Stream Processing Research Articles

Ontology Development for Detecting Complex Events in Stream Processing: Use Case of Air Quality Monitoring

With the increasing amount of data collected by IoT devices, detecting complex events in real-time has become a challenging task. To overcome this challenge, we propose the utilisation of semantic web technologies to create ontologies that structure background knowledge about the complex event-processing (CEP) framework in a way that machines can easily comprehend. Our ontology focuses on Indoor Air Quality (IAQ) data, asthma patients’ activities and symptoms, and how IAQ can be related to asthma symptoms and daily activities. Our goal is to detect complex events within the stream of events and accurately determine pollution levels and symptoms of asthma attacks based on daily activities. We conducted a thorough testing of our enhanced CEP framework with a real dataset, and the results indicate that it outperforms traditional CEP across various evaluation metrics such as accuracy, precision, recall, and F1-score.

Real-time data integration in information systems using stream processing for medical data

Real-time data integration in information systems using stream processing for medical data

The effects of object size on spatial orientation: an eye movement study.

The processing of visual information in the human brain is divided into two streams, namely, the dorsal and ventral streams, object identification is related to the ventral stream and motion processing is related to the dorsal stream. Object identification is interconnected with motion processing, object size was found to affect the information processing of motion characteristics in uniform linear motion. However, whether the object size affects the spatial orientation is still unknown. Thirty-eight college students were recruited to participate in an experiment based on the spatial visualization dynamic test. Eyelink 1,000 Plus was used to collect eye movement data. The final direction difference (the difference between the final moving direction of the target and the final direction of the moving target pointing to the destination point), rotation angle (the rotation angle of the knob from the start of the target movement to the moment of key pressing) and eye movement indices under conditions of different object sizes and motion velocities were compared. The final direction difference and rotation angle under the condition of a 2.29°-diameter moving target and a 0.76°-diameter destination point were significantly smaller than those under the other conditions (a 0.76°-diameter moving target and a 0.76°-diameter destination point; a 0.76°-diameter moving target and a 2.29°-diameter destination point). The average pupil size under the condition of a 2.29°-diameter moving target and a 0.76°-diameter destination point was significantly larger than the average pupil size under other conditions (a 0.76°-diameter moving target and a 0.76°-diameter destination point; a 0.76°-diameter moving target and a 2.29°-diameter destination point). A relatively large moving target can resist the landmark attraction effect in spatial orientation, and the influence of object size on spatial orientation may originate from differences in cognitive resource consumption. The present study enriches the interaction theory of the processing of object characteristics and motion characteristics and provides new ideas for the application of eye movement technology in the examination of spatial orientation ability.

Stream Processing Based on Enormous Data Continuous Analysis System

Stream Processing Based on Enormous Data Continuous Analysis System

An adaptive load balancing strategy for stateful join operator in skewed data stream environments

As one of the most computationally intensive operations in stream processing applications, join operation can cause severe load imbalance problem when dealing with skewed data. Most of the popular solutions focused on monitoring-based dynamic balancing strategies, making it difficult to quickly adapt to the changing frequency of data stream, and sometimes failing the balancing strategies that try to address the skewed load in the cluster. To address these issues, we propose to use the prediction results of a deep reinforcement learning model and adjust the grouping strategy in advance before the frequency change of data stream. It will enable the system to quickly adapt to data stream fluctuation, while managing the resources for effective resource utilization. The following contributions are made in this paper: 1) Explore the main factors that trigger the load skewness problem in distributed stream join systems and carefully model the load balancing problem at the application level. 2) Develop a Gated Recurrent Unit Sequence to Sequence model to predict key frequency distribution of streams, and propose a dynamic grouping algorithm and a feedback-based resource elasticity scaling algorithm to solve the load imbalance problem caused by hot keys in real time. 3) Design and implement an adaptive stream join system Aj-Stream based on the prediction model and the proposed algorithm on Apache Storm. 4) Evaluate the system performance through extensive experiments on a large scale real-world dataset and multiple synthetic datasets. The experimental results demonstrate that the Aj-Stream proposed in this paper exhibits stable throughput and latency performance with both static data streams of varying skewnesses and dynamic data streams. In comparison to existing stream-connected systems, Aj-Stream demonstrated a 22.1% increase in system throughput and a 45.5% decrease in system latency when dealing with frequently fluctuating data streams.

An implicit unified gas-kinetic wave–particle method for radiative transport process

The unified gas-kinetic wave–particle method (UGKWP) has been developed for the multiscale gas, plasma, and multiphase flow transport processes for the past years. In this work, we propose an implicit UGKWP (IUGKWP) method to remove the Courant–Friedrichs–Lewy time step constraint. Based on the local integral solution of the radiative transfer equation (RTE), the particle transport processes are categorized into the long-λ streaming process and the short-λ streaming process compared to a local physical characteristic time tp. In the construction of the IUGKWP method, the long-λ streaming process is tracked by the implicit Monte Carlo method; the short-λ streaming process is evolved by solving the implicit moment equations; and the photon distribution is closed by a local integral solution of RTE. In the IUGKWP method, the multiscale flux of radiation energy and the multiscale closure of photon distribution are constructed based on the local integral solution. The IUGKWP method preserves the second-order asymptotic expansion of RTE in the optically thick regime and adapts its computational complexity to the flow regime. The numerical dissipation is well controlled, and the teleportation error is significantly reduced in the optically thick regime. The computational complexity of the IUGKWP method decreases exponentially as the Knudsen number approaches zero, and the computational efficiency is remarkably improved in the optically thick regime. The IUGKWP is formulated on a generalized unstructured mesh, and multidimensional 2D and 3D algorithms are developed. Numerical tests are presented to validate the capability of IUGKWP in capturing the multiscale photon transport process. The algorithm and code will apply in the engineering applications of inertial confinement fusion.

A Contemporary and Comprehensive Survey on Streaming Tensor Decomposition

Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is however nontrivial due to several inherent problems of real-time stream processing. In recent years, many research efforts have been dedicated to developing online techniques for decomposing such tensors, resulting in significant advances in streaming tensor decomposition or tensor tracking. This topic is emerging and enriches the literature on tensor decomposition, particularly from the data stream analystics perspective. Thus, it is imperative to carry out an overview of tensor tracking to help researchers and practitioners understand its developments and achievements, summarise the current trends and advances, and identify challenging problems. In this article, we provide a contemporary and comprehensive survey on different types of tensor tracking techniques. We particularly categorize the state-of-the-art methods into three main groups: streaming CP decompositions, streaming Tucker decompositions, and streaming decompositions under other tensor formats (i.e., tensor-train, t-SVD, and BTD). In each group, we further divide the existing algorithms into sub-categories based on their main optimization framework and model architectures. Finally, we present several applications, research challenges, open problems, and potential directions of tensor tracking in the future.

The neural oscillations in delta- and theta-bands contribute to divided attention in audiovisual integration.

One of key mechanisms implicated in multisensory processing is neural oscillations in distinct frequency band. Many studies explored the modulation of attention by recording the electroencephalography signals when subjects attended one modality, and ignored the other modality input. However, when attention is directed toward one modality, it may be not always possible to shut out completely inputs from a different modality. Since many situations require division of attention between audition and vision, it is imperative to investigate the neural mechanisms underlying processing of concurrent auditory and visual sensory streams. In the present study, we designed a task of audiovisual semantic discrimination, in which the subjects were asked to share attention to both auditory and visual stimuli. We explored the contribution of neural oscillations in lower-frequency to the modulation of divided attention on audiovisual integration. Our results implied that theta-band activity contributes to the early modulation of divided attention, and delta-band activity contributes to the late modulation of divided attention to audiovisual integration. Moreover, the fronto-central delta- and theta-bands activity is likely a marker of divided attention in audiovisual integration, and the neural oscillation on delta- and theta-bands is conducive to allocating attention resources to dual-tasking involving task-coordinating abilities.

A novel family of online censoring based complex-valued least mean kurtosis algorithms

In this paper, we propose a novel family of online censoring (OC) based complex-valued least mean kurtosis (CLMK) algorithms by inspiring the advantages of the online censoring strategy and kurtosis-based cost function. We first develop important members of this family of algorithms such as OC based CLMK (OC-CLMK) and augmented CLMK (OC-ACLMK). These algorithms censor less informative complex-valued data streams in an online manner and keep only the most informative ones for performing their weight vector updates. Thus, they reduce the costs of data processing without markedly affecting performance accuracy. However, they do not take into account possible outliers that disturb their performances considerably. Therefore, we also develop robust members such as robust OC-CLMK (ROC-CLMK) and OC-ACLMK (ROC-CLMK) algorithms, which censor less informative complex-valued data streams as well as possible outliers. The convergence analyses of the proposed algorithms in the sense of Lyapunov are also presented to derive the bounds of their step sizes in a compact form. The simulation results on large-scale system identification and regression scenarios affirm the mentioned attractive features of the proposed algorithms. This study also shows that the noteworthy properties of the proposed algorithms will provide important contributions to the processing of complex-valued big data streams.

SeQual-Stream: approaching stream processing to quality control of NGS datasets

BackgroundQuality control of DNA sequences is an important data preprocessing step in many genomic analyses. However, all existing parallel tools for this purpose are based on a batch processing model, needing to have the complete genetic dataset before processing can even begin. This limitation clearly hinders quality control performance in those scenarios where the dataset must be downloaded from a remote repository and/or copied to a distributed file system for its parallel processing.ResultsIn this paper we present SeQual-Stream, a streaming tool that allows performing multiple quality control operations on genomic datasets in a fast, distributed and scalable way. To do so, our approach relies on the Apache Spark framework and the Hadoop Distributed File System (HDFS) to fully exploit the stream paradigm and accelerate the preprocessing of large datasets as they are being downloaded and/or copied to HDFS. The experimental results have shown significant improvements in the execution times of SeQual-Stream when compared to a batch processing tool with similar quality control features, providing a maximum speedup of 2.7times when processing a dataset with more than 250 million DNA sequences, while also demonstrating good scalability features.ConclusionOur solution provides a more scalable and higher performance way to carry out quality control of large genomic datasets by taking advantage of stream processing features. The tool is distributed as free open-source software released under the GNU AGPLv3 license and is publicly available to download at https://github.com/UDC-GAC/SeQual-Stream.

A stream processing abstraction framework.

Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by analysts. We describe the evolution of RAM3S, a software infrastructure for the integration of Big Data stream processing platforms, to SPAF, an abstraction framework able to provide programmers with a simple but powerful API to ease the development of stream processing applications. By using SPAF, the programmer can easily implement real-time complex analyses of massive streams on top of a distributed computing infrastructure, able to manage the volume and velocity of Big Data streams, thus effectively transforming data into value.

Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud

Context:The combination of distributed stream processing with microservice architectures is an emerging pattern for building data-intensive software systems. In such systems, stream processing frameworks such as Apache Flink, Apache Kafka Streams, Apache Samza, Hazelcast Jet, or the Apache Beam SDK are used inside microservices to continuously process massive amounts of data in a distributed fashion. While all of these frameworks promote scalability as a core feature, there is only little empirical research evaluating and comparing their scalability. Objective:The goal of this study to obtain evidence about the scalability of state-of-the-art stream processing framework in different execution environments and regarding different scalability dimensions. Method:We benchmark five modern stream processing frameworks regarding their scalability using a systematic method. We conduct over 740 h of experiments on Kubernetes clusters in the Google cloud and in a private cloud, where we deploy up to 110 simultaneously running microservice instances, which process up to one million messages per second. Results:All benchmarked frameworks exhibit approximately linear scalability as long as sufficient cloud resources are provisioned. However, the frameworks show considerable differences in the rate at which resources have to be added to cope with increasing load. There is no clear superior framework, but the ranking of the frameworks depends on the use case. Using Apache Beam as an abstraction layer still comes at the cost of significantly higher resource requirements regardless of the use case. We observe our results regardless of scaling load on a microservice, scaling the computational work performed inside the microservice, and the selected cloud environment. Moreover, vertical scaling can be a complementary measure to achieve scalability of stream processing frameworks. Conclusion:While scalable microservices can be designed with all evaluated frameworks, the choice of a framework and its deployment has a considerable impact on the cost of operating it.

Enhancing Data Engineering Frameworks for Scalable Real-Time Marketing Solutions

This report discusses improvements to the data engineering frameworks for cost-efficient real-time marketing environment, with the applications in mobile advertising and smart city analytics. The proposed framework combines the processing of time-series data with map-reduce (M-R) systems in order to compensate for inefficiencies in the execution of temporal queries and the processing of real-time data streams. It enables effective behavioral targeting in mobile marketing and helps integrate various domains’ real-time data for smart city utilization. Specific examples that illustrate further refinements in the algorithms are showcased through case study and experimental evidence where gains in throughput, data reliability, and system effectiveness are quantified in real-time Marketing and Smart City applications.

Hierarchical Auto-scaling Policies for Data Stream Processing on Heterogeneous Resources

Data Stream Processing (DSP) applications analyze data flows in near real-time by means of operators, which process and transform incoming data. Operators handle high data rates running parallel replicas across multiple processors and hosts. To guarantee consistent performance without wasting resources in the face of variable workloads, auto-scaling techniques have been studied to adapt operator parallelism at run-time. However, most of the effort has been spent under the assumption of homogeneous computing infrastructures, neglecting the complexity of modern environments.We consider the problem of deciding both how many operator replicas should be executed and which types of computing nodes should be acquired. We devise heterogeneity-aware policies by means of a two-layered hierarchy of controllers. While application-level components steer the adaptation process for whole applications, aiming to guarantee user-specified requirements, lower-layer components control auto-scaling of single operators. We tackle the fundamental challenge of performance and workload uncertainty, exploiting Bayesian optimization (BO) and reinforcement learning (RL) to devise policies. The evaluation shows that our approach is able to meet users’ requirements in terms of response time and adaptation overhead, while minimizing the cost due to resource usage, outperforming state-of-the-art baselines. We also demonstrate how partial model information is exploited to reduce training time for learning-based controllers.

Simple and complex cells revisited: toward a selectivity-invariance model of object recognition.

This paper presents a theoretical perspective on modeling ventral stream processing by revisiting the computational abstraction of simple and complex cells. In parallel to David Marr's vision theory, we organize the new perspective into three levels. At the computational level, we abstract simple and complex cells into space partitioning and composition in a topological space based on the redundancy exploitation hypothesis of Horace Barlow. At the algorithmic level, we present a hierarchical extension of sparse coding by exploiting the manifold constraint in high-dimensional space (i.e., the blessing of dimensionality). The resulting over-parameterized models for object recognition differ from existing hierarchical models by disentangling the objectives of selectivity and invariance computation. It is possible to interpret our hierarchical construction as a computational implementation of cortically local subspace untangling for object recognition and face representation, which are closely related to exemplar-based and axis-based coding in the medial temporal lobe. At the implementation level, we briefly discuss two possible implementations based on asymmetric sparse autoencoders and divergent spiking neural networks.

General-purpose data stream processing on heterogeneous architectures with WindFlow

Many emerging applications analyze data streams by running graphs of communicating tasks called operators. To develop and deploy such applications, Stream Processing Systems (SPSs) like Apache Storm and Flink have been made available to researchers and practitioners. They exhibit imperative or declarative programming interfaces to develop operators running arbitrary algorithms working on structured or unstructured data streams. In this context, the interest in leveraging hardware acceleration with GPUs has become more pronounced in high-throughput use cases. Unfortunately, GPU acceleration has been studied for relational operators working on structured streams only, while non-relational operators have often been overlooked. This paper presents WindFlow, a library supporting the seamless GPU offloading of general partitioned-stateful operators, extending the range of operators that benefit from hardware acceleration. Its design provides high throughput still exposing a high-level API to users compared with the raw utilization of GPUs in Apache Flink.

A new RFID Middleware architecture based on a hybrid security technique using data encryption and RBAC for modern real-time tracking applications

Radio Frequency Identification (RFID) is a contactless technology that has developed over the 90s and 20th centuries. It employs electromagnetic or electrostatic coupling in the radio frequency part of the electromagnetic spectrum to uniquely identify traceable objects, and is widely used in various sectors (e.g., medical, Supply Chain Management, transportation, and IoT applications.). Through the supply of real-world monitoring and context information about things, the integration of this technology in such areas delivers various benefits in the future of ubiquitous computing. However, one of the primary challenges will be the capacity to manage data since RFID events have specific characteristics and requires special treatment, such as the large volume of data flow, inaccuracy, temporal and spatial data, are typical examples of RFID event data. The goal of this research is to first highlight the concerns and limitations of existing middleware architectures before introducing and implementing a new Middleware architecture to address the identified issues, specifically real-time processing of massive volumes of data coming from physical RFID infrastructure. This middleware combines role-based access control with an encryption algorithm to increase security, a NoSQL database for storing large amounts of data, complex event processing (CEP) to provide high-volume data stream processing, and improved interoperability via the Data Transformation Module. Finally, our architecture is evaluated and compared to several middleware architectures based on standard ISO/IEC 9126 metrics.

Effect of streaming process on the quality of wheat (Triticum aestivum L.) kernel during extraction

Streaming is a process of routing wheat flour streams to produce flour products and continuing with the milling process using plan-sifters. This machine has six ranges of opening mesh that produce six-grain granulations divided into several flour streams. This study aimed to assess the streaming process effect on the quality changes of wheat kernel extraction. The triplicates observation was conducted on the same 56 flour streams with different multi-wheat blends. The ash, protein, moisture, and gluten quality of blended wheat were analyzed. The streaming process significantly changed grain quality (p<0.001). Increasing the mesh width in the streaming process increased ash content (p<0.05) and protein content (p<0.05). In addition, there was a decrease in water content (p<0.05) and gluten content (p<0.05) at a wider mesh opening. The streaming process produces more than 72% wheat flour. This study showed the vital role of the streaming process in the quality determination of wheat flour produced.

Building Fake Review Detection Model Based on Sentiment Intensity and PU Learning.

Fake review detection has the characteristics of huge stream data processing scale, unlimited data increment, dynamic change, and so on. However, the existing fake review detection methods mainly target limited and static review data. In addition, deceptive fake reviews have always been a difficult point in fake review detection due to their hidden and diverse characteristics. To solve the above problems, this article proposes a fake review detection model based on sentiment intensity and PU learning (SIPUL), which can continuously learn the prediction model from the constantly arriving streaming data. First, when the streaming data arrive, the sentiment intensity is introduced to divide the reviews into different subsets (i.e., strong sentiment set and weak sentiment set). Then, the initial positive and negative samples are extracted from the subset using the marking mechanism of selection completely at random (SCAR) and Spy technology. Second, building a semi-supervised positive-unlabeled (PU) learning detector based on the initial sample to detect fake reviews in the data stream iteratively. According to the detection results, the data of initial samples and the PU learning detector are continuously updated. Finally, the old data are continually deleted according to the historical record points, so that the training sample data are within a manageable size and prevent overfitting. Experimental results show that the model can effectively detect fake reviews, especially deceptive reviews.

Autonomous Cross Domain Adaptation Under Extreme Label Scarcity.

A cross domain multistream classification is a challenging problem calling for fast domain adaptations to handle different but related streams in never-ending and rapidly changing environments. Notwithstanding that existing multistream classifiers assume no labeled samples in the target stream, they still incur expensive labeling costs since they require fully labeled samples of the source stream. This article aims to attack the problem of extreme label shortage in the cross domain multistream classification problems where only very few labeled samples of the source stream are provided before process runs. Our solution, namely, Learning Streaming Process from Partial Ground Truth (LEOPARD), is built upon a flexible deep clustering network where its hidden nodes, layers, and clusters are added and removed dynamically with respect to varying data distributions. A deep clustering strategy is underpinned by a simultaneous feature learning and clustering technique leading to clustering-friendly latent spaces. A domain adaptation strategy relies on the adversarial domain adaptation technique where a feature extractor is trained to fool a domain classifier by classifying source and target streams. Our numerical study demonstrates the efficacy of LEOPARD where it delivers improved performances compared to prominent algorithms in 15 of 24 cases. Source codes of LEOPARD are shared in https://github.com/wengweng001/LEOPARD.git to enable further study.