A relational algebra approach to ETL modeling

The MAP-i Doctoral Programme in Informatics, of the Universities of Minho, Aveiro and PortoInformation Technology has been one of drivers of the revolution that currently is happening in today’s management decisions in most organizations. The amount of data gathered and processed through the use of computing devices has been growing every day, providing a valuable source of information for decision makers that are managing every type of organization, public or private. Gathering the right amount of data in a centralized and unified repository like a data warehouse is similar to build the foundations for a system that will act has a base to support decision making processes requiring factual information. Nevertheless, the complexity of building such a repository is very challenging, as well as developing all the components of a data warehousing system. One of the most critical components of a data warehousing system is the Extract-Transform-Load component, ETL for short, which is responsible for gathering data from information sources, clean, transform and conform it in order to store it in a data warehouse. Several designing methodologies for the ETL components have been presented in the last few years with very little impact in ETL commercial tools. Basically, this was due to an existing gap between the conceptual design of an ETL system and its correspondent physical implementation. The methodologies proposed ranged from new approaches, with novel notation and diagrams, to the adoption and expansion of current standard modeling notations, like UML or BPMN. However, all these proposals do not contain enough detail to be translated automatically into a specific execution platform. The use of a standard well-known notation like Relational Algebra might bridge the gap between the conceptual design and the physical design of an ETL component, mainly due to its formal approach that is based on a limited set of operators and also due to its functional characteristics like being a procedural language operating over data stored in relational format. The abstraction that Relational Algebra provides over the technological infrastructure might also be an advantage for uncommon execution platforms, like computing grids that provide an exceptional amount of processing power that is very critical for ETL systems. Additionally, partitioning data and task distribution over computing nodes works quite well with a Relational Algebra approach. An extensive research over the use of Relational Algebra in the ETL context was conducted to validate its usage. To complement this, a set of Relational Algebra patterns were also developed to support the most common ETL tasks, like changing data capture, data quality enforcement, data conciliation and integration, slowly changing dimensions and surrogate key pipelining. All these patterns provide a formal approach to the referred ETL tasks by specifying all the operations needed to accomplish them in a series of Relational Algebra operations. To evaluate the feasibility of the work done in this thesis, we used a real ETL application scenario for the extraction of data in two different social networks operational systems, storing hashtag usage information in a specific data mart. The ability to analyze trends in social network usage is a hot topic in today’s media and information coverage. A complete design of the ETL component using the patterns developed previously is also provided, as well as a critical evaluation of its usage.As Tecnologias da Informação têm sido um dos principais catalisadores na revolução que se assiste nas tomadas de decisão na maioria das organizações. A quantidade de dados que são angariados e processados através do uso de dispositivos computacionais tem crescido diariamente, tornando-se uma fonte de informação valiosa para os decisores que gerem todo o tipo de organizações, públicas ou privadas. Concentrar o conjunto ideal de dados num repositório centralizado e unificado, como um data warehouse, é essencial para a construção de um sistema que servirá de suporte aos processos de tomada de decisão que necessitam de factos. No entanto, a complexidade associada à construção deste repositório e de todos os componentes que caracterizam um sistema de data warehousing é extremamente desafiante. Um dos componentes mais críticos de um sistema de data warehousing é a componente de Extração-Transformação- Alimentação (ETL) que lida com a extração de dados das fontes, que limpa, transforma e concilia os dados com vista à sua integração no data warehouse. Nos últimos anos têm sido apresentadas várias metodologias de desenho da componente de ETL, no entanto estas não têm sido adotadas pelas ferramentas comerciais de ETL principalmente devido ao diferencial existente entre o desenho concetual e as plataformas físicas de execução. As metodologias de desenho propostas variam desde propostas que assentam em novas notações e diagramas até às propostas que usam notações standard como a notação UML e BPMN que depois são complementadas com conceitos de ETL. Contudo, estas propostas de modelação concetual não contêm informações detalhadas que permitam uma tradução automática para plataformas de execução. A utilização de uma linguagem standard e reconhecida como a linguagem de Álgebra Relacional pode servir como complemento e colmatar o diferencial existente entre o desenho concetual e o desenho físico da componente de ETL, principalmente devido ao facto de esta linguagem assentar numa abordagem procedimental com um conjunto limitado de operadores que atuam sobre dados armazenados num formato relacional. A abstração providenciada pela Álgebra Relacional relativamente às plataformas de execução pode eventualmente ser uma vantagem tendo em vista a utilização de plataformas menos comuns, como por exemplo grids computacionais. Este tipo de arquiteturas disponibiliza por norma um grande poder computacional o que é essencial para um sistema de ETL. O particionamento e distribuição dos dados e tarefas pelos nodos computacionais conjugam relativamente bem com a abordagem da Álgebra Relacional. No decorrer deste trabalho foi efetuado um estudo extensivo às propriedades da AR num contexto de ETL com vista à avaliação da sua usabilidade. Como complemento, foram desenhados um conjunto de padrões de AR que suportam as atividades mais comuns de ETL como por exemplo changing data capture, data quality enforcement, data conciliation and integration, slowly changing dimensions e surrogate key pipelining. Estes padrões formalizam este conjunto de atividades ETL, especificando numa série de operações de Álgebra Relacional quais os passos necessários à sua execução. Com vista à avaliação da sustentabilidade da proposta presente neste trabalho, foi utilizado um cenário real de ETL em que os dados fontes pertencem a duas redes sociais e os dados armazenados no data mart identificam a utilização de hashtags por parte dos seus utilizadores. De salientar que a deteção de tendências e de assuntos que estão na ordem do dia nas redes sociais é de vital importância para as empresas noticiosas e para as próprias redes sociais. Por fim, é apresentado o desenho completo do sistema de ETL para o cenário escolhido, utilizando os padrões desenvolvidos neste trabalho, avaliando e criticando a sua utilização

Automating the multidimensional design of data warehouses

Les experiències prèvies en l'àmbit dels magatzems de dades (o data warehouse), mostren que l'esquema multidimensional del data warehouse ha de ser fruit d'un enfocament híbrid; això és, una proposta que consideri tant els requeriments d'usuari com les fonts de dades durant el procés de disseny.Com a qualsevol altre sistema, els requeriments són necessaris per garantir que el sistema desenvolupat satisfà les necessitats de l'usuari. A més, essent aquest un procés de reenginyeria, les fonts de dades s'han de tenir en compte per: (i) garantir que el magatzem de dades resultant pot ésser poblat amb dades de l'organització, i, a més, (ii) descobrir capacitats d'anàlisis no evidents o no conegudes per l'usuari.Actualment, a la literatura s'han presentat diversos mètodes per donar suport al procés de modelatge del magatzem de dades. No obstant això, les propostes basades en un anàlisi dels requeriments assumeixen que aquestos són exhaustius, i no consideren que pot haver-hi informació rellevant amagada a les fonts de dades. Contràriament, les propostes basades en un anàlisi exhaustiu de les fonts de dades maximitzen aquest enfocament, i proposen tot el coneixement multidimensional que es pot derivar des de les fonts de dades i, conseqüentment, generen massa resultats. En aquest escenari, l'automatització del disseny del magatzem de dades és essencial per evitar que tot el pes de la tasca recaigui en el dissenyador (d'aquesta forma, no hem de confiar únicament en la seva habilitat i coneixement per aplicar el mètode de disseny elegit). A més, l'automatització de la tasca allibera al dissenyador del sempre complex i costós anàlisi de les fonts de dades (que pot arribar a ser inviable per grans fonts de dades).Avui dia, els mètodes automatitzables analitzen en detall les fonts de dades i passen per alt els requeriments. En canvi, els mètodes basats en l'anàlisi dels requeriments no consideren l'automatització del procés, ja que treballen amb requeriments expressats en llenguatges d'alt nivell que un ordenador no pot manegar. Aquesta mateixa situació es dona en els mètodes híbrids actual, que proposen un enfocament seqüencial, on l'anàlisi de les dades es complementa amb l'anàlisi dels requeriments, ja que totes dues tasques pateixen els mateixos problemes que els enfocament purs.En aquesta tesi proposem dos mètodes per donar suport a la tasca de modelatge del magatzem de dades: MDBE (Multidimensional Design Based on Examples) and AMDO (Automating the Multidimensional Design from Ontologies). Totes dues consideren els requeriments i les fonts de dades per portar a terme la tasca de modelatge i a més, van ser pensades per superar les limitacions dels enfocaments actuals.1. MDBE segueix un enfocament clàssic, en el que els requeriments d'usuari són coneguts d'avantmà. Aquest mètode es beneficia del coneixement capturat a les fonts de dades, però guia el procés des dels requeriments i, conseqüentment, és capaç de treballar sobre fonts de dades semànticament pobres. És a dir, explotant el fet que amb uns requeriments de qualitat, podem superar els inconvenients de disposar de fonts de dades que no capturen apropiadament el nostre domini de treball.2. A diferència d'MDBE, AMDO assumeix un escenari on es disposa de fonts de dades semànticament riques. Per aquest motiu, dirigeix el procés de modelatge des de les fonts de dades, i empra els requeriments per donar forma i adaptar els resultats generats a les necessitats de l'usuari. En aquest context, a diferència de l'anterior, unes fonts de dades semànticament riques esmorteeixen el fet de no tenir clars els requeriments d'usuari d'avantmà.Cal notar que els nostres mètodes estableixen un marc de treball combinat que es pot emprar per decidir, donat un escenari concret, quin enfocament és més adient. Per exemple, no es pot seguir el mateix enfocament en un escenari on els requeriments són ben coneguts d'avantmà i en un escenari on aquestos encara no estan clars (un cas recorrent d'aquesta situació és quan l'usuari no té clares les capacitats d'anàlisi del seu propi sistema). De fet, disposar d'uns bons requeriments d'avantmà esmorteeix la necessitat de disposar de fonts de dades semànticament riques, mentre que a l'inversa, si disposem de fonts de dades que capturen adequadament el nostre domini de treball, els requeriments no són necessaris d'avantmà. Per aquests motius, en aquesta tesi aportem un marc de treball combinat que cobreix tots els possibles escenaris que podem trobar durant la tasca de modelatge del magatzem de dades.Previous experiences in the data warehouse field have shown that the data warehouse multidimensional conceptual schema must be derived from a hybrid approach: i.e., by considering both the end-user requirements and the data sources, as first-class citizens. Like in any other system, requirements guarantee that the system devised meets the end-user necessities. In addition, since the data warehouse design task is a reengineering process, it must consider the underlying data sources of the organization: (i) to guarantee that the data warehouse must be populated from data available within the organization, and (ii) to allow the end-user discover unknown additional analysis capabilities.Currently, several methods for supporting the data warehouse modeling task have been provided. However, they suffer from some significant drawbacks. In short, requirement-driven approaches assume that requirements are exhaustive (and therefore, do not consider the data sources to contain alternative interesting evidences of analysis), whereas data-driven approaches (i.e., those leading the design task from a thorough analysis of the data sources) rely on discovering as much multidimensional knowledge as possible from the data sources. As a consequence, data-driven approaches generate too many results, which mislead the user. Furthermore, the design task automation is essential in this scenario, as it removes the dependency on an expert's ability to properly apply the method chosen, and the need to analyze the data sources, which is a tedious and timeconsuming task (which can be unfeasible when working with large databases). In this sense, current automatable methods follow a data-driven approach, whereas current requirement-driven approaches overlook the process automation, since they tend to work with requirements at a high level of abstraction. Indeed, this scenario is repeated regarding data-driven and requirement-driven stages within current hybrid approaches, which suffer from the same drawbacks than pure data-driven or requirement-driven approaches.In this thesis we introduce two different approaches for automating the multidimensional design of the data warehouse: MDBE (Multidimensional Design Based on Examples) and AMDO (Automating the Multidimensional Design from Ontologies). Both approaches were devised to overcome the limitations from which current approaches suffer. Importantly, our approaches consider opposite initial assumptions, but both consider the end-user requirements and the data sources as first-class citizens.1. MDBE follows a classical approach, in which the end-user requirements are well-known beforehand. This approach benefits from the knowledge captured in the data sources, but guides the design task according to requirements and consequently, it is able to work and handle semantically poorer data sources. In other words, providing high-quality end-user requirements, we can guide the process from the knowledge they contain, and overcome the fact of disposing of bad quality (from a semantical point of view) data sources.2. AMDO, as counterpart, assumes a scenario in which the data sources available are semantically richer. Thus, the approach proposed is guided by a thorough analysis of the data sources, which is properly adapted to shape the output result according to the end-user requirements. In this context, disposing of high-quality data sources, we can overcome the fact of lacking of expressive end-user requirements.Importantly, our methods establish a combined and comprehensive framework that can be used to decide, according to the inputs provided in each scenario, which is the best approach to follow. For example, we cannot follow the same approach in a scenario where the end-user requirements are clear and well-known, and in a scenario in which the end-user requirements are not evident or cannot be easily elicited (e.g., this may happen when the users are not aware of the analysis capabilities of their own sources). Interestingly, the need to dispose of requirements beforehand is smoothed by the fact of having semantically rich data sources. In lack of that, requirements gain relevance to extract the multidimensional knowledge from the sources.So that, we claim to provide two approaches whose combination turns up to be exhaustive with regard to the scenarios discussed in the literaturePostprint (published version

On the enhancement of Big Data Pipelines through Data Preparation, Data Quality, and the distribution of Optimisation Problems

Nowadays, data are fundamental for companies, providing operational support by facilitating daily transactions. Data has also become the cornerstone of strategic decision-making processes in businesses. For this purpose, there are numerous techniques that allow to extract knowledge and value from data. For example, optimisation algorithms excel at supporting decision-making processes to improve the use of resources, time and costs in the organisation. In the current industrial context, organisations usually rely on business processes to orchestrate their daily activities while collecting large amounts of information from heterogeneous sources. Therefore, the support of Big Data technologies (which are based on distributed environments) is required given the volume, variety and speed of data. Then, in order to extract value from the data, a set of techniques or activities is applied in an orderly way and at different stages. This set of techniques or activities, which facilitate the acquisition, preparation, and analysis of data, is known in the literature as Big Data pipelines. In this thesis, the improvement of three stages of the Big Data pipelines is tackled: Data Preparation, Data Quality assessment, and Data Analysis. These improvements can be addressed from an individual perspective, by focussing on each stage, or from a more complex and global perspective, implying the coordination of these stages to create data workflows. The first stage to improve is the Data Preparation by supporting the preparation of data with complex structures (i.e., data with various levels of nested structures, such as arrays). Shortcomings have been found in the literature and current technologies for transforming complex data in a simple way. Therefore, this thesis aims to improve the Data Preparation stage through Domain-Specific Languages (DSLs). Specifically, two DSLs are proposed for different use cases. While one of them is a general-purpose Data Transformation language, the other is a DSL aimed at extracting event logs in a standard format for process mining algorithms. The second area for improvement is related to the assessment of Data Quality. Depending on the type of Data Analysis algorithm, poor-quality data can seriously skew the results. A clear example are optimisation algorithms. If the data are not sufficiently accurate and complete, the search space can be severely affected. Therefore, this thesis formulates a methodology for modelling Data Quality rules adjusted to the context of use, as well as a tool that facilitates the automation of their assessment. This allows to discard the data that do not meet the quality criteria defined by the organisation. In addition, the proposal includes a framework that helps to select actions to improve the usability of the data. The third and last proposal involves the Data Analysis stage. In this case, this thesis faces the challenge of supporting the use of optimisation problems in Big Data pipelines. There is a lack of methodological solutions that allow computing exhaustive optimisation problems in distributed environments (i.e., those optimisation problems that guarantee the finding of an optimal solution by exploring the whole search space). The resolution of this type of problem in the Big Data context is computationally complex, and can be NP-complete. This is caused by two different factors. On the one hand, the search space can increase significantly as the amount of data to be processed by the optimisation algorithms increases. This challenge is addressed through a technique to generate and group problems with distributed data. On the other hand, processing optimisation problems with complex models and large search spaces in distributed environments is not trivial. Therefore, a proposal is presented for a particular case in this type of scenario. As a result, this thesis develops methodologies that have been published in scientific journals and conferences.The methodologies have been implemented in software tools that are integrated with the Apache Spark data processing engine. The solutions have been validated through tests and use cases with real datasets

Construction de modèles de données relationnels temporalisés guidée par les ontologies

Au sein d’une organisation, de même qu’entre des organisations, il y a plusieurs intervenants qui doivent prendre des décisions en fonction de la vision qu’ils se font de l’organisation concernée, de son environnement et des interactions entre les deux. Dans la plupart des cas, les données sont fragmentées en plusieurs sources non coordonnées ce qui complique, notamment, le fait de retracer leur évolution chronologique. Ces différentes sources sont hétérogènes par leur structure, par la sémantique des données qu’elles contiennent, par les technologies informatiques qui les manipulent et par les règles de gouvernance qui les contrôlent. Dans ce contexte, un système de santé apprenant (Learning Health System) a pour objectif d’unifier les soins de santé, la recherche biomédicale et le transfert des connaissances, en offrant des outils et des services pour améliorer la collaboration entre les intervenants ; l’optique sous-jacente à cette collaboration étant de fournir à un individu de meilleurs services qui soient personnalisés. Les méthodes classiques de construction de modèle de données sont fondées sur des règles de pratique souvent peu précises, ad hoc, non automatisables. L’extraction des données d’intérêt implique donc d’importantes mobilisations de ressources humaines. De ce fait, la conciliation et l’agrégation des sources sont sans cesse à recommencer parce que les besoins ne sont pas tous connus à l’avance, qu’ils varient au gré de l’évolution des processus et que les données sont souvent incomplètes. Pour obtenir l’interopérabilité, il est nécessaire d’élaborer une méthode automatisée de construction de modèle de données qui maintient conjointement les données brutes des sources et leur sémantique. Cette thèse présente une méthode qui permet, une fois qu’un modèle de connaissance est choisi, la construction d’un modèle de données selon des critères fondamentaux issus d’un modèle ontologique et d’un modèle relationnel temporel basé sur la logique des intervalles. De plus, la méthode est semi- automatisée par un prototype, OntoRelα. D’une part, l’utilisation des ontologies pour définir la sémantique des données est un moyen intéressant pour assurer une meilleure interopérabilité sémantique étant donné que l’ontologie permet d’exprimer de façon exploitable automatiquement différents axiomes logiques qui permettent la description de données et de leurs liens. D’autre part, l’utilisation d’un modèle relationnel temporalisé permet l’uniformisation de la structure du modèle de données, l’intégration des contraintes temporelles ainsi que l’intégration des contraintes du domaine qui proviennent des ontologies.Within an organization, many stakeholders must make decisions based on their vision of the organization, its environment, and the interactions between these two. In most cases, the data are fragmented in several uncoordinated sources, making it difficult, in particular, to trace their chronological evolution. These different sources are heterogeneous in their structure, in the semantics of the data they contain, in the computer technologies that manipulate them, and in the governance rules that control them. In this context, a Learning Health System aims to unify health care, biomedical research and knowledge transfer by providing tools and services to enhance collaboration among stakeholders in the health system to provide better and personalized services to the patient. The implementation of such a system requires a common data model with semantics, structure, and consistent temporal traceability that ensures data integrity. Traditional data model design methods are based on vague, non-automatable best practice rules where the extraction of data of interest requires the involvement of very important human resources. The reconciliation and the aggregation of sources are constantly starting over again because not all needs are known in advance and vary with the evolution of processes and data are often incomplete. To obtain an interoperable data model, an automated construction method that jointly maintains the source raw data and their semantics is required. This thesis presents a method that build a data model according to fundamental criteria derived from an ontological model, a relational model and a temporal model based on the logic of intervals. In addition, the method is semi-automated by an OntoRelα prototype. On the one hand, the use of ontologies to define the semantics of data is an interesting way to ensure a better semantic interoperability since it automatically expresses different logical axioms allowing the description of data and their links. On the other hand, the use of a temporal relational model allows the standardization of data model structure and the integration of temporal constraints as well as the integration of domain constraints defines in the ontologies

Modelling ETL conciliation tasks using relational algebra operators

The design and development of a data warehousing system (DWS) tends to be an exceptional resource consuming project which in turn makes it a high risk/reward project. In order to minimize the risk, some design methodologies and tools are used along the several phases of the project. The Extract-Transform-Load (ETL) component is normally one of the most critical components of a DWS since it gathers, corrects and conforms data in order to be loaded into the Data Warehouse (DW). Data conciliation task tends to be a dull and manual intensive job that often deals with several heterogeneous sources which is critical to the correct representation of the enterprise’s information. The manual nature of this task makes it prone to errors and subject of intensive and successive monitoring. In this paper, we analyse some of the most common ETL tasks for data conciliation using a Relational Algebra approach, as an effort to standardize them for future use in a generic ETL environment. A slowly changed dimension scenario will be used to support the data conciliation modelling process designed for this work

Ecology-based planning. Italian and French experimentations

This paper examines some French and Italian experimentations of green infrastructures’ (GI) construction in relation to their techniques and methodologies. The construction of a multifunctional green infrastructure can lead to the generation of a number of relevant bene fi ts able to face the increasing challenges of climate change and resilience (for example, social, ecological and environmental through the recognition of the concept of ecosystem services) and could ease the achievement of a performance-based approach. This approach, differently from the traditional prescriptive one, helps to attain a better and more fl exible land-use integration. In both countries, GI play an important role in contrasting land take and, for their adaptive and cross-scale nature, they help to generate a res ilient approach to urban plans and projects. Due to their fl exible and site-based nature, GI can be adapted, even if through different methodologies and approaches, both to urban and extra-urban contexts. On one hand, France, through its strong national policy on ecological networks, recognizes them as one of the major planning strategies toward a more sustainable development of territories; on the other hand, Italy has no national policy and Regions still have a hard time integrating them in already existing planning tools. In this perspective, Italian experimentations on GI construction appear to be a simple and sporadic add-on of urban and regional plans

Die Qualität von Organisationen : ein kommunikationsbasierter Messansatz

The goal of this research is to develop an understanding of what causes organizations and information systems to be “good” with regard to communication and coordination. This study (1) gives a theoretical explanation of how the processes of organizational adaptation work and (2) what is required for establishing and measuring the goodness of an organization with regard to communication and coordination. By leveraging concepts from cybernetics and philosophy of language, particularly the theoretical conceptualization of information systems as social systems and language communities, this research arrives at new insights. After discussing related work from systems theory, organization theory, cybernetics, and philosophy of language, a theoretical conceptualization of information systems as language communities is adopted. This provides the foundation for two exploratory field studies. Then a formal theory for explaining the adaptation of organizations via language and communication is presented. This includes measures for the goodness of organizations with regard to communication and coordination. Finally, propositions stemming from the theoretical model are tested using multiple case studies in six information system development projects in the financial services industry.Zielsetzung der hier vorgestellten Forschung ist es, ein Verständnis für die Güte von Organisationen und Informationssystemen im Hinblick auf Kommunikation und Koordination zu entwickeln. Diese Studie gibt (1) eine theoretische Erklärung zur Funktionsweise organisatorischer Anpassungsprozesse und (2) Handlungsanleitungen zur Messung der Güte einer Organisation im Hinblick auf Kommunikation und Koordination. Dies geschieht durch die Nutzung von Konzepten der Kybernetik und der Sprachphilosophie, insbesondere der Formalisierung von Informationssystemen als soziale Systeme und Sprachgemeinschaften. Nach der Diskussion bestehender Ansätze in der Systemtheorie, der Organisationstheorie, der Kybernetik und der Sprachphilosophie wird die Konzeptualisierung von Informationssystemen als Sprachgemeinschaften übernommen. Diese bildet die Grundlage für zwei explorative Feldstudien. Im Anschluss wird eine Theorie zur Erklärung der Anpassung von Organisationen durch Sprache und Kommunikation vorgestellt. Dies beinhaltet Maße für die Güte von Organisationen im Hinblick auf Kommunikation und Koordination. Schließlich werden anhand dieses theoretischen Modells Hypothesen aufgestellt und in einer multiplen Fallstudie in sechs Informationssystementwicklungsprojekten in der Finanzdienstleistungsindustrie überprüft

Environmental and territorial modelling for planning and design

Between 5th and 8th September 2018 the tenth edition of the INPUT conference took place in Viterbo, guests of the beautiful setting of the University of Tuscia and its DAFNE Department. INPUT is managed by an informal group of Italian academic researchers working in many fields related to the exploitation of informatics in planning. This Tenth Edition pursed multiple objectives with a holistic, boundary-less character, to face the complexity of today socio-ecological systems following a systemic approach aimed to problem solving. In particular, the Conference will aim to present the state of art of modeling approaches employed in urban and territorial planning in national and international contexts. Moreover, the conference has hosted a Geodesign workshop, by Carl Steinitz (Harvard Graduate School of Design) and Hrishi Ballal (on skype), Tess Canfield, Michele Campagna. Finally, on the last day of the conference, took place the QGIS hackfest, in which over 20 free software developers from all over Italy discussed the latest news and updates from the QGIS network. The acronym INPUT was born as INformatics for Urban and Regional Planning. In the transition to graphics, unintentionally, the first term was transformed into “Innovation”, with a fine example of serendipity, in which a small mistake turns into something new and intriguing. The opportunity is taken to propose to the organizers and the scientific committee of the next appointment to formalize this change of the acronym. This 10th edition was focused on Environmental and Territorial Modeling for planning and design. It has been considered a fundamental theme, especially in relation to the issue of environmental sustainability, which requires a rigorous and in-depth analysis of processes, a theme which can be satisfied by the territorial information systems and, above all, by modeling simulation of processes. In this topic, models are useful with the managerial approach, to highlight the many aspects of complex city and landscape systems. In consequence, their use must be deeply critical, not for rigid forecasts, but as an aid to the management decisions of complex systems