In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer i...
The need to transfer the primary data conversions close to the sensors, to the endpoints of monitoring systems, as well as in IoT terminal devices makes the development of new approaches to computing and the design of appropriate algorithms relevant....
Today, big data are generated from many sources, and there is a huge demand for storing, managing, processing, and querying on big data. The MapReduce model and its counterpart open source implementation Hadoop, has proven itself as the de facto solu...
Many kinds of spatio-temporal data in our daily lives, such as the trajectory data of moving objects, stream natively. Streaming systems exhibit significant advantages in processing streaming data due to their distributed architecture, high throughpu...
To detect perimeter intrusion accurately and quickly, a stream computing technology was used to improve real-time data processing in perimeter intrusion detection systems. Based on the traditional density-based spatial clustering of applications with...
Recently, there has been an explosive generation of streaming data in various fields such as IoT and network attack detection, medical data monitoring, and financial trend analysis. These domains require precise and rapid analysis capabilities by min...
Data stream processing systems are used to continuously run mission-critical applications for real-time monitoring and alerting. These systems require high throughput and low latency to process incoming data streams in real time. However, changes in...
Distributed stream processing engines (DSPEs) deploy multiple tasks on distributed servers to process data streams in real time. Many DSPEs have provided locality-aware stream partitioning (LSP) methods to reduce network communication costs. However,...
Efficient matching of incoming events of data streams to persistent queries is fundamental to event stream processing systems in wireless sensor networks. These applications require dealing with high volume and continuous data streams with fast proce...
The availability of geodata with high spatial and temporal resolution is increasing steadily. Often, these data are continuously generated by distributed sensor networks and provided as geodata streams. Geostatistical analysis methods, such as spatio...
With the upswing in the volume of data, information online, and magnanimous cloud applications, big data analytics becomes mainstream in the research communities in the industry as well as in the scholarly world. This prompted the emergence and devel...
Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable an...
Scalable real-time processing of large amounts of data has become a research topic of particular importance due to the continuously rising amount of data that is generated by devices equipped with sensing components. While existing approaches allow f...
The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications...
One of the most challenging tasks for network operators is implementing accurate per-packet monitoring, looking for signs of performance degradation, security threats, and so on. Upon critical event detection, corrective actions must be taken to keep...
With the expansion of the application range of big data and artificial intelligence technologies, graph data have been increasingly used to analyze the relationships among objects. With the advancement of network technology and the spread of social n...
Stream processing frameworks have become key enablers of real-time data processing in modern distributed systems. However, robust and scalable mechanisms for verifying temporal properties are often lacking in existing systems. To address this gap, a...
There is varied natural landscape in Taiwan. Erosion and sediment control engineering used to be a major strategy for watershed management and planning but ecological conservation in natural environments and industrial development, as well as the dev...
This paper presents IRONEDGE, an architectural framework that can be used in different edge Stream Processing solutions for “Smart Infrastructure” scenarios, on a case-by-case basis. The architectural framework identifies the common compo...
The avalanche of (both user- and device-generated) multimedia data published in online social networks poses serious challenges to researchers seeking to analyze such data for many different tasks, like recommendation, event recognition, and so on. F...
At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize prod...
Controlling active and passive systems in buildings with the aim of optimizing energy efficiency and maintaining occupants’ comfort is the major task of building management systems. However, most of these systems use a predefined configuration,...
Big Data and primarily distributed stream processing systems (DSPSs) are growing in complexity and scale. As a result, effective performance management to ensure that these systems meet the required service level objectives (SLOs) is becoming increas...
Glacier-fed streams are one of the environments most sensitive to global climate change. However, the effects of the freezing–thawing process on benthic macroinvertebrate communities in different habitats of glacier-fed streams are unclear. In...
In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Proc...
Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes...
With an estimated 2.4 million cases of foodborne illnesses recorded annually in the UK alone, food safety has become a paramount concern among stakeholders. Modern technology has positioned streaming platforms as pivotal conduits for disseminating in...
Natural disasters have a significant impact on human welfare. In recent years, disasters are more violent and frequent due to climate change, so their impact may be higher if no preemptive measures are taken. In this context, real-time data processin...
Processing in memory (PIM) is a new computing paradigm that stores the function values of some input modes in a lookup table (LUT) and retrieves their values when similar input modes are encountered (instead of performing online calculations), which...
Over the years, advanced IT technologies have facilitated the emergence of new ways of generating and gathering data rapidly, continuously, and largely and are associated with a new research and application branch, namely, data stream mining (DSM). A...
Parallel hardware designed for image processing promotes vision-guided intelligent applications. With the advantages of high-throughput and low-latency, streaming architecture on FPGA is especially attractive to real-time image processing. Notably, m...
Owing to the recent advancements in Internet of Things technology, social media, and mobile devices, real-time stream balancing processing systems are commonly used to process vast amounts of data generated in various media. In this paper, we propose...
During the assembly process, there are inevitable variations and noise factors in the material properties, process parameters and screening scheme, which may affect the quality of the product. Using the stress-strength model, an evaluated screening s...
Eco-hydrological water quality modeling for integrated water resources management of river basins should include all necessary landscape and in-stream nutrient processes as well as possible changes in boundary conditions and driving forces for nutrie...
The current market is highly competitive, and customers are increasingly demanding. In this context, organizations need to adopt tools that enhance process efficiency to ensure competitiveness. This report aims to implement Lean tools, specifically V...
Real-time performance is important in rule-based continuous spatiotemporal query processing for risk analysis and decision making of target objects collected by sensors of combat vessels. The existing Rete algorithm, which creates a compiled node lin...
While macroinvertebrates are extensively investigated in many river ecosystems, meta-community ecology perspectives in alpine streams are very limited. We assessed the role of ecological factors and temporal dynamics in the macroinvertebrate meta-com...
This study aims to improve the assembly manufacturing process to solve the workload imbalances by combining value stream mapping (VSM) and ranked positional weight (RPW). An empirical study was conducted in a defense manufacturing firm located in Ind...
The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effe...
A significant rise in the adoption of streaming applications has changed the decision-making processes in the last decade. This movement has led to the emergence of several Big Data technologies for in-memory processing, such as the systems Apache St...
In a wireless sensor network, sensors collect data about natural phenomena and transmit them to a server in real-time. Many studies have been conducted focusing on the processing of continuous queries in an approximate form. However, this approach is...
A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or...
Forestry 4.0 is inspired by the Industry 4.0 concept, which plays a vital role in the next industrial generation revolution. It is ushering in a new era for efficient and sustainable forest management. Environmental sustainability and climate change...
Manufacturing companies strive to minimize costs, maximize efficiency and improve production quality, which is crucial for market competitiveness. As companies grow and technologies evolve, increasingly complex challenges arise in effectively managin...
In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical i...
Sugar production generates wastewater rich in dissolved solids and organic matter, and improper disposal poses severe environmental risks, exacerbates water scarcity, and creates regulatory challenges. Conventional treatment methods, such as evaporat...
Studies on the real-time detection of connected components in graph streams have been carried out. The existing connected component detection method cannot process connected components incrementally, and the performance deteriorates due to frequent d...
As a new e-commerce model, live-streaming commerce enhances consumers’ shopping experiences by providing deeply involved online interaction. However, in live-streaming commerce, consumers are also faced with many shopping risks, such as fake pr...
With the increasing use of wireless sensor networks in water quality monitoring, an enormous amount of streaming data is generated by widely deployed sensors. However, the current batch mode used for data analysis can no longer meet the diverse combi...
Value Stream Mapping (VSM) is a popular and easy tool for visualizing production processes in enterprise. It allows one to eliminate or reduce waste, i.e., activities that do not create added value for the customer. The versatility of this tool allow...
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