Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data

Benefits and barriers of self-service business intelligence implementation in micro-enterprises: a case of ABC Travel & Consulting

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementSmall medium enterprises (hereinafter: SME) represent 99.8 % of firms in the non-financial business sector of the European Union. SME’s cover three different types of companies, namely micro-, small- and medium-sized enterprises. Micro-enterprises are the most common type of SME in the European Economic Area, accounting for 93.2 % of the non-financial business sector (Muller, Julius, Herr & Peycheva, 2017). Due to their importance, the focus of this work will be on micro-enterprises. They are defined by two factors: firstly, the number of employees has to be lower than ten, and secondly, the turnover or the total assets must be lower than or equal to two million Euros (European Commission, 2014). Business intelligence systems (hereinafter: BIS) have become significantly important in the business world and academic community over the last two decades (Chen, Chiang & Storey, 2012). The global revenue reached a volume of $18.3 billion in 2017 and is forecasted to reach$22.8 billion by the end of 2020. Modern BIS continue to expand more rapidly than the overall market (Moore, 2017). The benefits of the integration of BIS can be seen longterm, users are typically decision makers at higher organizational levels (Puklavec, Oliveira & Popovic, 2014). With the usage of BIS, knowledge workers such as executives, managers, and analysts can make better and faster decisions (Chaudhuri, Dayal & Narasayya, 2011). The proper usage of BIS can be seen as a prerequisite for business success, but these tools are often complex and require a high level of expertise to work with (Davenport, 2017). It is a challenge for micro companies to implement BIS because they have often only a limited set of financial and human resources (Puklavec, Oliveira & Popovic, 2014)

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

Cryptographic solutions of organization’s memory protection from the point of management’s knowledge

Moderne kompanije se svakim danom suočavaju sa problemom opterećenosti velikom količinom informacija i podataka, a što otežava njihovo poslovanje i donošenje efikasnih poslovnih odluka. Pronalaženje nove suštine primene načina menadžmenta znanja u smislu efikasnog korišćenja memorije organizacije (znanja), predstavlja sve veću potrebu kompanija da unaprede svoje poslovanje. Isto tako, zaštita načina pristupanja memoriji organizacije (znanju kompanije), njegovoj razmeni i upravljanju njime, kompanije sve više posvećuju pažnju i stavljaju akcenat u svom poslovanju. Primena koncepta poslovne inteligencije u upravljanju memorije organizacije postaje neizostavan element strategije uspešnih kompanija. Integrisano automatizovano upravljanje memorijom organizacije (znanjem jedne kompanije), iako veoma složeno, predstavlja rešenje za interakciju menadžmenta znanja i informacione tehnologije. Time se stvara mogućnost potpunog objašnjavanja procesa donošenja odluka u jednoj kompaniji, ali i procesa toka dokumenata, informacija i podataka. Integrisanim automatizovanim upravljanjem memorijom organizacije, kompanija ostvaruje mogućnost dobijanja detaljnih podataka na osnovu kojih je olakšano konkretno poslovno odlučivanje. Takođe, ovde se javlja i zahtev za zaštitu jednog takvog integrisanog automatizo vanog procesa. U skladu sa određenim i usvojenim međunarodnim standardima (ISO 27001), menadžment u ovakvom sistemu kakav je memorija organizacije treba da osigura efikasnu implementaciju, praćenje i unapređenje sistema za rukovanje bezbednošću memorije organizacije. Zaštita i bezbednost memorije organizacije kroz kriptografska rešenje treba da zadovolji balans između zahteva korisnika, funkcionalnosti unutar memorije organizacije i potrebe zaštite osetljivih podataka i čuvanje njihovog integriteta. Ovakav integrisani automatizovani proces upravljanja memorijom organizacije predstavlja jedno rešenje koje bi svoju upotrebu moglo da nađe kako u oblasti učenja inteligentnih sistema, tako i u postojećim sistemima savremenog poslovnog odlučivanja. Jedan od predloženog načina rešenja zaštite integrisanog sistema za proces upravljanja memorijom organizacije u ovom radu biće i mogućnost snimanja u šifrovanom obliku, čime podaci postaju dostupni samo kroz informacioni sistem kompanije. U ovom radu biće predstavljeno sopstveno kriptografsko rešenje zaštite memorije organizacije sa stanovišta menadžmenta znanja. Pristup dokumentima i podacima će imati samo ovlašćeni korisnici sistema na osnovu definisanih dozvola pristupa. Autentičnost dokumenata i njihova nepromenljivost bi se obezbedila pomoću digitalnih potpisa, što predložena kriptografska rešenja obezbeđuju u skladu sa aktuelnim zakonskim propisima za elektronski dokument. Isto tako, biće razmotreni principi i modeli koji obezbeđuju i zaštitu podataka i privilegovan pristup podacima, a sve u cilju donošenja odluka zasnovanih na memoriji organizacije. Najbolji primer za ovakvu analizu su bezbednosno-informativne agencije, a brojni su primeri, kako dobrih organizacija, tako i propusta u njihovom radu

Performances of Multi-Level and Multi-Component Compressed BitmapIndices

This paper presents a systematic study of two large subsetsof bitmap indexing methods that use multi-component and multi-levelencodings. Earlier studies on bitmap indexes are either empirical or foruncompressed versions only. Since most of bitmap indexes in use arecompressed, we set out to study the performance characteristics of thesecompressed indexes. To make the analyses manageable, we choose to use aparticularly simple, but efficient, compression method called theWord-Aligned Hybrid (WAH) code. Using this compression method, a numberof bitmap indexes are shown to be optimal because their worst-case timecomplexities for answering a query is a linear function of the number ofhits. Since compressed bitmap indexes behave drastically different fromuncompressed ones, our analyses also lead to a number of new methods thatare much more efficient than commonly used ones. As a validation for theanalyses, we implement a number of the best methods and measure theirperformance against well-known indexes. The fastest new methods arepredicted and observed to be 5 to 10 times faster than well-knownindexing methods