Exploratory Analysis of Pairwise Interactions in Online Social Networks

In the last few decades sociologists were trying to explain human behaviour by analysing social networks, which requires access to data about interpersonal relationships. This represented a big obstacle in this research field until the emergence of online social networks (OSNs), which vastly facilitated the process of collecting such data. Nowadays, by crawling public profiles on OSNs, it is possible to build a social graph where "friends" on OSN become represented as connected nodes. OSN connection does not necessarily indicate a close real-life relationship, but using OSN interaction records may reveal real-life relationship intensities, a topic which inspired a number of recent researches. Still, published research currently lacks an extensive exploratory analysis of OSN interaction records, i.e. a comprehensive overview of users' interaction via different ways of OSN interaction. In this paper we provide such an overview by leveraging results of conducted extensive social experiment which managed to collect records for over 3,200 Facebook users interacting with over 1,400,000 of their friends. Our exploratory analysis focuses on extracting population distributions and correlation parameters for 13 interaction parameters, providing valuable insight in online social network interaction for future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4 on pages 422 to 42

Intelligent data sources and integrated data repository as a foundation for business intelligence analysis

Abstract: Data mining and data analysis in general demonstrate high dependency on data quality. Gathering the right data of high enough quality takes most of researcher’s time and often demonstrates need for some additional data to be parsed. In order to eliminate or at least reduce required effort for this first phase of every analysis, authors of this paper present the idea of Integrated Data Repository and Intelligent Data Source. Concepts of those components are presented and approach to their development is suggested together with the high-level view of the system architecture. Finally, an experimental implementation is described

Poboljšanje rezultata hijerarhijskog grupiranja podataka primjerenijim tretiranjem tipova podataka i prilagodbom mjere udaljenosti

Hierarchical clustering method is used to assign observations into clusters further connected to form a hierarchical structure. Observations in the same cluster are close together according to the predetermined distance measure, while observations belonging to different clusters are afar. This paper presents an implementation of specific distance measure used to calculate distances between observations which are described by a mixture of variable types. Data mining tool ‘Orange’ was used for implementation, testing, data processing and result visualization. Finally, a comparison was made between results obtained by using already available widget and the output of newly programmed widget which employs new variable types and new distance measure. The comparison was made on different well-known datasets.Hijerarhijsko grupiranje se koristi za grupiranje objekata promatranja u grupe koje se dalje pripajaju te tako tvore hijerarhijsku strukturu. Prema odabranoj mjeri udaljenosti instance koje pripadaju istoj grupi su ’blizu’ dok su instance koje pripadaju različitim grupama ’udaljenije’. U ovom članku prikazana je implementacija specifične mjere udaljenosti koja se koristi za izračun udaljenosti izme.u instanci koje su opisane atributima različitih tipova podataka. Alat za dubinsku analizu podataka ’Orange’ je odabran za implementaciju, testiranje, obradu podataka te vizualizaciju rezultata. članak uz opis specifikacije novih varijabli te mjere udaljenosti tako.er daje usporedbu rezultata dobivenih otprije poznatim modulom i novim modulom koji koristi nove tipove podataka i novu mjeru udaljenosti. Usporedba je napravljena nad različitim poznatim skupovima podataka

Improved Visualization of Frequent Itemset Relationships Using the Minimal Spanning Tree Algorithm

Descriptive data mining techniques offer a way of extracting useful information out of large datasets and presenting it in an interpretable fashion to be used as a basis for future decisions. Since users interpret information most easily through visual means, techniques which produce concise, visually attractive results are usually preferred. We define a method, which converts transactional data into tree-like data structures, which depict important relationships between items contained in this data. The new approach we propose is offering a way to mitigate the loss of information present in previously developed algorithms, which use mined frequent itemsets and construct tree structures. We transfer the problem to the domain of graph theory and through minimal spanning tree construction achieve more informative visualizations. We highlight the new approach with comparison to previous ones by applying it on a real-life datasets – one connected to market basket data and the other from the educational domain

Automatic extraction of learning concepts from exam query repositories

Educational data mining (or EDM) is an emerging interdisciplinary research field concerned with developing methods for exploring the specific and diverse data encountered in the field of education. One of the most valuable data sources in the educational domain are exam query repositories, which are commonly pre-dating modern e-learning systems. Exam queries in those repositories usually lack additional metadata which helps establish relationships between questions and corresponding learning concepts whose adoption is being tested. In this paper we present our novel approach of using data mining methods able to automatically annotate pre-existing exam queries with information about learning concepts they relate to, leveraging both textual and visual information contained in the queries. This enables automatic categorization of exam queries which allows for both better insight into the usability of the current exam query corpus as well as easier reporting of learning concept adoption after these queries are used in exams. We apply this approach to real-life exam questions from a high education university course and show validation of our results performed in consultation with experts from the educational domain

Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

Designing concise representation of correlations among elements in transactional data

U doktorskom radu je obrađena problematika sažetih prikaza povezanosti transakcijskih elemenata. U svrhu dobivanja sažetih prikaza povezanosti korištene su dvije sasvim različite poznate deskriptivne metode dubinske analize podataka: stvaranje asocijacijskih pravila i hijerarhijsko grupiranje. Metoda stvaranja asocijacijskih pravila je iskorištena za izoliranje zatvorenih čestih kolekcija elemenata. Spomenute kolekcije se povezuju u stablastu strukturu pomoću dvije razvijene strategije koje su i implementirane. Da bi gubitak informacije u prikazanoj strukturi bio minimalan, napravljena je poveznica s teorijom grafova te je iskorišten algoritam za razapinjanje minimalnog stabla. S druge strane problemu se prišlo razradom različitih mjera zanimljivosti vezanih uz povezivanje elemenata u transakcijama. Ove su mjere prilagođene hijerarhijskom grupiranju te je za njihovo testiranje razvijena i programska podrška koja uz korištenje različitih mjera omogućuje i obrezivanje na osnovu podrške. Dva navedena pristupa su testirana nad umjetno konstruiranim podacima koji bi trebali analitičaru omogućiti lakši odabir pogodne mjere udaljenosti te nad referentnim skupovima podataka. Tu su se pokazale vrlo dobrima za jednostavan i brzi uvid u odnose među elementima transakcija. Razvijene metode su također korištene nad podacima vezanim uz visokoškolsko obrazovanje te pokazale potencijal za sažeti prikaz odnosa među elementima ispita – među pitanjima i uz njih vezanih koncepataThis doctoral thesis proposes new approaches in designing concise representation of correlations among elements in transactional data. For this purpose two different known data mining methods are used: association rules generation and hierarchical clustering. Method of association rules generation is used to isolate closed frequent itemsets which are further connected to form a tree structure. For this purpose two strategies are developed and implemented. To minimize information loss, graph theory was used along with its existing algorithms for minimal spanning tree creation. Additionally, different interest measures that demonstrate certain relationships between transaction elements were modulated to serve in hierarchical clustering. Software implementation of these measures along with support based pruning ability is also made. Two different approaches are tested both on artificial datasets constructed to serve analysts in selecting the most appropriate measure as well as on known and easily reachable reference datasets. They proved to be very useable as descriptive methods that provide quick and easily interpretable structures that represent relationships between transactional elements. Developed methods were also used on the real-life educational data and showed great potential in displaying relationships between test elements – questions and related concepts

Application of business intelligence in academic environment

Unutar svake organizacije, pa tako i na visokoškolskim ustanovama, vrlo je bitno kvalitetno upravljati znanjem što omogućava prilagodbu novim okolnostima te stalna unapređenja različitih procesa i aktivnosti. Sustavi poslovne inteligencije omogućuju efikasnu sintezu i prenošenje znanja. Kao takvi omogućuju zaposlenicima na svim razinama unutar organizacije bolji uvid u čimbenike koji utječu na njihov segment poslovanja i donošenje boljih odluka. U radu je predstavljena teoretska podloga kreiranja organizacijskog znanja. Ona je isprepletena s cjelokupnim procesom dubinske analize podataka te povezana sa sustavima poslovne inteligencije, od faze njihovog planiranja do faze intenzivnog korištenja sustava. Značajni segmenti poslovne inteligencije su različite statističke analize i dubinska analiza podataka. U radu je prikazano kako se ove analize mogu iskoristiti za unapređenje specifičnog područja djelovanja visokoškolske ustanove – u nastavi. Poseban je naglasak dan na mogućnost unapređenja izvođenja nastave, poboljšanja testiranja studenata te potencijalnog povećanja prolaznosti bez snižavanja kriterija ocjenjivanja. Također su dani prijedlozi proširenja modela podataka koji bi omogućili otkrivanje novih zakonitosti i daljnja poboljšanja.For every organization, including universities, it is very important to efficiently govern with knowledge. This enables adoption to new circumstances and improvement in different processes and business related activities. Business intelligence systems enable efficient creation and transfer of knowledge. They enable employees at every organizational level to have better view of factors affecting their business and make better decisions. This thesis introduces theoretical background of organizational knowledge creation. Presented theory is interlaced with data mining process and further related to business intelligence systems, from their planning phase to phase of their extensive usage. Important segments of business intelligence are various statistical analysis same as data mining. This thesis presents how could these segments play important role within specific ‘business’ area at universities – improvement of teaching related activities. Focus has been put particularly on efficiency of the teaching process, objectivity at student’s work evaluation and potential improvement in student passing rate without reduction in grading criteria. At the end, proposals for data model extension were given, thus enabling better insight in real world rules same as further improvements

Designing concise representation of correlations among elements in transactional data

U doktorskom radu je obrađena problematika sažetih prikaza povezanosti transakcijskih elemenata. U svrhu dobivanja sažetih prikaza povezanosti korištene su dvije sasvim različite poznate deskriptivne metode dubinske analize podataka: stvaranje asocijacijskih pravila i hijerarhijsko grupiranje. Metoda stvaranja asocijacijskih pravila je iskorištena za izoliranje zatvorenih čestih kolekcija elemenata. Spomenute kolekcije se povezuju u stablastu strukturu pomoću dvije razvijene strategije koje su i implementirane. Da bi gubitak informacije u prikazanoj strukturi bio minimalan, napravljena je poveznica s teorijom grafova te je iskorišten algoritam za razapinjanje minimalnog stabla. S druge strane problemu se prišlo razradom različitih mjera zanimljivosti vezanih uz povezivanje elemenata u transakcijama. Ove su mjere prilagođene hijerarhijskom grupiranju te je za njihovo testiranje razvijena i programska podrška koja uz korištenje različitih mjera omogućuje i obrezivanje na osnovu podrške. Dva navedena pristupa su testirana nad umjetno konstruiranim podacima koji bi trebali analitičaru omogućiti lakši odabir pogodne mjere udaljenosti te nad referentnim skupovima podataka. Tu su se pokazale vrlo dobrima za jednostavan i brzi uvid u odnose među elementima transakcija. Razvijene metode su također korištene nad podacima vezanim uz visokoškolsko obrazovanje te pokazale potencijal za sažeti prikaz odnosa među elementima ispita – među pitanjima i uz njih vezanih koncepataThis doctoral thesis proposes new approaches in designing concise representation of correlations among elements in transactional data. For this purpose two different known data mining methods are used: association rules generation and hierarchical clustering. Method of association rules generation is used to isolate closed frequent itemsets which are further connected to form a tree structure. For this purpose two strategies are developed and implemented. To minimize information loss, graph theory was used along with its existing algorithms for minimal spanning tree creation. Additionally, different interest measures that demonstrate certain relationships between transaction elements were modulated to serve in hierarchical clustering. Software implementation of these measures along with support based pruning ability is also made. Two different approaches are tested both on artificial datasets constructed to serve analysts in selecting the most appropriate measure as well as on known and easily reachable reference datasets. They proved to be very useable as descriptive methods that provide quick and easily interpretable structures that represent relationships between transactional elements. Developed methods were also used on the real-life educational data and showed great potential in displaying relationships between test elements – questions and related concepts

Application of business intelligence in academic environment

Unutar svake organizacije, pa tako i na visokoškolskim ustanovama, vrlo je bitno kvalitetno upravljati znanjem što omogućava prilagodbu novim okolnostima te stalna unapređenja različitih procesa i aktivnosti. Sustavi poslovne inteligencije omogućuju efikasnu sintezu i prenošenje znanja. Kao takvi omogućuju zaposlenicima na svim razinama unutar organizacije bolji uvid u čimbenike koji utječu na njihov segment poslovanja i donošenje boljih odluka. U radu je predstavljena teoretska podloga kreiranja organizacijskog znanja. Ona je isprepletena s cjelokupnim procesom dubinske analize podataka te povezana sa sustavima poslovne inteligencije, od faze njihovog planiranja do faze intenzivnog korištenja sustava. Značajni segmenti poslovne inteligencije su različite statističke analize i dubinska analiza podataka. U radu je prikazano kako se ove analize mogu iskoristiti za unapređenje specifičnog područja djelovanja visokoškolske ustanove – u nastavi. Poseban je naglasak dan na mogućnost unapređenja izvođenja nastave, poboljšanja testiranja studenata te potencijalnog povećanja prolaznosti bez snižavanja kriterija ocjenjivanja. Također su dani prijedlozi proširenja modela podataka koji bi omogućili otkrivanje novih zakonitosti i daljnja poboljšanja.For every organization, including universities, it is very important to efficiently govern with knowledge. This enables adoption to new circumstances and improvement in different processes and business related activities. Business intelligence systems enable efficient creation and transfer of knowledge. They enable employees at every organizational level to have better view of factors affecting their business and make better decisions. This thesis introduces theoretical background of organizational knowledge creation. Presented theory is interlaced with data mining process and further related to business intelligence systems, from their planning phase to phase of their extensive usage. Important segments of business intelligence are various statistical analysis same as data mining. This thesis presents how could these segments play important role within specific ‘business’ area at universities – improvement of teaching related activities. Focus has been put particularly on efficiency of the teaching process, objectivity at student’s work evaluation and potential improvement in student passing rate without reduction in grading criteria. At the end, proposals for data model extension were given, thus enabling better insight in real world rules same as further improvements