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MIPRO 2020 - 43rd International Convention

miproBIS - Business Intelligence Systems

Wednesday, 9/30/2020 2:00 PM - 6:15 PM, Nava 1, Hotel Admiral, Opatija

Hybrid Event

Event program
Wednesday, 9/30/2020 2:00 PM - 6:15 PM,
Nava 1, Hotel Admiral, Opatija
2:00 PM - 3:15 PM Papers  
1.S. Križanić, T. Šestanj-Perić, A. Kutnjak (University of Zagreb, Faculty of Organization and Informatics, Varaždin, Croatia)
ERP Solutions in Cloud Technologies as a Driver for Digital Transformation of Businesses 
Introduction of digital platforms has become an important part of digital transformation of businesses. Typically, these platforms include, among other things, cloud technology-based solutions. The growth of data streams caused by digital technologies results with large amounts of data that need to be processed and stored. Enterprise Resource Planning (ERP) systems as a common type of information systems which process and handle business data aim to do this with the lowest costs. The role of cloud technologies in this context is the promise to contribute to this aim. This article examines the existing literature on the role of cloud technologies in ERP solutions as a driver for digital transformation of businesses. The research questions addressed in this paper are following: How does the digital transformation paradigm affect the deployment of ERP systems in enterprises by using cloud technology? How do cloud based ERPs impact enterprise productivity? Which issues have been reported on this subject? The aim of this article is to explore the role of cloud technologies in digital transformation of businesses from the aspect of shifting from owning ERP solutions physically on the user-side to hosted ERPS on a remote service.
2.M. Cvitanušić Brečić (Agency for Science and Higher Education, Zagreb, Croatia)
Role of Business Intelligence Systems in Croatian Higher Education Quality Assurance  
Quality assurance in higher education in Croatia is conducted by and in line with the European standards and guidelines developed by the European umbrella organization for quality assurance in higher education, European Association for Quality Assurance in Higher Education (ENQA). One of the standards for internal quality assurance directly relates to the information management stating that "Institutions should ensure that they collect, analyse and use relevant information for the effective management of their programmes and other activities." Internal quality assurance is, or should be, at the core of every higher education institution, influencing its performance and quality perceived by its users, but also external evaluators (e.g. government and professional bodies that regulate or influence the work of the institution). Higher education institutions can ensure the quality of their services only through a well-developed internal quality assurance system that can provide information for timely decisions. This paper will analyse the requirements and obligations that higher education institutions have regarding the collection, processing and analysis of data within the scope of their work. In addition, it will provide examples of existing and possible use of information systems that lead to improvement of internal quality assurance system of the higher education providers.
3.T. Ivanovski (Department of Informatics University of Rijeka, Rijeka, Croatia), G. Zhang (China Agricultural University Beijing, Beijing, China), T. Jemrić (Department of Pomology University of Zagreb Faculty of Agriculture Unit of Horticulture and Landscap, Zagreb, Croatia), M. Gulić (Faculty of Maritime Studies University of Rijeka, Rijeka, Croatia), M. Matetić (Department of Informatics, University of Rijeka, Rijeka, Croatia)
Fruit Firmness Prediction Using Multiple Linear Regression 
Smart agriculture is a term used to describe the utilization of digital technologies used in optimizing agricultural food production systems. In order to increase the efficiency of manufacturing process, modern tools for collecting, storing and analyzing electronic data are used. The focus of this paper is creation and comparison of peach firmness prediction models using various machine learning algorithms. The size of the data set, which is used to construct machine learning models described in this paper, is small. Because size of the data set has a large impact on the performance of the machine learning algorithm, models of different complexities were developed to tackle this problem. Simple linear regression is used as fundamental techniques for predicting numerical outcome variable, the peach firmness using a single predictor variable. By extending simple linear regression model so that is can accommodate multiple predictors, multiple linear regression model is obtained, which is the top performing model when applied to the dataset described in this paper. The backpropagation neural network model is developed and its performance is compared to the performance of regression models.
4.A. Kutnjak, L. Hrustek, S. Križanić (University of Zagreb, Faculty of Organization and Informatics, Varaždin, Croatia)
Applying the Decision Tree Method in Identifying Key Indicators of the Digital Economy and Society Index (DESI) 
The fourth industrial revolution is affecting the way people live, work and communicate. Driven by rapidly changing technology, it reflects on a new paradigm - digital transformation (DT). DT seeks to strike a balance between improving business operations and new opportunities that the revolution brings. Essentially, DT brings changes in the business model making it fully end-user oriented. Well prepared are those organizations that accept transformation at all levels and incorporate DT into strategic plans. Because organizations approach the transformation process in different ways, so is the impact of DT on their competitiveness level also different. There are various methods for measuring the success of the transformation process, and Digital Economy and Society Index (DESI) is one of them. It measures the digital competitiveness of EU Member States through defined dimensions and sub-dimensions, and ranks the observed countries accordingly. This paper is aimed to identify key dimensions and sub-dimension that affect the rank, using the method of the decision tree for this research.
3:15 PM - 3:45 PM Invited Lecture  
A. Tjoa (TU Wien - Institute of Software Technology and Interactive Systems, Vienna, Austria)
The Linked Data Enterprise as Enabler for both Intra- and Inter-organizational Business Data Integration and Usage 
Information silos and conceptual heterogeneity within organizations remain to be main problems towards an effective utilization of available information resources. Since decades, proper data and knowledge-integration and extraction remain to be giant tasks in the information systems arena. This paper introduces the Linked-Data Concept as a possible approach for easing this problem. Linked Data can be utilized on the one hand within organizations (“intra-enterprise”) for the integration of possible existing heterogeneous systems and on the other hand as enabler of cross-organizational data integration (“inter-enterprise”) in a networked economy. The “Linked Data Enterprise” introduced in this paper is an attempt to unify both concepts of intra- and inter-enterprise networks as a very flexible and extendible instrument of data integration. The concept of “Linked Data Enterprise” is mainly based on rigorous data and knowledge sharing in a business world where the potential benefits of sharing information are perceived to be larger than the associated costs. The shortage of data-integration experts is another convincing argument for building-up a business world based on Linked Data concepts.
3:45 PM - 4:45 PM Papers  
5.T. Hlupić, D. Oreščanin, A. Petric (Poslovna inteligencija, Zagreb, Croatia)
Time Series Model for Sales Predictions in the Wholesale Industry 
The prediction process in sales is a basis for a successful ongoing planning process for any organization. Wholesale companies, being B2B oriented, have to plan their organisational environment carefully to optimize the costs and maximize revenue. As the sales process is intersected with logistics, having precise sales predictions optimizes both sales and logistics processes. In order to track the sales towards a customer, we propose a data mart built on the top of the data warehouse to be used with daily loads of outgoing invoices and uninvoiced shipments data. Predictions are based on ARIMA model, one of the most popular forecasting models for the time series. The data is aggregated on a weekly level, as it was proven to be the most useful in this process. For the prediction purposes, we are focusing only on the outgoing invoices. From the business perspective, each product is tracked with data about the sales market, customer, quantity, and the date. In the article, the process of data preparation will also be included as it is the crucial step for successful prediction.
6.D. Borissova, N. Keremedchieva (IICT-BAS, Sofia, Bulgaria), D. Keremedchiev (New Bulgarian University, Sofia, Bulgaria)
Business Intelligence Approach to Support Decision Making in Publishing Sector 
The article deals with decision making approach when generation of different offers for printing of monograph. In accordance to the law for academic staff development in Republic of Bulgaria, the monograph should be at least 100 pages with 1800 signs within page. Depending on the monograph subject, different pictures, diagrams, formulas and tables may be included. All of these considerations are taken into account when generate different offers depending on the quality of printing pictures, type of paper and number of pages. To cope with such problems a framework of web-based decision support system is proposed. The three-layer architecture described web-application is capable to process the input information about the monograph contents and to generate different offers in accordance to different users’ preferences.
7.M. Prouza (TU Wien, Vienna, Austria), Š. Brodinová (Solvistas, Vienna, Austria), A. Tjoa (Vienna University of Technology, Vienna, Austria)
Towards an Agile Framework for Business Intelligence Projects  
Business Intelligence (BI) solutions provide decision makers with information about their business and beyond as a utility to help them making better decisions for their business purpose. Establishing such a BI system in a company can often be very complex and may end up in a financial disaster due to cancelling the project because of no valuable output after a specific time. Complex technologies, different stakeholder from various domains in a company, and vague requirements are often the initial situation for a BI project. Being flexible and adaptable in the project are key factors for success. An agile approach can be the answer for a successful BI project. This paper deals with a conceptional agile framework for such a project, identifying key success factors and risks during a starting phase of a BI project.
8.S. Skansi (Fakultet hrvatskih studija, Zagreb, Croatia), K. Šekrst (Filozofski fakultet, Zagreb, Croatia), M. Kardum (Fakultet hrvatskih studija, Zagreb, Croatia)
A Different Approach for Clique and Household Analysis in Synthetic Telecom Data Using Propositional Logic 
In this paper we propose an artificial intelligence (AI) based approach for telecom data analysis, with a special focus on clique detection. Clique detection can be used to identify households, which is a major challenge in telecom data analysis and predictive analytics. Our approach does not use any form of machine learning, but another type of algorithm: satisfiability for propositional logic. This is a neglected approach in modern AI, and we aim to demonstrate that for certain tasks, such as the ones we will showcase, it is the best analytic tool available. We have used a simple DPLL satisfiability solver over an artificially generated telecom dataset (due to GDPR regulations), but our approach can be implement on any telecom data by following the SAT encoding we have developed, and the DPLL solver can be substituted by a more advanced alternative such as CDCL.
4:45 PM - 5:00 PM Break  
5:00 PM - 6:15 PM Papers  
9.G. Matošević, V. Bevanda (Sveučilište Jurja Dobrile u Puli, Pula, Croatia)
Sentiment Analysis of Tweets about COVID-19 Disease during Pandemic 
In this paper authors are presenting results of sentiment analysis of tweets – textual posts generated by users of Twitter in April 2020 in 6 selected countries: USA, UK, Spain, Italy, Sweden and Germany. There are two datasets formed in this research: first containing tweets about COVID-19 in selected countries, and second, containing tweets from top 10 politicians, users of Twitter in selected countries. Results show that most tweets from both data sources share similar positive sentiments, with minor differences between countries. Top emotions include “trust”, “fear” and “anticipation”.
10.E. Exenberger, J. Bučko (Technical University of Košice, Košice, Slovakia)
Study of Customer Behavior in Online B2B Shopping 
The main goal of our study is to analyze structure and behavior of the online shopping customers in a particular company, which is distributor of electronic components for industrial production. To reach the main goal we perform a data analysis of the data obtained from the company´s online sales process by using methods like clustering, decision tree, classification rules and association rules. The purpose of the analysis is to identify and describe the different groups of customers and the differences between them in order to provide information to support management decision making.
11.A. Kosovac, E. Muharemović, M. Begović, E. Šimić (Faculty of Traffic and Communications, Sarajevo, Bosnia and Herzegovina)
Determining the Location of Postal Centers in B&H using Machine Learning Clustering Method and GIS 
The rapid development of technology is directly affecting the growth and development of e-commerce shipments, especially in the Bussines to Customer segment. An increase in e-commerce shipments has a strong impact on the express delivery industry. In these conditions, a very significant challenge is how to organize a postal network. The problem that arises is how many postal centers, and at what locations, should be implemented in a specific geographical area in order to optimize the level of service for the users. Solving this challenge has latterly received increased attention in both industry and academia. The aim of this paper is to firstly provide a concise overview of current approaches in the process of determining the optimal location of postal centers. The second part of the paper will focus on proposing an approach that will rely on machine learning methods for clustering in defined conditions and specific geographical environment using appropriate GIS tools for spatial data analysis and visualization.
12.A. Mahmutović (Elektroprojekt, Zagreb, Croatia), M. Nikitović (Visoka škola za informacijske tehnologije, Zagreb, Croatia)
Defining ERP System Selection Methodology – Research Carried out in Small and Medium-sized Production Companies in Croatia 
Each company has to independently decide on the manner of selection of an optimal ERP solution. In order to achieve realistic results, it is necessary to, before every interview or presentation with the suppliers, and especially before the announcement of a tender, define a selection model. From the existing literature, it is evident that there are a number of factors that could be deemed important in defining a selection model. This paper recommends 4 factors groups with belonging27 factors. The questionnaires were sent to the project managers in companies that have implemented an ERP system and to ERP implementers in companies that supply ERP solutions. The importance of factor groups and individual factors represents a basis for potential buyers of ERP systems with regarding to their implementation in procurement contracts in which they should be used to a logical extent.
13.I. Kostanjevec , R. Picek (Fakultet organizacije i informatike, Sveučilište u Zagrebu, Varaždin, Croatia)
Implementacija ERP sustava iz perspektive konzultanata 
Suvremeno poslovanje danas nezamislivo je bez informacijsko komunikacijske tehnologije, a ERP sustavi predstavljaju samu srž uspjeha svakog poslovanja. Uspješna implementacija ERP sustava ključ je transformacije poslovanja poduzeća. Postoje mnoga istraživanja koja govore o različitim aspektima uvođenja ERP sustava, ali vrlo malo se istražuje kako sami konzultanti ERP sustava vide aktivnosti koje provode u mnogobrojnim implementacijama. Stoga je cilj ovog rada analizirati uvođenje ERP sustava u organizaciju, ali iz perspektive konzultanata koja obuhvaća pristupe i tijek implementacije, probleme i rizike s kojim se susreću te post implementacijske aktivnosti. Kao ishodišna točka analizirana su istraživanja te tematike pronađena u znanstvenoj literaturi, a odnose se na stanje u Hrvatskoj, ali i šire. Rad prikazuje rezultate istraživanja provedenog u hrvatskim poduzećima koja je bave uvođenjem ERP sustava, a iz kojih se može zaključiti da su poduzeća koja obavljaju djelatnosti konzultantskih usluga i uvođenja ERP rješenja u Hrvatskoj uglavnom mala ili mikro poduzeća za čijim uslugama postoji velika potražnja iz različitih grana industrije. Doprinos ovog istraživanja usmjeren je na razumijevanje kritičnih čimbenika uspjeha uvođenja ERP sustava iz perspektive konzultanata ERP sustava.

Basic information:

Boris Vrdoljak (Croatia), Mirta Baranović (Croatia), Matteo Golfarelli (Italy), Mihaela Vranić (Croatia)

Program Committee:

Marko Banek (Croatia), Mirta Baranović (Croatia), Bartosz Bebel (Poland), Ladjel Bellatreche (France), Ljiljana Brkić (Croatia), Alfredo Cuzzocrea (Italy), Todd Eavis (Canada), Enrico Gallinucci (Italy), Dragan Gamberger (Croatia), Paolo Garza (Italy), Matteo Golfarelli (Italy), Igor Mekterović (Croatia), Damir Pintar (Croatia), Sandro Skansi (Croatia), Zoran Skočir (Croatia), A. Min Tjoa (Austria), Mladen Varga (Croatia), Mihaela Vranić (Croatia), Boris Vrdoljak (Croatia)

Registration / Fees:
Price in EUR
Up to 14 September 2020
From 15 September 2020
Members of MIPRO and IEEE
Students (undergraduate and graduate), primary and secondary school teachers

The discount doesn't apply to PhD students.


Boris Vrdoljak
University of Zagreb
Faculty of Electrical Engineering and Computing
Unska 3
HR-10000 Zagreb, Croatia

Phone: +385 1 6129 532
Fax: +385 1 6129 915

The best papers will get a special award.
Accepted papers will be published in the ISSN registered conference proceedings. Presented papers in English will be submitted for inclusion in the IEEE Xplore Digital Library (and exclusively not presented papers with the justified reason for not being able to be presented).
There is a possibility that the selected scientific papers with some further modification and refinement are being published in the Journal of Computing and Information Technology (CIT).



Opatija, with its 170-year-old tourism tradition, is the leading seaside resort of the Eastern Adriatic and one of the most famous tourist destinations on the Mediterranean. With its aristocratic architecture and style, Opatija has been attracting artists, kings, politicians, scientists, sportsmen, as well as business people, bankers and managers for more than 170 years.

The tourist offer in Opatija includes a vast number of hotels, excellent restaurants, entertainment venues, art festivals, superb modern and classical music concerts, beaches and swimming pools – this city satisfies all wishes and demands.

Opatija, the Queen of the Adriatic, is also one of the most prominent congress cities in the Mediterranean, particularly important for its ICT conventions, one of which is MIPRO, which has been held in Opatija since 1979, and has attracted more than a thousand participants from over forty countries. These conventions promote Opatija as one of the most desirable technological, business, educational and scientific centers in South-eastern Europe and the European Union in general.

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