Lab

Attila Kiss's Information Systems Lab (EFOP-3.6.3-VEKOP-16-2017-00002)

About the lab

Information Systems Lab started in 2017, supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002).

The aim is to support talented students and involve them in writing publications primarily in the fields of knowledge management systems, databases, Big Data, semantic web, data science, artificial intelligence, data protection, information systems, evolutionary computing, bioinformatics and software technology research.

(The lab is a continuation of the Knowledge Management Systems lab launched in 2011, which was also led by Attila Kiss.)

Featured research (71)

During the spread of a pandemic such as COVID-19, the effort required of health institutions increases dramatically. Generally, Health systems' response and efficiency depend on monitoring vital signs such as blood oxygen level, heartbeat, and body temperature. At the same time, remote health monitoring and wearable health technologies have revolutionized the concept of effective healthcare provision from a distance. However, analyzing such a large amount of medical data in time to provide the decision-makers with necessary health procedures is still a challenge. In this research, a wearable device and monitoring system are developed to collect real data from more than 400 COVID-19 patients. Based on this data, three classifiers are implemented using two ensemble classification techniques (Adaptive Boosting and Adaptive Random Forest). The analysis of collected data showed a remarkable relationship between the patient's age and chronic disease on the one hand and the speed of recovery on the other. The experimental results indicate a highly accurate performance for Adaptive Boosting classifiers, reaching 99%, while the Adaptive Random Forest got a 91% accuracy metric.
Data science techniques have increasing importance in medical data analysis, including detecting and predicting the probability of contracting a disease. A large amount of medical data is generated close to the patients in the form of a stream, such as data from sensors and medical devices. The distribution of these kinds of data may change from time to time; adaptive Machine Learning (ML) consists of a continuous training process responding to the distribution’s change. Adaptive ML models require high computational resources, which can be provided by cloud computing. In this work, a classification model is proposed to utilize the advantages of cloud computing, edge computing, and adaptive ML. It aims to precisely and efficiently classify EEG signal data, thereby detecting the seizures of epileptic patients using Adaptive Random Forest (ARF). It includes a global adaptive classifier in the cloud master node and a local light classifier in each edge node. In this model, the delayed labels consider missing values, and the Model-based imputation method is used to handle them in the global classifier. Implementing the proposed model on a real huge dataset (CHB-MIT) showed an accurate performance. It has a 0.998 True Negative Rate, a 0.785 True Positive Rate, and a 0.0017 False Positive Rate, which overcomes much of the research in the state-of-the-art. Keywords: adaptive machine learning; electroencephalography; epilepsy classification; cloud computing; edge computing
Numerical simulations of physical systems are found in many industries, as they currently play a crucial role in product development. There are many numerical methods for solving differential equations that describe the underlying physics behind the mathematical models in the simulation, among which, the finite element method (FEM) is one of the most commonly used. Although in many applications the FEM seems to provide an acceptable solution to the problem, there are still many complex real-life processes that can be challenging to simulate numerically due to their complexity and large size. Recently, there has been a shift in research towards efficiently applying quantum algorithms in finite element analysis (FEA), as the potential and speedup that they could offer have been shown, but little to no effort has been made towards the applicability and cost efficiency of these algorithms in real-world quantum devices. In this paper, we propose a cost-efficient method for applying quantum algorithms in FEA for industrial problems post-processed by classical algorithms in order to address the limitations of available quantum hardware and their cost when accessing them through different cloud-based services. We carry this out by approximating the solution of the initially large system with a suitable quantum algorithm and using the obtained solutions to generate a set of reduced-order models (ROMs) that are much smaller in complexity and size than the original model. This allows the simulation of the original model with different parameter sets and excitations to be run efficiently on classical computers without having the need to access quantum subroutines again. This way, we have reduced the usage of quantum hardware (and thus the development cost) while still taking advantage of its quantum speedup. Keywords: quantum equation solver; computational fluid dynamics; finite element method; model order reduction; proper orthogonal decomposition; quantum numerical simulation
In the present day, virtually every application software generates large amounts of log entries during its work. The log files that are made from these entries are a collection of information about what happened while the program was running. This report can be used for multiple purposes such as performance monitoring, maintaining security, or improving business decision making. Log entries are usually generated in a disorganized manner. Using template miners, the different `event types’ can be distinguished (each log entry is an event), and the set of all entries is split into disjointed subsets according to the event types. These events consist of two parts. The first is the constant part, which is the same for all occurrences of the same event type. The second is the parameter part, which can be different for each occurrence. Since software mass-produces log files, in our previous paper, we introduced an algorithm that uses the templates mined from the data to create a dictionary, which is then used to encode the log entries, so only the ID and the parameter list would be stored. In this paper, we enhance our algorithm with the use of the frequency of the templates, by encoding the parameters and also making use of Huffman coding. With the use of these measures, compared to the previous 67.4% compression rate, a 94.98% compression rate can be achieved (where compression rate is 1 minus the ratio of the size of the compressed file to the uncompressed size). The running times of the different measures that we used to enhance our algorithm are also compared. We also analyze the difference between the compression rate of the enhanced algorithm and general compressors such as LZMA, Bzip2, and PPMd. We examine whether the size of the log files can be further decreased with the combined use of our enhanced method and the general compressors. We also generate log files that follow different distributions to examine the compression capability if the distribution does not follow the power law. Based on our experiments, we would recommend the use of the MoLFI (Multi-objective Log message Format Identification) template miner method with our enhanced algorithm together with PPMd. Keywords: log file processing; template mining; compression; LZMA; Bzip2; PPMd
In this paper, we present a novel implementation of an ecosystem simulation. In our previous work, we implemented a 3D environment based on a predator–prey model, but we found that in most cases, regardless of the choice of starting parameters, the simulation quickly led to extinctions. We wanted to achieve system stabilization, long-term operation, and better simulation of reality by incorporating genetic evolution. Therefore we applied the predator–prey model with an evolutional approach. Using the Unity game engine we created and managed a closed 3D ecosystem environment defined by an artificial or real uploaded map. We present some demonstrative runs while gathering data, observing interesting events (such as extinction, sustainability, and behavior of swarms), and analyzing possible effects on the initial parameters of the system. We found that incorporating genetic evolution into the simulation slightly stabilized the system, thus reducing the likelihood of extinction of different types of objects. The simulation of ecosystems and the analysis of the data generated during the simulations can also be a starting point for further research, especially in relation to sustainability. Our system is publicly available, so anyone can customize and upload their own parameters, maps, objects, and biological species, as well as inheritance and behavioral habits, so they can test their own hypotheses from the data generated during its operation. The goal of this article was not to create and validate a model but to create an IT tool for evolutionary researchers who want to test their own models and to present them, for example, as animated conference presentations. The use of 3D simulation is primarily useful for educational purposes, such as to engage students and to increase their interest in biology. Students can learn in a playful way while observing in the graphical scenery how the ecosystem behaves, how natural selection helps the adaptability and survival of species, and what effects overpopulation and competition can have.

Lab head

Attila Kiss
Department
  • Department of Information Systems
About Attila Kiss
  • Mathematician at ELTE. (1985-). CSc (PhD) in 1991. Habilitation (2010). 7 PhD students got degrees. 180+ publications. Head of Information Systems Department, ELTE (2010-). Current research interests: Information systems, Data Base Theory, Data Science, Deep learning, Evolution of general intelligence, Brain intelligence, Models of consciousness, The limits and risks of deep learning, Artificial life.

Members (24)

János Szalai-Gindl
  • Eötvös Loránd University
Péter Marjai
  • Eötvös Loránd University
István Donkó
  • Eötvös Loránd University
Bálint Fazekas
  • Eötvös Loránd University
Bence Szabari
  • Eötvös Loránd University
László Nemes
  • Eötvös Loránd University
Szabolcs Jóczik
  • Eötvös Loránd University

Alumni (6)

Bálint Molnár
  • Eötvös Loránd University
András Benczúr
  • Eötvös Loránd University
Sandor Laki
  • Eötvös Loránd University
Balazs Kosa
  • Eötvös Loránd University