Social Business Intelligence: a Literature Review and Research Agenda

The domains of Business Intelligence (BI) and social media have meanwhile become significant research fields. While BI aims at supporting an organization’s decisions by providing relevant analytical data, social media is an emerging source of personal and individual knowledge, opinion, and attitudes of stakeholders. For a while, a convergence of the two domains can be observed in real-world implementations and research, resulting in concepts like social BI. Many research questions still remain open – or even worse – are not yet formulated. Therefore, the paper aims at articulating a research agenda for social BI. By means of a literature review we systematically explored previous work and developed a framework. It contrasts social media characteristics with BI design areas and is used to derive the social BI research agenda. Our results show that the integration of social media (data) into a BI system has impact on almost all BI design objects

An integrated personalization framework for SaaS-based cloud services

Software as a Service (SaaS) has recently emerged as one the most popular service delivery models in cloud computing. The number of SaaS services and their users is continuously increasing and new SaaS service providers emerge on a regular basis. As users are exposed to a wide range of SaaS services, they may soon become more demanding when receiving/consuming such services. Similar to the web and/or mobile applications, personalization can play a critical role in modern SaaS-based cloud services. This paper introduces a fully designed, cloud-enabled personalization framework to facilitate the collection of preferences and the delivery of corresponding SaaS services. The approach we adapt in the design and development of the proposed framework is to synthesize various models and techniques in a novel way. The objective is to provide an integrated and structured environment wherein SaaS services can be provisioned with enhanced personalization quality and performance

Processamento analítico espacial e exploratório integrando dados estruturados e semiestruturados.

Tecnologias de Business Intelligence (BI) têm sido utilizadas com sucesso para fins de análise de dados. Tradicionalmente, essa análise é realizada em um contexto restrito e bem controlado, onde as fontes de dados são estruturadas, periodicamente carregadas, estáticas e totalmente materializadas. Atualmente, há uma diversidade de dados nos mais diversos formatos, a exemplo de RDF (Resource Description Framework), um formato semiestruturado, semanticamente rico e externo à infraestrutura de BI. Embora tal formato seja enriquecido semanticamente, e muitas vezes possua um componente espacial, realizar a análise é um desafio. Nessa perspectiva, uma nova categoria de ferramentas analíticas vem surgindo. As ferramentas exploratórias (Exploratory OLAP), como são conhecidas, se caracterizam pela descoberta, aquisição e integração de dados externos em ambientes comuns de análise. Do nosso conhecimento, até a presente data, existem apenas duas ferramentas exploratórias propostas na literatura e elas apresentam duas grandes limitações: exploram apenas fontes de dados estruturadas; e não há exploração do componente espacial dos dados integrados. São ferramentas exploratórias OLAP, e não ferramentas exploratórias SOLAP. Baseando-se nessas ferramentas, este trabalho propõe uma abordagem exploratória SOLAP que integra dados semiestruturados espaciais semânticos com fontes de dados estruturados espaciais tradicionais. Um sistema, denominado ExpSOLAP, que dá suporte a consultas SOLAP on-line sob as duas fontes de dados foi desenvolvido. Por fim, o sistema ExpSOLAP é avaliado através de um exemplo prático, no contexto da base de dados obtida no Linked Movie Data Base, utilizando RDF e banco de dados relacional. Foram formuladas consultas que validaram a análise convencional e espacial na exploração de ambas fontes de dados.Business Intelligence (BI) technologies have been successfully applied for data analysis purposes. Traditionally, such analysis is performed in well-controlled and restricted context, where data sources are structured, periodically loaded, static and fully materialized. Nowadays, there is a plenty of data in different formats such as the Resource Description Framework (RDF), a semi-structured and semantically rich format external to the BI infrastructure. Although such data formats are enriched by semantics and contains a spatial data component, performing data analysis is challenging. As a result, the Exploratory OLAP field has emerged for discovery, acquisition, integration and query such data, aiming at performing a complete and effective analysis on both internal and external data. To the best of our knowledge, there are only two exploratory tools proposed in the literature and they have two major limitations due to only structured data sources can be explored and there is no exploration of the spatial component of the integrated data. While they are exploratory OLAP tools, they are not exploratory SOLAP tools. Based on these tools, this work proposes an Exploratory SOLAP approach that integrates semantic spatial semi-structured data with traditional spatial structured data sources. A system named ExpSOLAP, which supports online SOLAP queries on both data sources, was developed. Finally, a case study was carried out in order to evaluate the ExpSOLAP system based on a dataset originating from the Linked Movie Data Base and using RDF and relational datasets. The formulated queries enabled to validate the conventional and spatial analysis from both data sources.CNP

Ontology transformation into taxonomic structure algorithm for evidential reasoning

Modeliranje podataka predstavlja proces stvaranja modela podataka kojim se simboliˇcki opisuju hijerarhijski odnosi izmed¯u stvari i dogad¯aja u nekom sustavu ili procesu. Ontologije kao napredniji naˇcin modeliranja podataka na raspolaganju imaju ve´ci broj jeziˇcnih konstrukata u odnosu na uobiˇcajene naˇcine modeliranja. Uz navedeno, ontologije na raspolaganju imaju i zaklju ˇcivaˇc koji na temelju znaˇcenja daje logiˇcke zakljuˇcke koji utjeˇcu na hijerarhijsku strukturu i raspored objekata (instanci) klasa. S druge strane, evidencijsko zakljuˇcivanje predstavlja najnoviju metodu višekriterijskog odluˇcivanja temeljenu na Dempster-Shafer teoriji koja se odnosi na donošenje odred¯enih odluka s prisutnim, a cˇesto i konfliktnim, kriterijima. Kako bi se evidencijsko zakljucˇivanje moglo primijeniti nad OWL ontologijama potrebno je napraviti odred¯ene prilagodbe s obzirom da se OWL ontologija strukturno razlikuje od zahtjeva za primjenu evidencijskog zakljuˇcivanja. U ovoj disertaciji predstavljen je model prilagodbe najˇceš´ce korištene OWL ontologije u taksonomsku strukturu temeljen na HermiT zakljuˇcivaˇcu. Predloženi model dijeli se na tri glavna dijela. Prvi dio odnosi se na primjenu HermiT zakljuˇcivaˇca nad ulaznom ontologijom, a kao rezultat dobija se hijerarhijska struktura koja predstavlja ontologiju pri ˇcemu se u obzir uzimaju svi korišteni aksiomi OWL ontologije. Drugi dio odnosi se na primjenu algoritma za prilagodbu ontologije u taksonomsku strukturu gdje dobivena taksonomija zadovoljava pravila op´ceg stabla, odnosno, nad takvom taksonomijom mogu´ce je primjeniti evidencijsko zakljuˇcivanje. Tre´ci dio odnosi se na pripremu za proces primjene evidencijskog zakljuˇcivanja. Najbitniji dio modela prilagodbe je predloženi algoritam prilagodbe koji na temelju skupa pravila i podpravila rješava specifiˇcne situacije koje se mogu na´ci u ontološkoj strukturi u svrhu dobivanja taksonomske strukture. Kako bi se provjerila ispravnost prilagod¯ene strukture, predložena je metoda evaluacije koja se temelji na provjeri definirana tri svojstva, a to su svojstvo klasa, svojstvo veza i svojstvo povezanosti. Primjena predloženog modela prilagodbe, algoritma i metode evaluacije prikazana je na skupu ulaznih ontologija iz dostupnih repozitorija. Dobiveni rezultati prikazuju vremena izvršavanja zasebno za HermiT zakljuˇcivaˇc i predloženi algoritam prilagodbe, te vrijednosti definirana tri svojstva evaluacije prilagodbe. Iz rezultata je vidljivo kako bi se takav naˇcin prilagodbe mogao iskoristiti na ve´c postoje´cim modelima prikazanih u OWL ontologiji, a koji opisuju neki objekt ocjenjivanja u svrhu primjene evidencijskog zakljuˇcivanjaData modelling represents the process of creating data model which symbolically describes hierarchical relationships between things and events within a system or process. Ontologies as more advanced data modelling approach have greater number of available language constructs compared to usual data modelling methods. Also, ontologies use reasoner which generates logical conclusions based on meaning that affect hierarchical structure and instance membership. On the other hand, evidential reasoning represents the latest multiple criteria decision method based on Dempster-Shafer theory related to the decision making process with present, but often conflicted, criteria. In order to apply evidential reasoning over OWL ontologies it is necessary to apply some adjustments considering that OWL ontology is structurally different from demands that impose evidential reasoning. This dissertation presents adjustment model for commonly used OWL ontology into taxonomic structure based on HermiT reasoner. The proposed model is divided into three main parts. The first part is related to application of HermiT reasoner over input ontology resulting in hierarchical structure that represents original ontology regarding all used OWL ontology axioms. The second part is related to the application of ontology adjustment algorithm into taxonomic structure where resulting taxonomy satisfies general tree rules, i.e. evidential reasoning can be applied over such taxonomy. The third part is related to preparation for evidential reasoning application. The most significant part of the adjustment model is proposed adjustment algorithm that addresses specific situations existing in ontology structure based on set of rules and sub rules in order to get taxonomic structure. The evaluation method based on satisfying of three predefined properties (class property, relation property and connectivity property) in order to verify the adjusted structure correctness is proposed. The application of proposed adjustment model, adjustment algorithm and evaluation method is performed over the input set of ontologies from available online repositories. The results show executing times separately for HermiT reasoner and proposed adjustment algorithm and also the values of three predefined evaluation properties. The results show that proposed adjustment method can be used over existing data models represented in OWL ontology that describe some object of evaluation in order to apply evidential reasoning

Ontology transformation into taxonomic structure algorithm for evidential reasoning

Modeliranje podataka predstavlja proces stvaranja modela podataka kojim se simboliˇcki opisuju hijerarhijski odnosi izmed¯u stvari i dogad¯aja u nekom sustavu ili procesu. Ontologije kao napredniji naˇcin modeliranja podataka na raspolaganju imaju ve´ci broj jeziˇcnih konstrukata u odnosu na uobiˇcajene naˇcine modeliranja. Uz navedeno, ontologije na raspolaganju imaju i zaklju ˇcivaˇc koji na temelju znaˇcenja daje logiˇcke zakljuˇcke koji utjeˇcu na hijerarhijsku strukturu i raspored objekata (instanci) klasa. S druge strane, evidencijsko zakljuˇcivanje predstavlja najnoviju metodu višekriterijskog odluˇcivanja temeljenu na Dempster-Shafer teoriji koja se odnosi na donošenje odred¯enih odluka s prisutnim, a cˇesto i konfliktnim, kriterijima. Kako bi se evidencijsko zakljucˇivanje moglo primijeniti nad OWL ontologijama potrebno je napraviti odred¯ene prilagodbe s obzirom da se OWL ontologija strukturno razlikuje od zahtjeva za primjenu evidencijskog zakljuˇcivanja. U ovoj disertaciji predstavljen je model prilagodbe najˇceš´ce korištene OWL ontologije u taksonomsku strukturu temeljen na HermiT zakljuˇcivaˇcu. Predloženi model dijeli se na tri glavna dijela. Prvi dio odnosi se na primjenu HermiT zakljuˇcivaˇca nad ulaznom ontologijom, a kao rezultat dobija se hijerarhijska struktura koja predstavlja ontologiju pri ˇcemu se u obzir uzimaju svi korišteni aksiomi OWL ontologije. Drugi dio odnosi se na primjenu algoritma za prilagodbu ontologije u taksonomsku strukturu gdje dobivena taksonomija zadovoljava pravila op´ceg stabla, odnosno, nad takvom taksonomijom mogu´ce je primjeniti evidencijsko zakljuˇcivanje. Tre´ci dio odnosi se na pripremu za proces primjene evidencijskog zakljuˇcivanja. Najbitniji dio modela prilagodbe je predloženi algoritam prilagodbe koji na temelju skupa pravila i podpravila rješava specifiˇcne situacije koje se mogu na´ci u ontološkoj strukturi u svrhu dobivanja taksonomske strukture. Kako bi se provjerila ispravnost prilagod¯ene strukture, predložena je metoda evaluacije koja se temelji na provjeri definirana tri svojstva, a to su svojstvo klasa, svojstvo veza i svojstvo povezanosti. Primjena predloženog modela prilagodbe, algoritma i metode evaluacije prikazana je na skupu ulaznih ontologija iz dostupnih repozitorija. Dobiveni rezultati prikazuju vremena izvršavanja zasebno za HermiT zakljuˇcivaˇc i predloženi algoritam prilagodbe, te vrijednosti definirana tri svojstva evaluacije prilagodbe. Iz rezultata je vidljivo kako bi se takav naˇcin prilagodbe mogao iskoristiti na ve´c postoje´cim modelima prikazanih u OWL ontologiji, a koji opisuju neki objekt ocjenjivanja u svrhu primjene evidencijskog zakljuˇcivanjaData modelling represents the process of creating data model which symbolically describes hierarchical relationships between things and events within a system or process. Ontologies as more advanced data modelling approach have greater number of available language constructs compared to usual data modelling methods. Also, ontologies use reasoner which generates logical conclusions based on meaning that affect hierarchical structure and instance membership. On the other hand, evidential reasoning represents the latest multiple criteria decision method based on Dempster-Shafer theory related to the decision making process with present, but often conflicted, criteria. In order to apply evidential reasoning over OWL ontologies it is necessary to apply some adjustments considering that OWL ontology is structurally different from demands that impose evidential reasoning. This dissertation presents adjustment model for commonly used OWL ontology into taxonomic structure based on HermiT reasoner. The proposed model is divided into three main parts. The first part is related to application of HermiT reasoner over input ontology resulting in hierarchical structure that represents original ontology regarding all used OWL ontology axioms. The second part is related to the application of ontology adjustment algorithm into taxonomic structure where resulting taxonomy satisfies general tree rules, i.e. evidential reasoning can be applied over such taxonomy. The third part is related to preparation for evidential reasoning application. The most significant part of the adjustment model is proposed adjustment algorithm that addresses specific situations existing in ontology structure based on set of rules and sub rules in order to get taxonomic structure. The evaluation method based on satisfying of three predefined properties (class property, relation property and connectivity property) in order to verify the adjusted structure correctness is proposed. The application of proposed adjustment model, adjustment algorithm and evaluation method is performed over the input set of ontologies from available online repositories. The results show executing times separately for HermiT reasoner and proposed adjustment algorithm and also the values of three predefined evaluation properties. The results show that proposed adjustment method can be used over existing data models represented in OWL ontology that describe some object of evaluation in order to apply evidential reasoning

Ontology transformation into taxonomic structure algorithm for evidential reasoning

Modeliranje podataka predstavlja proces stvaranja modela podataka kojim se simboliˇcki opisuju hijerarhijski odnosi izmed¯u stvari i dogad¯aja u nekom sustavu ili procesu. Ontologije kao napredniji naˇcin modeliranja podataka na raspolaganju imaju ve´ci broj jeziˇcnih konstrukata u odnosu na uobiˇcajene naˇcine modeliranja. Uz navedeno, ontologije na raspolaganju imaju i zaklju ˇcivaˇc koji na temelju znaˇcenja daje logiˇcke zakljuˇcke koji utjeˇcu na hijerarhijsku strukturu i raspored objekata (instanci) klasa. S druge strane, evidencijsko zakljuˇcivanje predstavlja najnoviju metodu višekriterijskog odluˇcivanja temeljenu na Dempster-Shafer teoriji koja se odnosi na donošenje odred¯enih odluka s prisutnim, a cˇesto i konfliktnim, kriterijima. Kako bi se evidencijsko zakljucˇivanje moglo primijeniti nad OWL ontologijama potrebno je napraviti odred¯ene prilagodbe s obzirom da se OWL ontologija strukturno razlikuje od zahtjeva za primjenu evidencijskog zakljuˇcivanja. U ovoj disertaciji predstavljen je model prilagodbe najˇceš´ce korištene OWL ontologije u taksonomsku strukturu temeljen na HermiT zakljuˇcivaˇcu. Predloženi model dijeli se na tri glavna dijela. Prvi dio odnosi se na primjenu HermiT zakljuˇcivaˇca nad ulaznom ontologijom, a kao rezultat dobija se hijerarhijska struktura koja predstavlja ontologiju pri ˇcemu se u obzir uzimaju svi korišteni aksiomi OWL ontologije. Drugi dio odnosi se na primjenu algoritma za prilagodbu ontologije u taksonomsku strukturu gdje dobivena taksonomija zadovoljava pravila op´ceg stabla, odnosno, nad takvom taksonomijom mogu´ce je primjeniti evidencijsko zakljuˇcivanje. Tre´ci dio odnosi se na pripremu za proces primjene evidencijskog zakljuˇcivanja. Najbitniji dio modela prilagodbe je predloženi algoritam prilagodbe koji na temelju skupa pravila i podpravila rješava specifiˇcne situacije koje se mogu na´ci u ontološkoj strukturi u svrhu dobivanja taksonomske strukture. Kako bi se provjerila ispravnost prilagod¯ene strukture, predložena je metoda evaluacije koja se temelji na provjeri definirana tri svojstva, a to su svojstvo klasa, svojstvo veza i svojstvo povezanosti. Primjena predloženog modela prilagodbe, algoritma i metode evaluacije prikazana je na skupu ulaznih ontologija iz dostupnih repozitorija. Dobiveni rezultati prikazuju vremena izvršavanja zasebno za HermiT zakljuˇcivaˇc i predloženi algoritam prilagodbe, te vrijednosti definirana tri svojstva evaluacije prilagodbe. Iz rezultata je vidljivo kako bi se takav naˇcin prilagodbe mogao iskoristiti na ve´c postoje´cim modelima prikazanih u OWL ontologiji, a koji opisuju neki objekt ocjenjivanja u svrhu primjene evidencijskog zakljuˇcivanjaData modelling represents the process of creating data model which symbolically describes hierarchical relationships between things and events within a system or process. Ontologies as more advanced data modelling approach have greater number of available language constructs compared to usual data modelling methods. Also, ontologies use reasoner which generates logical conclusions based on meaning that affect hierarchical structure and instance membership. On the other hand, evidential reasoning represents the latest multiple criteria decision method based on Dempster-Shafer theory related to the decision making process with present, but often conflicted, criteria. In order to apply evidential reasoning over OWL ontologies it is necessary to apply some adjustments considering that OWL ontology is structurally different from demands that impose evidential reasoning. This dissertation presents adjustment model for commonly used OWL ontology into taxonomic structure based on HermiT reasoner. The proposed model is divided into three main parts. The first part is related to application of HermiT reasoner over input ontology resulting in hierarchical structure that represents original ontology regarding all used OWL ontology axioms. The second part is related to the application of ontology adjustment algorithm into taxonomic structure where resulting taxonomy satisfies general tree rules, i.e. evidential reasoning can be applied over such taxonomy. The third part is related to preparation for evidential reasoning application. The most significant part of the adjustment model is proposed adjustment algorithm that addresses specific situations existing in ontology structure based on set of rules and sub rules in order to get taxonomic structure. The evaluation method based on satisfying of three predefined properties (class property, relation property and connectivity property) in order to verify the adjusted structure correctness is proposed. The application of proposed adjustment model, adjustment algorithm and evaluation method is performed over the input set of ontologies from available online repositories. The results show executing times separately for HermiT reasoner and proposed adjustment algorithm and also the values of three predefined evaluation properties. The results show that proposed adjustment method can be used over existing data models represented in OWL ontology that describe some object of evaluation in order to apply evidential reasoning