Iskanje po katalogu digitalne knjižnice

Opis: Skills and competencies related to data literacy and data management are indispensable in today’s data-intensive research environment, both in STEM as well as in the humanities and social sciences. In recent years, there has been a significant shift in scientific communication towards open science, and many new approaches, tools and technologies have emerged, enabling a research process that goes beyond the traditional way of research. Open science has the potential to lead researchers to an open, collaborative, transparent, reproducible and, therefore, effective research process. The University of Maribor Summer School offers PhD students and young researchers an opportunity to learn and develop skills in the fields of research data management, open access to research results, the use of open research infrastructure, supercomputing resources and the European Open Science Cloud. With lectures and workshops led by domestic and international experts, the course will provide a deeper understanding of these topics and insight into valuable practical experience.
Ključne besede: scientific research, open science, open publications, research data
Objavljeno v DKUM: 21.12.2022; Ogledov: 896; Prenosov: 11
Povezava na datoteko
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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: 771; Prenosov: 96
Celotno besedilo (3,43 MB)

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: 1204; Prenosov: 127
Celotno besedilo (2,31 MB)

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: 2030; Prenosov: 70
Raziskovalni podatki (169,58 KB)
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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: 894; Prenosov: 7
Celotno besedilo (1,71 MB)