Internet of Things (IoT) is getting more popular day by day, which triggers its adoption for solving domain specific problems. Cities are becoming smart by gathering the context knowledge through sensors and controlling specific parameters through ac...
Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models gen...
Selecting indexes capable of reducing the cost of query processing in database systems is a challenging task, especially in large-scale applications. Quantum computing has been investigated with promising results in areas related to database manageme...
Three-dimensional block models are the most widely used tool for the study and evaluation of ore deposits, the calculation and design of economical pits, mine production planning, and physical and numerical simulations of ore deposits. The way these...
Text indexing is a classical algorithmic problem that has been studied for over four decades: given a text T, pre-process it off-line so that, later, we can quickly count and locate the occurrences of any string (the query pattern) in T in time propo...
Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. M...
The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume...
With the rapid development of cloud-based data sharing technologies, enterprises and organizations tend to outsource their local data to cloud servers. They adopt searchable encryption (SE) techniques to access and search encrypted data. However, mos...
Multitask learning models provide benefits by reducing model complexity and improving accuracy by concurrently learning multiple tasks with shared representations. Leveraging inductive knowledge transfer, these models mitigate the risk of overfitting...
The objective of content-based image retrieval (CBIR) is to locate samples from a database that are akin to a query, relying on the content embedded within the images. A contemporary strategy involves calculating the similarity between compact vector...
Wireless sensor networks play a crucial role in IoT applications. Traditional range query methods have limitations in multi-sensor data fusion and energy efficiency. This paper proposes a new range query method for two-layer wireless sensor networks....
Most visual localization (VL) methods typically assume that keypoints in the query image are detected with the same algorithm as those stored in the reference map. This poses a serious limitation, as new and better detectors may progressively appear,...
An approach for molecular similarity/substructure searching based on structural hierarchy matching is proposed. In this approach, small molecules are divided into two categories, acyclic and cyclic forms. The latter are further divided into three str...
Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the resu...
Computing shortest-path distances is a fundamental primitive in the context of graph data mining, since this kind of information is essential in a broad range of prominent applications, which include social network analysis, data routing, web search...
Computational linguistics explores how human language is interpreted automatically and then processed. Research in this area takes the logical and mathematical features of natural language and advances methods and statistical procedures for automated...
Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In t...
Smart sensor-equipped mobile devices sense, collect, and process data generated by the edge network to achieve intelligent control, but such mobile devices usually have limited storage and computing resources. Mobile cloud storage provides a promisin...
Spatial crowdsourcing is a potentially powerful method for incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. This paper proposes and investigates task assignments...
Air traffic management (ATM) requires the handling big data of moving objects, such as flight trajectories. There is, however, a lack of specialized tools for trajectory-data management, where the spatiotemporal data are first class citizens. Instead...
The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allow...
In the era of big data, one of the key challenges is to discover process models and gain insights into business processes by analyzing event data recorded in information systems. However, Chaotic activity or infrequent behaviors often appear in actua...
Uncertain graphs are commonly used to model the uncertain relationships between entities that arise from experimental or measurement errors. In recent years, the analysis of uncertain graphs has attracted significant research attention, with the comp...
Vehicle safety risk can be decreased by diagnosing the lithium-ion battery system of electric road vehicles. Real-time cell diagnostics can avoid unexpected occurrences. However, lithium-ion batteries in electric vehicles can significantly differ in...
A temporal model describes processes as a sequence of observable events characterised by distinguishable actions in time. Conformance checking allows these models to determine whether any sequence of temporally ordered and fully-observable events com...
The k-nearest- neighbor (kNN) algorithm is crucial in data mining and machine learning, yet its deployment on large-scale datasets within cloud environments presents significant security and efficiency challenges. This paper is dedicated to advancing...
With the constant deepening of research on marine environment simulation and information expression, there are higher and higher requirements for the sense of the reality of ocean data visualization results and the real-time interaction in the visual...
With the rapid growth of Internet data, the performance of big data processing platforms is attracting more and more attention. In Spark, cache data are replaced by the Least Recently Used (LRU) Algorithm. LRU cannot identify the cost of cache data,...
R-tree is an important multi-dimensional data structure widely employed in many applications for storing and querying spatial data. As GPUs emerge as powerful computing hardware platforms, a GPU-based parallel R-tree becomes the key to efficiently po...
The challenging requirements of the fifth generation (5G) cellular networks motivate the need to explore the feasibility of delivering services over new network architectures. Information-Centric Networking (ICN) is an emerging and promising network...
In many applications, data streams are indispensable to describe the relationships between nodes in networks, such as social networks, computer networks, and hyperlink networks. Fundamentally, a graph stream is a dynamic representation of a graph, wh...
The ability to quickly calculate or query the shortest path distance between nodes on a road network is essential for many real-world applications. However, the traditional graph traversal shortest path algorithm methods, such as Dijkstra and Floyd&n...
The marine fishery is a high-risk industry, and when a fishing vessel is in distress at sea, it is essential to protect the lives of the fishermen by implementing a fast and effective rescue for the vessel in distress. In this process, quickly retrie...
Semantic Web has recently gained traction with the use of Linked Open Data (LOD) on the Web. Although numerous state-of-the-art methodologies, standards, and technologies are applicable to the LOD cloud, many issues persist. Because the LOD cloud is...
The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urb...
With the development of global maritime trade, ship emissions pose an increasing threat to the global atmospheric environment, especially in international navigation waters and important port areas, where their impact on coastal air quality and ecosy...
In this paper, we propose a Grid-based Non-uniform Probability Density Sampling Probabilistic Roadmap algorithm (GN-PRM) in response to the challenges of difficult sampling in narrow passages and low-probability map generation in traditional Probabil...
Object retrieval systems measure the degree of similarity of the shape of 3D models. They search for the elements of the 3D model databases that resemble the query model. In structural bioinformatics, the query model is a protein tertiary/quaternary...
Digital image source identification primarily focuses on analyzing and detecting the machine imprints or camera fingerprints left by imaging devices during the imaging process to trace the origin of digital images. The development of a swift search a...
This paper describes the derivation of fuzzy classification rules based on c-means fuzzy clustering algorithm as results that are induced of fuzzy clusters. Each fuzzy cluster is associated with a fuzzy classification rule in which fuzzy sets are obt...
With the advent of sensors, more and more services are developed in order to provide customers with insights about their health and their appliances’ energy consumption at home. To do so, these services use new mining algorithms that create new...
Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect...
The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not wit...
In visual-based localization, prior research falls short in addressing challenges for the Internet of Things with limited computational resources. The dominant state-of-the-art models are based on separate feature extractors and descriptors without c...
In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system...
The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incur...
During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in survei...
Systematic reviews are cumbersome yet essential to the epistemic process of medical science. Finding significant reports, however, is a daunting task because the sheer volume of published literature makes the manual screening of databases time-consum...
With the rapid development of smart handheld devices, wireless communication, and positioning technologies, location-based service (LBS) has been gaining tremendous popularity in mobile social networks (MSN). Users’ daily lives are facilitated...
This paper presents the design and implementation of a system for semi-automating the literature review process based on machine learning. By using machine learning algorithms, the system determines whether scientific papers belong to the topic that...
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