Aristeidis Karras

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• Doctor of Engineering
• Researcher at University of Patras

In this study, we analyze the performance of the machine learning operators in Apache Spark MLlib for K-Means, Random Forest Regression, and Word2Vec. We used a multi-node Spark cluster along with collected detailed execution metrics computed from the data of diverse datasets and parameter settings. The data were used to train predictive models tha...

Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key perform...

Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy. In this paper, we investigate the impact of Non-Independent and Identically Distributed (non-IID) data on the performance of federated training, where we find a reduction in accuracy of up t...

Background Currently, the investigative set for lung cancer primarily comprises of radiological imaging modalities, chest radiographs (X-rays) and CT scans. An increasing number of evidence underscores the pivotal role of screening programs, particularly among high-risk populations, in the early detection of lung cancer. This assertion is substanti...

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 allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computati...

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale...

In the current era, where data is expanding due to the unforeseen volume, velocity, and variety of data types produced by IoT devices, there is an imperative need to manage such data in remote IoT environments. However, these complexities have been inadequately addressed by conventional data management methods. In such scenarios, Distributed Hash T...

This paper presents an innovative approach to overcoming the limitations of traditional cloud-centric architectures in the evolving Internet of Things (IoT) landscape. We introduce a set of novel decentralized algorithms boosting Mobile Edge Computing (MEC), a paradigm shift towards placing computational resources near data sources, thus boosting r...

The Internet of Things (IoT) has seen remarkable growth in recent years, but the data volatility and limited energy resources in these networks pose significant challenges. In addition, traditional quality of service metrics like throughput, latency, packet delay variation, and error rate remain important benchmarks. In this work, we explore the ap...

In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference time, irrespective of the variations in the size of the test set, implying a robust ability to...

In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sus...

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data managemen...

As the Internet of Things (IoT) landscape grows, with estimates exceeding 75 billion devices by 2025, effective data management and processing become primary challenges. Traditional cloud-centric models may struggle under this large data volume. This research presents Edge AI as an innovative solution, integrating artificial intelligence directly a...

In modern agriculture, the capability to promptly detect and respond to specific events is crucial. This study centres on the transformative potential of TinyML for enhancing event detection in Smart Agriculture, particularly when integrated with LoRa-based Wireless Sensor Networks (WSNs). In this work, we underscore the unique advantages of utiliz...

This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifically targeting the aquacultural regions of Greece, the system captures geographical and climatic data...

In this work, we introduce an innovative Markov Chain Monte Carlo (MCMC) classifier, a synergistic combination of Bayesian machine learning and Apache Spark, highlighting the novel use of this methodology in the spectrum of big data management and environmental analysis. By employing a large dataset of air pollutant concentrations in Madrid from 20...

Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (Li...

Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent challenges of imbalanced and noisy data impacting scalability and resilience, our study introduces tw...

Mobile Edge Computing (MEC) is a promising computing paradigm that provides computing and storage services for mobile and big data applications. MEC servers are deployed at base stations to establish a mobile edge network (MEN) where mobile users can offload tasks to nearby servers to speed up their mobile applications. However, challenges such as...

A method for query optimization is presented by utilizing Spark SQL, a module of Apache Spark that integrates relational data processing. The goal of this paper is to explore NoSQL databases and their effective usage in conjunction with distributed environments to optimize query execution time, in order to accommodate the user complex demands in a...

In the context of big-data analysis, the clustering technique holds significant importance for the effective categorization and organization of extensive datasets. However, pinpointing the ideal number of clusters and handling high-dimensional data can be challenging. To tackle these issues, several strategies have been suggested, such as a consens...

The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generated has presented new challenges for AutoML systems in terms of big data management. In this paper, we...

In the modern era where data is produced from multivariate sources, there is an urge to handle such data in an efficient yet effective manner. Therefore, applications that necessitate such capabilities shall make use of data structures and indexing mechanisms that can perform fast index operations along with low complexity as per insertion, deletio...

Visualization is a critical component across every software as it enables users to familiarize themselves with the environment and perform certain tasks with ease. Therefore, straightforward yet interactive and easy-to-understand tools let users’ complex demands be satisfied within minutes. The objective of this work is to give an optimized graphic...

Human resource management has a significant influence on the performance of any public body. Employee classification and ranking are definitely time-consuming processes, which in many cases lead to controversial results. In addition, assessing employee efficiency through a variety of skills could lead to never-ending calculations and error-prone st...

The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices. In particular, the TinyML framework in such devices aims to deliver reduced latency,...

Speech processing, the field of analysing input speech signals and methods of processing them has emerged in the recent days. Additionally, the development of a speech processing system involves several components in the design phase with probabilistic approximations for enhanced audio sampling and de-noising. In this work, we focus into the use of...

Drones are intelligent devices that offer solutions for a continuously expanding variety of applications. Therefore, there would be a significant improvement if these systems could explore space automatically and without human-supervision. This work integrates cutting-edge artificial intelligence techniques that allow drones to travel independently...

Query optimization is a crucial process across data mining and big data analytics. As the size of the data in the modern applications is increasing due to various sources, types and multi-modal records across databases, there is an urge to optimize lookup and search operations. Therefore, indexes can be utilized to solve the matter of rapid data gr...

Emotion detection is crucial in many IoT deployments from an operational perspective with examples ranging from digital health to smart cities. This is particularly true in smart homes where the interaction between the local IoT ecosystem and the inhabitants are continuous, pervasive, and nuanced. More specifically, emotion estimation from human sp...

One of the biggest problems the public sector faces is the proper utilization of human resources. Within a particular combination of practices, dynamic resource allocation management aims to increase employee productivity. The question that arises is how to match the abilities of employees on a measurable scale through their connection with the log...

On the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for reliable wireless long range communications. LoRa is an emerging technology for effective long range communications which can be directly applied to IoT applications. Wireless sensor networks (WSNs) are by far an efficient infrastructure where sensors act as...

In this paper, the concepts and techniques for global graph clustering are examined, or the process of locating related clusters of vertices within a graph. We introduce the construction of a graph clustering technique based on an eigenvector embedding and a local graph clustering method based on stochastic exploration of the graph. Then, the devel...

In the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for intelligent microcontrollers that can collect, sense and analyse data effectively and efficiently. Such devices can be installed in large scale IoT deployments ranging from smart homes to smart cities and smart buildings. The aim of these devices shall be not...

Federated Learning (FL) is an emerging technique that assures user privacy and data integrity in distributed machine learning environments. To perform so, chunks of data are trained across edge devices and a high performance cluster server maintains a local copy without exchanging it with other parties. In this work, we investigate a FL scenario in...

Big data management methods are paramount in the modern era as applications tend to create massive amounts of data that comes from various sources. Therefore, there is an urge to create adaptive, speedy and robust frameworks that can effectively handle massive datasets. Distributed environments such as Apache Spark are of note, as they can handle s...

Data streams are becoming increasingly important across a wide array of fields and are generally expected to be the preferred form of big data as aggregators and smart stream analytics in general can efficiently yield stream descriptions in various levels. Among them, event detection analytics are paramount since they typically allow the identifica...

Social media are widely considered as reflecting to a great extent human behavior including thoughts, emotions, as well as reactions to events. Consequently social media analysis relies heavily on examining the interaction between accounts. This work departs from this established viewpoint by treating the online activity as a result of the diffusio...

Pure peer-to-peer networks serve to secure information in a decentralized, distributed topology. The multi-armed bandit (MAB) problem formulation proves to be a useful tool for analyzing the problem of optimizing new peer connections. In this paper, we outline the new peer scenario described as a reinforcement learning problem with MABs in order to...

The employment of various language modelling techniques in the area of information retrieval is gaining wide adoption in the state of the art methods. The precision of the language model enables the solution of the issue of information retrieval in a huge corpus of texts. To accomplish this, these techniques begin by estimating a probabilistic ling...

In the dairy industry farming as well as transportation conditions are paramount to product quality and to the overall supply chain resiliency. However, modern farms are complex installations with a broad spectrum of factors such as atmospheric conditions, including rain and humidity, ground composition, and highly irregular animal motion making di...

Markov Chain Monte Carlo techniques are used to generate samples that closely approximate a given multivariate probability distribution, with the function not having to be normalised in the case of certain algorithms such as Metropolis-Hastings. As with other Monte Carlo techniques, MCMC employs repeated random sampling to exploit the law of large...

Monte Carlo simulations using Markov chains as the Gibbs sampler and Metropolis algorithm are widely used techniques for modelling stochastic problems for decision making. Like all other Monte Carlo approaches, MCMC exploits the law of large numbers via repeated random sampling. Samples are formed by running a Markov Chain that is constructed in su...

User evaluations include a significant quantity of information across online platforms. This information source has been neglected by the majority of existing recommendation systems, despite its potential to ease the sparsity issue and enhance the quality of suggestions. This work presents a deep model for concurrently learning item attributes and...

Markov Chain Monte Carlo (MCMC) techniques have long been studied in computational geometry subjects whereabouts the problems to be studied are complex geometric objects which by their nature require optimized techniques to be deployed or to gain useful insights by them. MCMC approaches are directly answering to geometric problems we are attempting...

Big data streams are possibly one of the most essential underlying notions. However, data streams are often challenging to handle owing to their rapid pace and limited information lifetime. It is difficult to collect and communicate stream samples while storing, transmitting and computing a function across the whole stream or even a large segment o...