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1.
Mapping of the emergence of society 5.0 : a bibliometric analysis
Vasja Roblek, Maja Meško, Iztok Podbregar, 2021, izvirni znanstveni članek

Opis: Background and purpose: The study aims to answer a research question: With which essential cornerstones technological innovations the transformation from Society 4.0 and Industry 4.0 to Society 5.0 and Industry 5.0 is enabled? The study is important for practitioners and researchers to understand the meaning of Society 5.0 and to familiarise themselves with the drivers that will help shape Society 5.0 policies and play an important role in its further development. Therefore, the authors conducted a quantitative bibliometric study that provides insights into the importance of the topic and incorporates current characteristics and future research trends. Methodology: The study used algorithmic co-occurrence of keywords to gain a different insight into the evolution of Society 5.0. Thirty-six selected articles from the Web of Science database were analysed with the bibliometric analysis and overlay visualisation. Results: The co-occurrence analysis shows that terms artificial intelligence, cyber-physical systems, big data, Industry 4.0, Industry 5.0, open innovation, Society 5.0, super-smart society have been widely used in researches in the last three years. Conclusion: The study presents a bibliometric analysis to analyse the current and future development drivers of a Society 5.0. According to the results, the transition from Society 4.0 to Society 5.0 can be achieved by implementing knowledge and technologies in the IoT, robotics, and Big Data to transform society into a smart society (Society 5.0). In particular, the concept would enable the adaptation of services and industrial activities to individuals’ real needs. Furthermore, these technologies allow advanced digital service platforms that will eventually be integrated into all areas of life.
Ključne besede: society 5.0, industry 5.0, information society, smart society, data-driven innovations
Objavljeno v DKUM: 15.09.2022; Ogledov: 110; Prenosov: 5
URL Povezava na datoteko

2.
K-vertex: a novel model for the cardinality constraints enforcement in graph databases : doctoral dissertation
Martina Šestak, 2022, doktorska disertacija

Opis: The increasing number of network-shaped domains calls for the use of graph database technology, where there are continuous efforts to develop mechanisms to address domain challenges. Relationships as 'first-class citizens' in graph databases can play an important role in studying the structural and behavioural characteristics of the domain. In this dissertation, we focus on studying the cardinality constraints mechanism, which also exploits the edges of the underlying property graph. The results of our literature review indicate an obvious research gap when it comes to concepts and approaches for specifying and representing complex cardinality constraints for graph databases validated in practice. To address this gap, we present a novel and comprehensive approach called the k-vertex cardinality constraints model for enforcing higher-order cardinality constraints rules on edges, which capture domain-related business rules of varying complexity. In our formal k-vertex cardinality constraint concept definition, we go beyond simple patterns formed between two nodes and employ more complex structures such as hypernodes, which consist of nodes connected by edges. We formally introduce the concept of k-vertex cardinality constraints and their properties as well as the property graph-based model used for their representation. Our k-vertex model includes the k-vertex cardinality constraint specification by following a pre-defined syntax followed by a visual representation through a property graph-based data model and a set of algorithms for the implementation of basic operations relevant for working with k-vertex cardinality constraints. In the practical part of the dissertation, we evaluate the applicability of the k-vertex model on use cases by carrying two separate case studies where we present how the model can be implemented on fraud detection and data classification use cases. We build a set of relevant k-vertex cardinality constraints based on real data and explain how each step of our approach is to be done. The results obtained from the case studies prove that the k-vertex model is entirely suitable to represent complex business rules as cardinality constraints and can be used to enforce these cardinality constraints in real-world business scenarios. Next, we analyze the performance efficiency of our model on inserting new edges into graph databases with varying number of edges and outgoing node degree and compare it against the case when there is no cardinality constraints checking. The results of the statistical analysis confirm a stable performance of the k-vertex model on varying datasets when compared against a case with no cardinality constraints checking. The k-vertex model shows no significant performance effect on property graphs with varying complexity and it is able to serve as a cardinality constraints enforcement mechanism without large effects on the database performance.
Ključne besede: Graph database, K-vertex cardinality constraint, Cardinality, Business rule, Property graph data model, Property graph schema, Hypernode, Performance analysis, Fraud detection, Data classification
Objavljeno v DKUM: 10.08.2022; Ogledov: 154; Prenosov: 26
.pdf Celotno besedilo (3,43 MB)

3.
35th Bled eConference Digital Restructuring and Human (Re)action : June 26 – 29, 2022, Bled, Slovenia, Conference Proceedings
2022, zbornik

Opis: The Bled eConference, organised by the University of Maribor, Faculty of Organizational Sciences, has been shaping electronic interaction since 1988. After 2 years COVID-19 pandemic, when the conference was held online, this year we met again in Bled, Slovenia. The theme of the 35th conference is "Digital Restructuring and Human (re)Action". During the pandemic, we experienced the important role of digital technologies in enabling people and enterprises to interact, collaborate, and find new opportunities and ways to overcome various challenges. The use of digital technologies in these times has accelerated the digital transformation of enterprises and societies. It will be important to leverage this momentum for further implementation and exploitation of digital technologies that will bring positive impacts and solutions for people, enterprises and societies. The need to achieve sustainability goals and sustainable development of society has increased. Digital technologies will continue to play an important role in achieving these goals. The papers in this conference proceedings address digital transformation of enterprises, digital wellness and health solutions, digital ethics challenges, artificial intelligence and data science solutions, new and digital business models, digital consumer behaviour and solutions, including the impact of social media, restructuring of work due to digital technologies, digital education challenges and examples, and solutions for smart sustainable cities.
Ključne besede: Digital transformation, digital business, digital technologijes, innovations, digitalization, sustainable development, smart and sustainable cities and societies, digital health and wellness, artificial intelligence and data science, digital ethics, digital education, restructured work, digital consumer, social media
Objavljeno v DKUM: 23.06.2022; Ogledov: 200; Prenosov: 22
.pdf Celotno besedilo (15,08 MB)
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4.
Analiza uporabe in postavitve podatkovnega jezera : magistrsko delo
Marcel Koren, 2021, magistrsko delo

Opis: Velepodatki in podatkovna jezera sta pojma, ki jih v zadnjih letih vedno pogosteje uporabljamo v povezavi s porastom količine ustvarjenih podatkov. V magistrskem delu predstavljamo lastnosti podatkovnih jezer, čemu so namenjena, kako jih lahko vzpostavimo ter kako so povezana z velepodatki. Podrobno opišemo odprtokodno rešitev Apache Hadoop in oblačno rešitev Microsoft Azure Data Lake. Pri tem smo spoznali tudi orodja, ki jih rešitvi ponujata, med katerimi sta pomembnejši Apache Spark in Azure Databricks. V nadaljevanju predstavljamo, kako ju vzpostavimo ter izvedemo eksperiment, kjer na podlagi hitrosti izvajanja in stroškov spoznamo njune prednosti in slabosti.
Ključne besede: velepodatki, podatkovna jezera, Hadoop, Spark, Azure Data Lake
Objavljeno v DKUM: 16.12.2021; Ogledov: 461; Prenosov: 77
.pdf Celotno besedilo (2,31 MB)

5.
A Comparison of Traditional and Modern Data Warehouse Architectures : zaključno delo
Rok Virant, 2021, diplomsko delo

Opis: Data has never been as desired or valued as it is today. The value of data and information over the past decade has not only changed trends in business and the IT industry but has also changed the dynamic of work. Enormous amounts of aggregate data offer companies and other corporations the option to explore and study data samples. Data collection and information processing are new dynamic factors, not only for individuals but also for corporations. Companies and corporations who are able to process large amounts of data in the shortest possible time can place themselves in a leading position in certain professions. In this bachelor’s thesis we will describe the basic concepts and factors that have shaped new, cloud-based data warehouse technologies. At the same time, we also emphasize why and how these technologies are used. We focus on how the changing technology influenced the users and their consumption of data, the changing dynamics of work as well as the changes of data itself. In the practical part, we created two DWH environments (on-premises and cloud) that we compare with each other. In the experiment, we underlined the fact that CDWHs are in certain situations not always faster than TDWH.
Ključne besede: Data Warehouses, Cloud Computing, Outsourcing, Data, Information
Objavljeno v DKUM: 18.10.2021; Ogledov: 504; Prenosov: 76
.pdf Celotno besedilo (3,58 MB)

6.
Crosswalk of most used metadata schemes and guidelines for metadata interoperability (Version 1.0)
Milan Ojsteršek, 2021, zaključena znanstvena zbirka podatkov ali korpus

Opis: This resource provides crosswalks among the most commonly used metadata schemes and guidelines to describe digital objects in Open Science, including: - RDA metadata IG recommendation of the metadata element set, - EOSC Pilot - EDMI metadata set, - Dublin CORE Metadata Terms, - Datacite 4.3 metadata schema, - DCAT 2.0 metadata schema and DCAT 2.0 application profile, - EUDAT B2Find metadata recommendation, - OpenAIRE Guidelines for Data Archives, - OpenAire Guidelines for literature repositories 4.0, - OpenAIRE Guidelines for Other Research Products, - OpenAIRE Guidelines for Software Repository Managers, - OpenAIRE Guidelines for CRIS Managers, - Crossref 4.4.2 metadata XML schema, - Harvard Dataverse metadata schema, - DDI Codebook 2.5 metadata XML schema, - Europeana EDM metadata schema, - Schema.org, - Bioschemas, - The PROV Ontology.
Ključne besede: crosswalk, metadata, EDMI metadata set, Dublin CORE, Datacite 4.3 metadata schema, DCAT 2.0 metadata schema, UDAT B2Find metadata recommendation, OpenAIRE Guidelines for Data Archives, OpenAire Guidelines for literature repositories 4.0, OpenAIRE Guidelines for Other Research Products, OpenAIRE Guidelines for Software Repository Managers, OpenAIRE Guidelines for CRIS Managers, Crossref 4.4.2 metadata XML schema, Harvard Dataverse metadata schema, DDI Codebook 2.5 metadata XML schema, Europeana EDM metadata schema, Schema.org, Bioschemas, The PROV Ontology
Objavljeno v DKUM: 21.09.2021; Ogledov: 809; Prenosov: 37
.xlsx Raziskovalni podatki (169,58 KB)
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7.
Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)
2021, zbornik

Opis: The 7th Student Computer Science Research Conference is an answer to the fact that modern PhD and already Master level Computer Science programs foster early research activity among the students. The prime goal of the conference is to become a place for students to present their research work and hence further encourage students for an early research. Besides the conference also wants to establish an environment where students from different institutions meet, let know each other, exchange the ideas, and nonetheless make friends and research colleagues. At last but not least, the conference is also meant to be meeting place for students with senior researchers from institutions others than their own.
Ključne besede: student conference, computer and information science, artificial intelligence, data science, data mining
Objavljeno v DKUM: 13.09.2021; Ogledov: 583; Prenosov: 70
.pdf Celotno besedilo (11,87 MB)
Gradivo ima več datotek! Več...

8.
Implementation of a new reporting process in a group x
Sara Črešnik, 2021, magistrsko delo

Opis: Reporting is present in every company. Whether it is small or big, it cannot be avoided. It plays a crucial role in the process and progress of business. The quality of reporting affects the development of the work environment and the company. Since business report is a document that contains business information, which supports the decisions about the future-oriented business decisions, it is very important for it to be designed in such a way that it contains the key information for the recipient and provides support for business decisions. The reporting process can take place horizontally upwards or downwards. Content and structure vary depending on the recipient of the report. We live in an age when our every step is accompanied by digitization, computerization, artificial intelligence, mass data, the Internet of Things, machine learning, and robotics. These changes have also affected the reporting process as well as its processes. The processes of data acquisition, processing and sharing have changed. Furthermore, the data quantity has increased, whereas the speed of the time in which to prepare the reports has decreased. We can have data without information, but we cannot have information without data. There is never enough time, especially nowadays when we are used to having everything at our fingertips. These are two conflicting factors – having more data and less time to prepare quality reports. The systems are developed to optimize the process, increase efficiency and quality and, what is nowadays most important, they have been created to obtain mass data in the shortest possible time. Therefore, it is important to adapt and implement software that can help achieve our daily tasks. We must know how to process huge amounts of real-time data and deliver the information they contain. It is crucial for companies to keep up with the environment and implement changes and innovations into their business process. A company is like a living organism for it must constantly evolve and grow. As soon as it stops growing and evolving, it can fail because it starts lagging and is therefore no longer competitive to others. To deliver faster feedback, companies need data of better quality. There are tools that can improve the business process, better facilitating the capacity of the human agents. The goal is to harness the employees’ full potential and knowledge for important tasks, such as analyzing, reviewing, and understanding data and acting upon them, invoking information technology to automate repetitive processes and facilitate better communication. The focus in this master’s thesis is on the reporting process in Group X. Group X is one of the world leaders in the automotive industry, a multinational corporation based in Canada with subsidiaries around the world. The complexity of the business reporting that is implemented for the Headquarters in Canada has to address the complexity of the multinational corporation to support the decision process. The aim of the thesis is to propose a reporting process for preparing and producing reports with a huge amount of data in a very time-efficient manner. We start by examining the existing processes and upon that, identifying the processes required for the reports to reach the final recipients. Our goal is to identify the toolset, which would increase efficiency, accuracy, credibility, and reduce errors in the fastest possible time. We investigate a short-term and a long-term solution. By a short-term solution, we mean a system, program, or a tool that can help us increase our potential by using digital resources, which are already existing in the organization. By a long-term solution, we mean a solution, which requires employment of specialized future tools in the field of reporting and in repetitive processes, which we can identify with current knowledge and expectations for development. This includes machine learning, robotic process automatization, artificial intelligence.
Ključne besede: Consolidated reporting, reporting process, robotic process automatization, business intelligence, artificial intelligence, machine learning, SharePoint, Big Data, digital transformation, electronic data interchange.
Objavljeno v DKUM: 01.09.2021; Ogledov: 431; Prenosov: 3
.pdf Celotno besedilo (1,71 MB)

9.
Discovering associations in COVID-19 related research papers
Iztok Fister, Karin Fister, Iztok Fister, 2020, elaborat, predštudija, študija

Ključne besede: data science, metro maps, optimization
Objavljeno v DKUM: 15.03.2021; Ogledov: 663; Prenosov: 35
.pdf Celotno besedilo (475,27 KB)
Gradivo ima več datotek! Več...

10.
Trendi digitalizacije v podjetju-industrija 4.0
Sara Vaupotič, 2020, diplomsko delo

Opis: Sam začetek industrijske revolucije sega v začetke druge polovice 18.stoletja z začetkom parne lokomotive ter strojev za predenje. Skozi zgodovino so ljudje vedno iskali izboljšave ter delali s tem, kar so imeli na voljo. Od samih začetkov parnih strojev, odkritja električne energije, prvih telegramov in telefonov, avtomobilov, letal, vse do razvoja digitalne tehnologije, poslovne programske opreme, razvoja prvih računalnikov ter superračunalnikov, razvoja komunikacijske tehnologije, prvih prenosnikov ter industrijske robotike, pa se trenutno razvija četrta industrijska revolucija, znana kot industrija 4.0. Razvija se v smeri digitalizacije in avtomatizacije, pametnih tovarn in naprav, povezanih med seboj (angl. Internet of Things), sistemov za shranjevanje velikih količin informacij ter podatkov (angl. Big Data) in proizvodnih zmogljivosti, ki lahko podatke shranjujejo samostojno kadarkoli in brez človeške prisotnosti. Tako proizvodnja, kot poslovanje potekata v veliki meri digitalno. Digitalizacija, ki je že zamenjala nekoč tradicionalno kulturo v podjetju, vso papirno hrambo so že nadomestile različne računalniške rešitve ter sistemi za lažje, bolj pregledno, brez papirno ter hitrejšo poslovanje (DMS). Za hitrejši dostop do podatkov, lažje in bolj pregledno poslovanje ter zbranost podatkov na enem mestu pa skrbijo sistemi za načrtovanje in pregledno planiranje virov podjetja (ERP), ki se lahko povezujejo tudi s sistemi za upravljanje proizvodnje (MES). Prav zaradi potrebe po izboljšavah, večji učinkovitosti, lažjim pregledom nad stroški in logistiko pa je nastala četrta industrijska revolucija. Kot dober primer podjetja, ki smernice četrte industrijske revolucije že v večji meri upošteva, pa bomo predstavili tehnološko podjetje (Xiaomi), ki trenutno zaseda četrto mesto na trgu pametnih telefonov.
Ključne besede: industrijske revolucije, industrija 4.0, IoT, Big Data, ERP, MES, DMS, elektronsko poslovanje, Xiaomi.
Objavljeno v DKUM: 23.11.2020; Ogledov: 760; Prenosov: 149
.pdf Celotno besedilo (767,12 KB)

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