| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Search the digital library catalog Help

Query: search in
search in
search in
search in
* old and bologna study programme

Options:
  Reset


1 - 10 / 10
First pagePrevious page1Next pageLast page
1.
2.
Towards trusted data sharing and exchange in agro-food supply chains: design principles for agricultural data spaces
Martina Šestak, Daniel Copot, original scientific article

Abstract: In the modern agricultural landscape, realizing data’s full potential requires a unified infrastructure where stakeholders collaborate and share their data to gain insights and create business value. The agricultural data ecosystem (ADE) serves as a crucial socio-technical infrastructure, aggregating diverse data from various platforms and, thus, advertising sustainable agriculture and digitalization. Establishing trustworthy data sharing and exchange in agro-food value chains involves socioeconomic and technological elements addressed by the agricultural data space (ADS) and its trust principles. This paper outlines key challenges to data sharing in agro-food chains impeding ADE establishment based on the review of 27 studies in scientific literature. Challenges mainly arise from stakeholders’ mistrust in the data-sharing process, inadequate data access and use policies, and unclear data ownership agreements. In the ADE context, interoperability is a particularly challenging topic for ensuring the long-term sustainability of the system. Considering these challenges and data space principles and building blocks, we propose a set of design principles for ADS design and implementation that aim to mitigate the adverse impact of these challenges and facilitate agricultural data sharing and exchange.
Keywords: data sharing and exchange, agro-food supply chain, design principles, agricultural data space, agricultural data ecosystem
Published in DKUM: 30.11.2023; Views: 376; Downloads: 22
.pdf Full text (948,02 KB)
This document has many files! More...

3.
Vzpostavitev ekosistema Hadoop : diplomsko delo
Mitja Cesar, 2023, undergraduate thesis

Abstract: V tej diplomski nalogi smo raziskali ogrodje Hadoop, ki s svojimi komponentami tvori celovito rešitev za hranjenje in analiziranje velikih podatkov. V diplomski nalogi najprej predstavimo ogrodje in njegove glavne oziroma najbolj uporabljene komponente, kot so HDFS, MapReduce in YARN. Sledi primer vzpostavitve ogrodja na Linux distribuciji Ubuntu, ter primeri uporabe, ki podajajo smernice za shranjevanje in analiziranje različnih vrst podatkov s Hadoop.
Keywords: Hadoop, vele podatki, porazdeljen datotečni sistem, shranjevanje in analiza podatkov
Published in DKUM: 05.10.2023; Views: 359; Downloads: 16
.pdf Full text (2,32 MB)

4.
Analiza in primerjava proceduralnih jezikov v relacijskih podatkovnih bazah : diplomsko delo
Sara Zadravec, 2022, undergraduate thesis

Abstract: Življenje danes si težko predstavljamo brez shranjevanja in obdelovanja podatkov, kar nam med drugim omogočajo podatkovne baze. Proceduralni jeziki v podatkovnih bazah združujejo jezik podatkovnih baz in proceduralni programski jezik ter tako omogočajo izvajanje proceduralne logike v podatkovnih bazah. V sklopu diplomske naloge smo opisali osnove podatkovnih baz, procedur, prožilcev in funkcij ter slednje tri primerjali. Opisali smo štiri proceduralne jezike, ki pripadajo štirim trenutno najbolj priljubljenim sistemom za upravljanje podatkovnih baz. Izbrane proceduralne jezike smo primerjali in izdelali praktični primer, s pomočjo katerega smo prikazali razlike med jeziki v sintaksi in času izvajanja.
Keywords: procedura, proceduralni jezik, relacijske podatkovne baze
Published in DKUM: 20.10.2022; Views: 476; Downloads: 55
.pdf Full text (12,47 MB)

5.
K-vertex: a novel model for the cardinality constraints enforcement in graph databases : doctoral dissertation
Martina Šestak, 2022, doctoral dissertation

Abstract: 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.
Keywords: Graph database, K-vertex cardinality constraint, Cardinality, Business rule, Property graph data model, Property graph schema, Hypernode, Performance analysis, Fraud detection, Data classification
Published in DKUM: 10.08.2022; Views: 692; Downloads: 77
.pdf Full text (3,43 MB)

6.
Analiza uporabe in postavitve podatkovnega jezera : magistrsko delo
Marcel Koren, 2021, master's thesis

Abstract: 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.
Keywords: velepodatki, podatkovna jezera, Hadoop, Spark, Azure Data Lake
Published in DKUM: 16.12.2021; Views: 1125; Downloads: 121
.pdf Full text (2,31 MB)

7.
Analiza in primerjava storitev dokumentnih podatkovnih shramb v oblaku : diplomsko delo
Bard Grujič, 2021, undergraduate thesis

Abstract: Namen diplomskega dela je predstaviti računalništvo v oblaku in podrobno raziskati ter analizirati prednosti in morebitne slabosti ne-relacijskih podatkovnih shramb, s posebno pozornostjo na dokumentnem tipu. Končni cilj je analizirati in primerjati dokumentne podatkovne shrambe kot storitve. Cilji diplomskega dela so torej analiza in primerjava oblačnih storitev dokumentnih podatkovnih shramb, izvedba testne implementacije, ki nam bo omogočila to analizo in ugotovitev funkcionalnosti, ki nam takšne shrambe ponujajo.
Keywords: računalništvo v oblaku, dokumentne podatkovne shrambe, oblačne storitve podatkovnih shramb
Published in DKUM: 18.10.2021; Views: 1144; Downloads: 95
.pdf Full text (1,49 MB)

8.
Uporaba sklada Elastic za obdelavo in vizualizacijo podatkov : diplomsko delo
Matej Sojer, 2021, undergraduate thesis

Abstract: V diplomskem delu smo predstavili pojem masovnih podatkov in poudarili njihovo pomembnost za prihodnost moderne informacijske družbe. Opisali smo podatkovno rudarjenje, način pridobivanja znanja iz masovnih podatkov in strojno učenje, način obdelave podatkov. V nadaljevanju smo predstavili in analizirali Sklad Elastic, ekosistem komponent za shranjevanje, pridobivanje in obdelavo podatkov, ki smo ga uporabili pri razvoju spletne strani za iskanje med dobitniki Nobelove nagrade.
Keywords: masovni podatki, Sklad Elastic, Elasticsearch, vizualizacija, obdelava podatkov
Published in DKUM: 18.10.2021; Views: 1042; Downloads: 39
.pdf Full text (1,32 MB)

9.
A Comparison of Traditional and Modern Data Warehouse Architectures : zaključno delo
Rok Virant, 2021, undergraduate thesis

Abstract: 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.
Keywords: Data Warehouses, Cloud Computing, Outsourcing, Data, Information
Published in DKUM: 18.10.2021; Views: 1049; Downloads: 170
.pdf Full text (3,58 MB)

10.
Primerjava podatkovnih baz iz vidika shranjevanja dokumentov JSON : zaključno delo
Goran Kirov, 2021, undergraduate thesis

Abstract: Diplomsko delo je sestavljeno iz teoretičnega in praktičnega dela. Najprej so opisane osnove relacijskih in nerelacijskih podatkovnih baz, nato pa njihovi najbolj znani predstavniki. Nato sledi razlaga formata za izmenjavo podatkov JSON in dela z njim v podatkovnih bazah (ustvarjanje, branje, posodabljanje, brisanje podatkov). Ključni del diplomske naloge je praktično delo, kjer smo merili in analizirali podatkovne baze pri shranjevanju dokumentov JSON. Najprej opišemo implementacijo aplikacije za samodejno testiranje, kjer merimo čas in porabo pomnilnika. Testiranje je bilo izvedeno nad manjšimi in večjimi dokumenti. Testirali smo podatkovne baze MySQL, PostgreSQL in MongoDB. Na koncu analiziramo dobljene rezultate in podamo zaključne ugotovitve.
Keywords: JSON, SQL, NoSQL, podatkovna baza
Published in DKUM: 18.10.2021; Views: 1284; Downloads: 127
.pdf Full text (1,27 MB)

Search done in 1.85 sec.
Back to top
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica