Meta-Estudio de BPM en e-GOV

La aplicación del paradigma BPM (Business Process Management) en las organizaciones presenta distintos grados de madurez, siendo el sector privado un espacio donde su aplicación está más consolidada. Los beneficios de utilización de BPM en organizaciones gubernamentales lleva a dicho ámbito muchos de los beneficios del paradigma, siendo los más importantes el modelado de los principales circuitos de la administración y la automatización de los mismos para poder monitorear y evaluar, cerrando un ciclo de mejora continua. El propósito de este trabajo es vincular los conceptos de e-gov – entendido en líneas generales como la aplicación de TIC a los ámbitos gubernamentales- y BPM, a través de un meta-estudio que parte de un análisis bibliográfico y realiza una conceptualización que surge de un conjunto de preguntas de investigación.Sociedad Argentina de Informática e Investigación Operativ

Perspectives in Business Informatics Research 13th International Conference, BIR 2014, Lund, Sweden, September 22-24, 2014. Proceedings

This book constitutes the proceedings of the 13th International Conference on Perspectives in Business Informatics Research, BIR 2014, held in Lund, Sweden, in September 2014. Overall, 71 submissions were rigorously reviewed by 55 members of the Program Committee representing 22 countries. As a result, 27 full papers have been selected for publication in this volume. The papers cover many aspects of business information research and have been organized in topical sections on: business, people, and systems; business and information systems development; and contextualized evaluation of business informatics

A knowledge discovery approach to urban analysis

Enhancing our knowledge of the complexities of cities in order to empower ourselves to make more informed decisions has always been a challenge for urban research. Recent developments in large-scale computing, together with the new techniques and automated tools for data collection and analysis are opening up promising opportunities for addressing this problem. The main motivation that served as the driving force behind this research is how these developments may contribute to urban data analysis. On this basis, the thesis focuses on urban data analysis in order to search for findings that can enhance our knowledge of urban environments, using the generic process of knowledge discovery using data mining. A knowledge discovery process based on data mining is a fully automated or semi-automated process which involves the application of computational tools and techniques to explore the “previously unknown, and potentially useful information” (Witten & Frank, 2005) hidden in large and often complex and multi-dimensional databases. This information can be obtained in the form of correlations amongst variables, data groupings (classes and clusters) or more complex hypotheses (probabilistic rules of co-occurrence, performance vectors of prediction models etc.). This research targets researchers and practitioners working in the field of urban studies who are interested in quantitative/ computational approaches to urban data analysis and specifically aims to engage the interest of architects, urban designers and planners who do not have a background in statistics or in using data mining methods in their work. Accordingly, the overall aim of the thesis is the development of a knowledge discovery approach to urban analysis; a domain-specific adaptation of the generic process of knowledge discovery using data mining enabling the analyst to discover ‘relational urban knowledge’. ‘Relational urban knowledge’ is a term employed in this thesis to refer to the potentially ‘useful’ and/or ‘valuable’ information patterns and relationships that can be discovered in urban databases by applying data mining algorithms. A knowledge discovery approach to urban analysis through data mining can help us to understand site-specific characteristics of urban environments in a more profound and useful way. On a more specific level, the thesis aims towards ‘knowledge discovery’ in traditional thematic maps published in 2008 by the Istanbul Metropolitan Municipality as a basis of the Master Plan for the Beyoğlu Preservation Area. These thematic maps, which represent urban components, namely buildings, streets, neighbourhoods and their various attributes such as floor space use of the buildings, land price, population density or historical importance, do not really extend our knowledge of Beyoğlu Preservation Area beyond documenting its current state and do not contribute to the interventions presented in the master plan. However it is likely that ‘useful’ and ‘valuable’ information patterns discoverable using data mining algorithms are hidden in them. In accordance with the stated aims, three research questions of the thesis concerns (1) the development of a general process model to adapt the generic process of knowledge discovery using data mining for urban data analysis, (2) the investigation of information patterns and relationships that can be extracted from the traditional thematic maps of the Beyoğlu Preservation Area by further developing and implementing this model and (3) the investigation of how could this ‘relational urban knowledge’ support architects, urban designers or urban planners whilst developing intervention proposals for urban regeneration. A Knowledge Discovery Process Model (KDPM) for urban analysis was developed, as an answer to the the first research question. The KDPM for urban analysis is a domain-specific adaptation of the widely accepted process of knowledge discovery in databases defined by Fayyad, Piatetsky-Shapiro, and Smyth (1996b). The model describes a semi-automated process of database formulation, analysis and evaluation for extracting information patterns and relationships from raw data by combining both GIS and data mining functionalities in a complementary way. The KDPM for urban analysis suggests that GIS functionalities can be used to formulate a database, and GIS and data mining can complement each other in analyzing the database and evaluating the outcomes. The model illustrates that the output of a GIS platform can become the input for a data mining platform and vice versa, resulting in an interlinked analytical process which allows for a more sophisticated analysis of urban data. To investigate the second and third research questions, firstly the KDPM for urban analysis was further developed to construct a GIS database of the Beyoğlu Preservation Area from the thematic maps. Then, three implementations were performed using this GIS database; the Beyoğlu Preservation Area Building Features Database consisting of multiple features attributed to the buildings. In Implementation (1), the KDPM for urban analysis was used to investigate a variety of patterns and relationships that can be extracted from the database using three different data mining methods. In Implementations (2) and (3), the KDPM for urban analysis was implemented to test how the knowledge discovery approach through data mining proposed in this thesis can assist in developing draft plans for the regeneration of a run-down neighbourhood in the Beyoğlu Preservation Area (Tarlabaşı). In Implementation (2), the KDPM for urban analysis is implemented in combination with an evolutionary process to apply a regeneration approach developed by the author; a computational process which generates draft plans for ground floor use, user-profile and tenure-type allocation was developed. In Implementation (3), students applied the KDPM for urban analysis during the course of an international workshop. The model enabled them to explore site-specific particularities of Tarlabaşı that would support their urban intervention proposals. Among the outputs of the thesis three of them are considered as utilizable outputs that distinguish this thesis from previous studies: The KDPM for urban analysis. Although there have been other studies which make use of data mining methods and techniques combined with GIS technology, to the best of our knowledge no previous research has implemented a process model to depict this process and used the model to extract ‘knowledge’ from traditional thematic maps. Researchers and practitioners can re-use this process model to analyze other urban environments. The KDPM for urban analysis is, therefore, one of the main utilizable outputs of the thesis and an important scientific contribution of this study. The Beyoğlu Preservation Area Building Features Database. A large and quite comprehensive GIS database which consists of 45 spatial and non-spatial features attributed to the 11,984 buildings located in the Beyoğlu Preservation Area was constructed. This database is one of the original features of this study. To the best of our knowledge, there are no other examples of applications of data mining using such a comprehensive GIS database, constructed from a range of actual micro-scale data representing such a variety of features attributed to the buildings. This database can be re-used by analysts interested in studying the Beyoğlu Preservation Area. The Beyoğlu Preservation Area Building Features Database is therefore one of the main utilizable outputs of the thesis and represents a scientific contribution to the research material on the Beyoğlu Preservation Area. A computational process which generates draft plans for ground floor use, user-profile and tenure-type allocation, using GIS and data mining functionalities with evolutionary computation. This output of the thesis was generated by Implementation (2), which aimed to investigate Research Question (3). The overall process involved the successive application of Naïve Bayes Classification, Association Rule Analysis and an Evolutionary Algorithm to a subset of the Beyoğlu Preservation Area Building Features Database representing the Tarlabaşı neighbourhood. Briefly, the findings of the data mining analysis were used to formulate a set of rules for assigning ground floor use information to the buildings. These rules were then used for fitness measurements of an Evolutionary Algorithm, together with other fitness measurements for assigning user-profile and tenure-type information (defined by the author according to the regeneration approach developed by the author). As a result, the algorithm transformed the existing allocation of the ground floor use in the buildings located in Tarlabaşı in accordance with the given rules and assigned user-profile and tenure type information for each building. This computational process demonstrated one way to use the data mining analysis findings in developing intervention proposals for urban regeneration. A similar computational process can be implemented in other urban contexts by researchers and practitioners. To the best of our knowledge, no prior research has used data mining analysis findings for fitness measurements of an Evolutionary Algorithm in order to produce draft plans for ground floor use, user-profile and tenure-type allocation. This is, therefore, the most original scientific contribution and utilizable output of the thesis. As a result of the research, on the basis of the data that is available in the thematic maps of the Beyoğlu Preservation Area, the potential of a knowledge discovery approach to urban analysis in revealing the relationships between various components of urban environments and their various attributes is demonstrated. It is also demonstrated that these relationships can reveal site-specific characteristics of urban environments and if found ‘valuable’ by the the targeted researchers and practitioners, these can lead to the development of more informed intervention proposals. Thereby the knowledge discovery approach to urban analysis developed in this thesis may help to improve the quality of urban intervention proposals and consequently the quality of built environments. On the other hand, the implementations carried out in the thesis also exposed the major limitation of the knowledge discovery approach to urban analysis through data mining, which is the fact that the findings discoverable by this approach are limited by the relevant data that is collectable and accessible

FIN-DM: finantsteenuste andmekaeve protsessi mudel

Andmekaeve hõlmab reeglite kogumit, protsesse ja algoritme, mis võimaldavad ettevõtetel iga päev kogutud andmetest rakendatavaid teadmisi ammutades suurendada tulusid, vähendada kulusid, optimeerida tooteid ja kliendisuhteid ning saavutada teisi eesmärke. Andmekaeves ja -analüütikas on vaja hästi määratletud metoodikat ja protsesse. Saadaval on mitu andmekaeve ja -analüütika standardset protsessimudelit. Kõige märkimisväärsem ja laialdaselt kasutusele võetud standardmudel on CRISP-DM. Tegu on tegevusalast sõltumatu protsessimudeliga, mida kohandatakse sageli sektorite erinõuetega. CRISP-DMi tegevusalast lähtuvaid kohandusi on pakutud mitmes valdkonnas, kaasa arvatud meditsiini-, haridus-, tööstus-, tarkvaraarendus- ja logistikavaldkonnas. Seni pole aga mudelit kohandatud finantsteenuste sektoris, millel on omad valdkonnapõhised erinõuded. Doktoritöös käsitletakse seda lünka finantsteenuste sektoripõhise andmekaeveprotsessi (FIN-DM) kavandamise, arendamise ja hindamise kaudu. Samuti uuritakse, kuidas kasutatakse andmekaeve standardprotsesse eri tegevussektorites ja finantsteenustes. Uurimise käigus tuvastati mitu tavapärase raamistiku kohandamise stsenaariumit. Lisaks ilmnes, et need meetodid ei keskendu piisavalt sellele, kuidas muuta andmekaevemudelid tarkvaratoodeteks, mida saab integreerida organisatsioonide IT-arhitektuuri ja äriprotsessi. Peamised finantsteenuste valdkonnas tuvastatud kohandamisstsenaariumid olid seotud andmekaeve tehnoloogiakesksete (skaleeritavus), ärikesksete (tegutsemisvõime) ja inimkesksete (diskrimineeriva mõju leevendus) aspektidega. Seejärel korraldati tegelikus finantsteenuste organisatsioonis juhtumiuuring, mis paljastas 18 tajutavat puudujääki CRISP- DMi protsessis. Uuringu andmete ja tulemuste abil esitatakse doktoritöös finantsvaldkonnale kohandatud CRISP-DM nimega FIN-DM ehk finantssektori andmekaeve protsess (Financial Industry Process for Data Mining). FIN-DM laiendab CRISP-DMi nii, et see toetab privaatsust säilitavat andmekaevet, ohjab tehisintellekti eetilisi ohte, täidab riskijuhtimisnõudeid ja hõlmab kvaliteedi tagamist kui osa andmekaeve elutsüklisData mining is a set of rules, processes, and algorithms that allow companies to increase revenues, reduce costs, optimize products and customer relationships, and achieve other business goals, by extracting actionable insights from the data they collect on a day-to-day basis. Data mining and analytics projects require well-defined methodology and processes. Several standard process models for conducting data mining and analytics projects are available. Among them, the most notable and widely adopted standard model is CRISP-DM. It is industry-agnostic and often is adapted to meet sector-specific requirements. Industry- specific adaptations of CRISP-DM have been proposed across several domains, including healthcare, education, industrial and software engineering, logistics, etc. However, until now, there is no existing adaptation of CRISP-DM for the financial services industry, which has its own set of domain-specific requirements. This PhD Thesis addresses this gap by designing, developing, and evaluating a sector-specific data mining process for financial services (FIN-DM). The PhD thesis investigates how standard data mining processes are used across various industry sectors and in financial services. The examination identified number of adaptations scenarios of traditional frameworks. It also suggested that these approaches do not pay sufficient attention to turning data mining models into software products integrated into the organizations' IT architectures and business processes. In the financial services domain, the main discovered adaptation scenarios concerned technology-centric aspects (scalability), business-centric aspects (actionability), and human-centric aspects (mitigating discriminatory effects) of data mining. Next, an examination by means of a case study in the actual financial services organization revealed 18 perceived gaps in the CRISP-DM process. Using the data and results from these studies, the PhD thesis outlines an adaptation of CRISP-DM for the financial sector, named the Financial Industry Process for Data Mining (FIN-DM). FIN-DM extends CRISP-DM to support privacy-compliant data mining, to tackle AI ethics risks, to fulfill risk management requirements, and to embed quality assurance as part of the data mining life-cyclehttps://www.ester.ee/record=b547227

International Open and Distance Learning Conference proceedings book = Uluslararası Açık ve Uzaktan Öğrenme Konferansı bildiri kitabı

The 4th International Open & Distance Learning Conference- IODL 2019, which was held at Anadolu University, Eskişehir, Türkiye on 14-16 November, 2019

A capability-based context modelling method to enhance digital service flexibility

The thesis argues that the enterprises need to understand their application context to be able to offer flexible digital services. Furthermore, after analysing the state of research in Services Science, it concludes that different roles with varying backgrounds participate to design and implementation of digital services, which adds the need for alignment between those as a further challenge for flexibility. To fulfil this, the thesis designs a context modelling method and evaluates it by means of Design Science Research (DSR).Digitalisierung in der Dienstleistungökonomie erfordert, die Auswirkungen von veränderten Anwendungskontexten an die zu erbringenden Services genau zu verstehen. Es wird nach der Analyse des Standes der Technik in Services Science festgestellt, dass unterschiedliche Rollen in der Gestaltung und Umsetzung von Digital Services beteiligt sind, was die Notwendigkeit der Abstimmung zwischen diesen Rollen als eine wichtige Herausforderung an die Flexibilität stellt. Um ein solches Alignment zu erreichen, entwickelt dieser Beitrag eine Kontextmodellierungsmethode und evaluiert diese mittels DSR