A Self-Service Supporting Business Intelligence and Big Data Analytics Architecture

Self-service Business Intelligence (SSBI) is an emerging topic for many companies. Casual users should be enabled to independently build their own analyses and reports. This accelerates and simplifies the decision-making processes. Although recent studies began to discuss parts of a self-service environment, none of these present a comprehensive architecture. Following a design science research approach, this study proposes a new self-service oriented BI architecture in order to address this gap. Starting from an in-depth literature review, an initial model was developed and improved by qualitative data analysis from interviews with 18 BI and IT specialists form companies across different industries. The proposed architecture model demonstrates the interaction between introduced self-service elements with each other and with traditional BI components. For example, we look at the integration of collaboration rooms and a self-learning knowledge database that aims to be a source for a report recommender

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Designing Cross-Company Business Intelligence Networks

Business Intelligence (BI) ist der allgemein akzeptierte Begriff für Methoden, Konzepte und Werkzeuge zur Sammlung, Aufbereitung, Speicherung, Verteilung und Analyse von Daten für Management- und Geschäftsentscheidungen. Obwohl unternehmensübergreifende Kooperation in den vergangenen Jahrzehnten stets an Einfluss gewonnen hat, existieren nur wenige Forschungsergebnisse im Bereich unternehmensübergreifender BI. Die vorliegende Arbeit stellt eine Arbeitsdefinition des Begriffs Cross-Company BI (CCBI) vor und grenzt diesen von gemeinschaftlicher Entscheidungsfindung ab. Auf Basis eines Referenzmodells, das existierende Arbeiten und Ansätze verwandter Forschungsbereiche berücksichtigt, werden umfangreiche Simulationen und Parametertests unternehmensübergreifender BI-Netzwerke durchgeführt. Es wird gezeigt, dass eine Peer-To-Peer-basierte Gestaltung der Netzwerke leistungsfähig und kompetitiv zu existierenden zentral-fokussierten Ansätzen ist. Zur Quantifizierung der Beobachtungen werden Messgrößen geprüft, die sich aus existierenden Konzepten zur Schemaüberführung multidimensionaler Daten sowie Überlegungen zur Daten- und Informationsqualität ableiten oder entwickeln lassen.Business Intelligence (BI) is a well-established term for methods, concepts and tools to retrieve, store, deliver and analyze data for management and business purposes. Although collaboration across company borders has substantially increased over the past decades, little research has been conducted specifically on Cross-Company BI (CCBI). In this thesis, a working definition and distinction from general collaborative decision making is proposed. Based on a reference model that takes existing research and related approaches of adjacent fields into account a peer-to-peer network design is created. With an extensive simulation and parameter testing it is shown that the design proves valuable and competitive to centralized approaches and that obtaining a critical mass of participants leads to improved usefulness of the network. To quantify the observations, appropriate quality measures rigorously derived from respected concepts on data and information quality and multidimensional data models are introduced and validated

Flexible Integration and Efficient Analysis of Multidimensional Datasets from the Web

If numeric data from the Web are brought together, natural scientists can compare climate measurements with estimations, financial analysts can evaluate companies based on balance sheets and daily stock market values, and citizens can explore the GDP per capita from several data sources. However, heterogeneities and size of data remain a problem. This work presents methods to query a uniform view - the Global Cube - of available datasets from the Web and builds on Linked Data query approaches

Adaptive P2P platform for data sharing

Ph.DDOCTOR OF PHILOSOPH

ICT tools for data management and analysis to support decisional process oriented to sustainable agri-food chains

Il settore agroalimentare sta affrontando delle sfide globali. La prima riguarda sfamare la popolazione mondiale che nel 2050, secondo le proiezioni delle Nazioni Unite, raggiungerà quota 9,3 miliardi di persone. La seconda sfida riguarda la richiesta da parte dei consumatori di prodotti ottenuti da filiere agroalimentari sempre più sostenibili, sicure e trasparenti. In particolare, l’Agricoltura sostenibile è una tecnica di gestione in grado di preservare la diversità biologica, la produttività, la capacità di rigenerazione, la vitalità e l’abilità alla funzione di un ecosistema agricolo, assicurandone, oggi e in futuro, le funzioni ecologiche, economiche e sociali a livello locale, nazionale ed globale, senza danneggiare altri ecosistemi. Quindi, per fronteggiare la sfida dell’agricoltura sostenibile, gli agricoltori devono aumentare la qualità e la quantità della produzione, riducendo l’impatto ambientale attraverso nuovi strumenti e nuove strategie di gestione. Questo lavoro analizza l’integrazione nel settore agroalimentare di alcune tecnologie e metodologie ICT per l’acquisizione, gestione e analisi dei dati, come la tecnologia RFID (Radio Frequency IDentification), i FMIS (Farm Management Information Systems), i DW (Data Warehouse) e l’approccio OLAP (On-Line Analytical Processing). Infine, l’adozione delle tecnologie ICT da parte di vere aziende è stata valutata attraverso un questionario. Al riguardo dell’adozione delle tecnologie RFID, questo lavoro analizza l’opportunità di trasferimento tecnologico relativo al monitoraggio e controllo dei prodotti agroalimentari tramite l’utilizzo di sensori innovativi, intelligenti e miniaturizzati. Le informazioni riguardanti lo stato del prodotto sono trasferite in tempo reale in wireless, come previsto dalla tecnologia RFID. In particolare, due soluzioni RFID sono state analizzate, evidenziando vantaggi e punti critici in confronto ai classici sistemi per assicurare la tracciabilità e la qualità dei prodotti agroalimentari. Quindi, questo lavoro analizza la possibilità di sviluppare una struttura che combina le tecnologie della Business Intelligence con i principi della Protezione Integrata (IPM) per aiutare gli agricoltori nel processo decisionale, andando a diminuire l’impatto ambientale ed aumentare la performance produttiva. L’IPM richiede di utilizzare simultaneamente diverse tecniche di protezione delle colture per il controllo dei parassiti e patogeni tramite un approccio ecologico ed economico. Il sistema di BI proposto è chiamato BI4IPM e combina l’approccio OLTP (On-Line Transaction Processing) con quello OLAP per verificare il rispetto dei disciplinari di produzione integrata. BI4IPM è stato testato con dati provenienti da vere aziende olivicole pugliesi. L’olivo è una delle principali colture a livello globale e la Puglia è la prima regione produttrice in Italia, con un gran numero di aziende che generano dati sull’IPM. Le strategie di protezione delle colture sono correlate alle condizioni climatiche, considerando la forte relazione tra clima, colture e parassiti. Quindi, in questo lavoro è presentato un nuovo e avanzato modello OLAP che integra il GSI (Growing Season Index), un modello fenologico, per comparare indirettamente le aziende agricole dal punto di vista climatico. Il sistema proposto permette di analizzare dati IPM di diverse aziende agricole che presentano le stesse condizioni fenologiche in un anno al fine di individuare best practices e di evidenziare e spiegare pratiche differenti adottate da aziende che lavorano in differenti condizioni climatiche. Infine, è stata effettuata un’indagine al fine di capire come le aziende agricole della Basilicata si raggruppano in funzione del livello di innovazione adottato. È stato utilizzato un questionario per domandare alle aziende se adottano strumenti ICT, ed eventualmente in quale processo produttivo o di management vengano usati. È stata quindi effettuata un’analisi cluster sui dati raccolti. I risultati mostrano che, usando il metodo di clustering k-means, appaiono due gruppi: gli innovatori e gli altri. Mentre, applicando la rappresentazione boxlot, si ottengono 3 gruppi: innovatori, utilizzatori precoci e ritardatari.The Agri-Food sector is facing global challenges. The first issue concerns feeding a world population that in 2050, according to United Nations projections, will reach 9.3 billion people. The second challenge is the request by consumers for high quality products obtained by more sustainable, safely and clear agri-food chains. In particular, the Sustainable agriculture is a management strategy able to preserve the biological diversity, productivity, regeneration capacity, vitality and ability to function of an agricultural ecosystem, ensuring, today and in the future, significant ecological, economic and social functions at the local, national and global scales, without harming other ecosystems. Therefore, to face the challenge of the sustainable agriculture, farmers need to increase quality and quantity of the production, reducing the environmental impact through new management strategies and tools. This work explores the integration of several ICT technologies and methodologies in the agri-food sector for the data acquisition, management and analysis, such as RFID technology, Farm Management Information Systems (FMIS), Data Warehouse (DW) and On-Line Analytical Processing (OLAP). Finally, the adoption of the ICT technologies by real farms is evaluated through a survey. Regarding the adoption of the RFID technology, this work explores an opportunity for technology transfer related to the monitoring and control of agri-food products, based on the use of miniaturized, smart and innovative sensors. The information concerning to the state of the product is transferred in real time in a wireless way, according to the RFID technology. In particular, two technical solutions involving RFID are provided, highlighting the advantages and critical points referred to the normal system used to ensure the traceability and the quality of the agri-food products. Therefore, this work explores the possibility of developing a framework that combines business intelligence (BI) technologies with Integrated Pest Management (IPM) principles to support farmers in the decisional process, thereby decreasing environmental cost and improving production performance. The IPM requires the simultaneous use of different crop protection techniques to control pests through an ecological and economic approach. The proposed BI system is called BI4IPM, and it combines on-line transaction processing (OLTP) with OLAP to verify adherence to the IPM technical specifications. BI4IPM is tested with data from real Apulian olive crop farms. Olive tree is one of the most important crop at global scale and Apulia is the first olive-producing region in Italy, with a huge amount of farms that generate IPM data. The crop protection strategies are correlated to the climate conditions considering the very important relation among climate, crops and pests. Therefore, in this work is presented a new advanced OLAP model integrating the Growing Season Index (GSI), a phenology model, to compare indirectly the farms by a climatic point of view. The proposed system allows analysing IPM data of different farms having the same phenological conditions over a year to understand some best practices and to highlight and explain different practices adopted by farms working in different climatic conditions. Finally, a survey aimed at investigating how Lucania' farms cluster according to the level of innovation adopted was performed. It was used a questionnaire for asking if farms adopt ICTs tools and, in case, what type they involved in managing and/or production processes. It has been done a cluster analysis on collected data. Results show that, using k-means clustering method, appear two clusters: innovators, remaining groups. While, using boxplot representation, clustered three groups: innovators, early adopters and laggards

Flexible Integration and Efficient Analysis of Multidimensional Datasets from the Web

If numeric data from the Web are brought together, natural scientists can compare climate measurements with estimations, financial analysts can evaluate companies based on balance sheets and daily stock market values, and citizens can explore the GDP per capita from several data sources. However, heterogeneities and size of data remain a problem. This work presents methods to query a uniform view - the Global Cube - of available datasets from the Web and builds on Linked Data query approaches

WEB recommendations for E-commerce websites

In this part of the thesis we have investigated how the navigation utilizing web recommendations can be implemented on the e-commerce websites based on integrated data sources. The integrated e-commerce websites are an interesting use case for web recommendations. One of the reasons for this interest is that many modern, large and economically successful e-commerce websites follow the integrated approach. Another reason is that especially in the integrated environment, due to the lack of the pre-defined semantic connections between the data, the web recommendations step forward as means of enabling user navigation. In this chapter we have presented the architecture for the websites based on integrated data sources named EC-Fuice. We have also presented the prototypical implementation of our architecture which serves as a proof-of-concept and investigated the challenges of creating navigation on an integrated website. The following issues were addressed in this part of the thesis: Combination of several state-of-the-art tools and techniques in the fields of databases, data integration, ontology matching and web engineering into one generic architecture for creating integrated websites. Comparative experiments with several techniques for instance matching (also known as record linkage or duplicate detection). Investigation on using the ontology matching to facilitate the instance matching. Comparative experiments with several techniques for ontology matching. Investigations on the instance-based ontology matching and the possibilities for combining instance-based ontology matching with other techniques for ontology matching. Investigation of the possibilities to improve user navigation in the integrated data environment with different types of web recommendations. Review of the related work in the fields of data integration and ontology matching and discussion of the contact points between the research described here and other related projects. The main contributions of the research described in this part of the thesis are the EC-Fuice architecture, the novel method for matching e-commerce ontologies based on combination of instance information and metadata information, the experimental results of ontology and instance matching performed by different matching algorithms and the classification of the types of recommendations which can be used on an integrated e-commerce website

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