization framework considering the specificity of information, model compactness, and variability and granular coverage of the data. We use $\alpha$ -level sets over Gaussian membership functions to set model granularity and operate with hyperrectangular forms of granules in nonstationary environments. The resulting rule-based systems are formed in a formal and systematic fashion. They can be useful in time series modeling, dynamic system identification, predictive analytics, and adaptive control. Precise estimates and enclosures are given by linear piecewise and inclusion functions related to optimal granular mappings.

Optimal Transmit Antenna Selection Using Improved GSA in Massive MIMO Technology

Massive Multiple Input Multiple Output (M-MIMO) systems depend on numerous antennas to transfer numerous data streams simultaneously in Wireless Network Systems. In M-MIMO systems, the optimal Transmit Antennas Selection remains as a major constraint. As the count of antennas is increased, the power or energy consumption also increases. In fact, for attaining higher capacity, more transmit antennas is required, which leads to an increase in power consumption. Hence, for solving these problems in M-MIMO systems, this paper intend to achieve the selection of optimal transmit antennas by considering a multi-objective problem that maximizes both the capacity and relative Energy Efficiency. For attaining this objective, the proposed novel optimization algorithm not only optimizes the number of transmit antennas but also optimizes which antenna has to be selected. Hence, for optimal selection of antennas, improved GSA is used here, based on a velocity vector, and hence the proposed scheme is termed as Modified Velocity vector based GSA (MV-GSA) that determines the number of antennas and how to select the antennas in an optimal way. Moreover, the adopted scheme is compared with conventional algorithms like Genetic Algorithm, Artificial Bee Colony, Particle Swarm Optimization, FireFly and conventional GSA and the results are obtained.

Bandwidth Compression of Floating-Point Numerical Data Streams for FPGA-Based High-Performance Computing

Although computational performance is often limited by insufficient bandwidth to/from an external memory, it is not easy to physically increase off-chip memory bandwidth. In this study, we propose a hardware-based bandwidth compression technique that can be applied to field-programmable gate array-- (FPGA) based high-performance computation with a logically wider effective memory bandwidth. Our proposed hardware approach can boost the performance of FPGA-based stream computations by applying a data compression technique to effectively transfer more data streams. To apply this data compression technique to bandwidth compression via hardware, several requirements must first be satisfied, including an acceptable level of compression performance and a sufficiently small hardware footprint. Our proposed hardware-based bandwidth compressor utilizes an efficient prediction-based data compression algorithm. Moreover, we propose a multichannel serializer and deserializer that enable applications to use multiple channels of computational data with the bandwidth compression. The serializer encodes compressed data blocks of multiple channels into a data stream, which is efficiently written to an external memory. Based on preliminary evaluation, we define an encoding format considering both high compression ratio and small hardware area. As a result, we demonstrate that our area saving bandwidth compressor increases performance of an FPGA-based fluid dynamics simulation by deploying more processing elements to exploit spatial parallelism with the enhanced memory bandwidth.

Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data.

The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users’ behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a ”blind” approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a “busyness” measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person’s needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner.

Sentiment analysis in financial texts

The growth of financial texts in the wake of big data has challenged most organizations and brought escalating demands for analysis tools. In general, text streams are more challenging to handle than numeric data streams. Text streams are unstructured by nature, but they represent collective expressions that are of value in any financial decision. It can be both daunting and necessary to make sense of unstructured textual data. In this study, we address key questions related to the explosion of interest in how to extract insight from unstructured data and how to determine if such insight provides any hints concerning the trends of financial markets. A sentiment analysis engine (SAE) is proposed which takes advantage of linguistic analyses based on grammars. This engine extends sentiment analysis not only at the word token level, but also at the phrase level within each sentence. An assessment heuristic is applied to extract the collective expressions shown in the texts. Also, three evaluations are presented to assess the performance of the engine. First, several standard parsing evaluation metrics are applied on two treebanks. Second, a benchmark evaluation using a dataset of English movie review is conducted. Results show our SAE outperforms the traditional bag of words approach. Third, a financial text stream with twelve million words that aligns with a stock market index is examined. The evaluation results and their statistical significance provide strong evidence of a long persistence in the mood time series generated by the engine. In addition, our approach establishes grounds for belief that the sentiments expressed through text streams are helpful for analyzing the trends in a stock market index, although such sentiments and market indices are normally considered to be completely uncorrelated.

A fast online learning algorithm for distributed mining of BigData

BigData analytics require that distributed mining of numerous data streams is performed in real-time. Unique challenges associated with designing such distributed mining systems are: online adaptation to incoming data characteristics, online processing of large amounts of heterogeneous data, limited data access and communication capabilities between distributed learners, etc. We propose a general framework for distributed data mining and develop an efficient online learning algorithm based on this. Our framework consists of an ensemble learner and multiple local learners, which can only access different parts of the incoming data. By exploiting the correlations of the learning models among local learners, our proposed learning algorithms can optimize the prediction accuracy while requiring significantly less information exchange and computational complexity than existing state-of-the-art learning solutions.

Incremental entropy-based clustering on categorical data streams with concept drift

Clustering on categorical data streams is a relatively new field that has not received as much attention as static data and numerical data streams. One of the main difficulties in categorical data analysis is lacking in an appropriate way to define the similarity or dissimilarity measure on data. In this paper, we propose three dissimilarity measures: a point-cluster dissimilarity measure (based on incremental entropy), a cluster–cluster dissimilarity measure (based on incremental entropy) and a dissimilarity measure between two cluster distributions (based on sample standard deviation). We then propose an integrated framework for clustering categorical data streams with three algorithms: Minimal Dissimilarity Data Labeling (MDDL), Concept Drift Detection (CDD) and Cluster Evolving Analysis (CEA). We also make comparisons with other algorithms on several data streams synthesized from real data sets. Experiments show that the proposed algorithms are more effective in generating clustering results and detecting concept drift.

Signature-Based Detection of Notable Transitions in Numeric Data Streams

A major challenge in large-scale process monitoring is to recognize significant transitions in the process conditions and to distinguish them from random fluctuations that do not produce a notable change in the process dynamics. Such transitions should be recognized at the early stages of their development using a minimal snapshot of the observable process log. We developed a novel approach to detect notable transitions based on analysis of coherent behavior of frequency components in the process log (coherency portraits). We have found that notable transitions in the process dynamics are characterized by unique coherency portraits, which are also invariant with respect to random process fluctuations. Our experimental study demonstrates significant efficiency of our approach as compared to traditional change detection techniques.

A Precise Statistical approach for concept change detection in unlabeled data streams

Recently data stream has been extensively explored due to its emergence in a great deal of applications such as sensor networks, web click streams and network flows. One of the most important challenges in data streams is concept change where data underlying distributions change from time to time. A vast majority of researches in the context of data stream mining are devoted to labeled data, whereas, in real word human practice label of data are rarely available to the learning algorithms. Moreover, most of the methods that detect changes in unlabeled data stream merely deal with numerical data sets, and also, they are facing considerable difficulty when dimension of data tends to increase. In this paper, we present a Precise Statistical approach for Concept Change Detection in unlabeled data streams, which, abbreviated as PSCCD, detects changes using an exchangeable test. This hypothesis test is driven from a martingale which is based on Doob’s Maximal Inequality. The advantages of our approach are three fold. First, it does not require a sliding window on the data stream whose size is a well-known challenging issue; second, it works well in multi-dimensional data stream, and last but not the least, it is applicable to different types of data including categorical, numerical and mixed-attribute data streams. To explore the advantages of our approach, quite a lot of experiments with different settings and specifications are conducted. The obtained results are very promising.

IBM Project Proposes Using Light to Make Chips Faster

IBM has developed an optical technology that could speed up data transmissions among servers, between chips within a system, or on the chips themselves. The company's SNIPER (silicon nanoscale integrated photonic and electronic transceiver) project uses CMOS integrated nanophotonic technology. This approach employs pulses of light, rather than copper connec tions, to move data at higher speeds. IBM's approach uses wavelength division multiplexing, which is faster because numerous data streams can be carried on multiple wavelengths simultaneously within a single fiber. IBM integrates the photonics and the CMOS electronics circuitry on the same chip during the manufacturing process. This approach is less expensive, yields more chips, and avoids damaging parts during assembly.

On clustering massive text and categorical data streams

In this paper, we will study the data stream clustering problem in the context of text and categorical data domains. While the clustering problem has been studied recently for numeric data streams, the problems of text and categorical data present different challenges because of the large and un-ordered nature of the corresponding attributes. Therefore, we will propose algorithms for text and categorical data stream clustering. We will propose a condensation based approach for stream clustering which summarizes the stream into a number of fine grained cluster droplets. These summarized droplets can be used in conjunction with a variety of user queries to construct the clusters for different input parameters. Thus, this provides an online analytical processing approach to stream clustering. We also study the problem of detecting noisy and outlier records in real time. We will test the approach for a number of real and synthetic data sets, and show the effectiveness of the method over the baseline OSKM algorithm for stream clustering.