Feature-based validation reasoning for intent-driven engineering design

Feature based modelling represents the future of CAD systems. However, operations such as modelling and editing can corrupt the validity of a feature-based model representation. Feature interactions are a consequence of feature operations and the existence of a number of features in the same model. Feature interaction affects not only the solid representation of the part, but also the functional intentions embedded within features. A technique is thus required to assess the integrity of a feature-based model from various perspectives, including the functional intentional one, and this technique must take into account the problems brought about by feature interactions and operations. The understanding, reasoning and resolution of invalid feature-based models requires an understanding of the feature interaction phenomena, as well as the characterisation of these functional intentions. A system capable of such assessment is called a feature-based representation validation system. This research studies feature interaction phenomena and feature-based designer's intents as a medium to achieve a feature-based representation validation system. [Continues.

A Model-Driven Approach to Automate Data Visualization in Big Data Analytics

In big data analytics, advanced analytic techniques operate on big data sets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in-house technical skills, the H2020 TOREADOR Project adopts a model-driven architecture for streamlining analysis processes, from data preparation to their visualization. In this paper we propose a new approach named SkyViz focused on the visualization area, in particular on (i) how to specify the user's objectives and describe the dataset to be visualized, (ii) how to translate this specification into a platform-independent visualization type, and (iii) how to concretely implement this visualization type on the target execution platform. To support step (i) we define a visualization context based on seven prioritizable coordinates for assessing the user's objectives and conceptually describing the data to be visualized. To automate step (ii) we propose a skyline-based technique that translates a visualization context into a set of most-suitable visualization types. Finally, to automate step (iii) we propose a skyline-based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on the one hand, more visualization coordinates on the other. The paper is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project

Brave New GES World:A Systematic Literature Review of Gestures and Referents in Gesture Elicitation Studies

How to determine highly effective and intuitive gesture sets for interactive systems tailored to end users’ preferences? A substantial body of knowledge is available on this topic, among which gesture elicitation studies stand out distinctively. In these studies, end users are invited to propose gestures for specific referents, which are the functions to control for an interactive system. The vast majority of gesture elicitation studies conclude with a consensus gesture set identified following a process of consensus or agreement analysis. However, the information about specific gesture sets determined for specific applications is scattered across a wide landscape of disconnected scientific publications, which poses challenges to researchers and practitioners to effectively harness this body of knowledge. To address this challenge, we conducted a systematic literature review and examined a corpus of N=267 studies encompassing a total of 187, 265 gestures elicited from 6, 659 participants for 4, 106 referents. To understand similarities in users’ gesture preferences within this extensive dataset, we analyzed a sample of 2, 304 gestures extracted from the studies identified in our literature review. Our approach consisted of (i) identifying the context of use represented by end users, devices, platforms, and gesture sensing technology, (ii) categorizing the referents, (iii) classifying the gestures elicited for those referents, and (iv) cataloging the gestures based on their representation and implementation modalities. Drawing from the findings of this review, we propose guidelines for conducting future end-user gesture elicitation studies

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

Mastering the requirements analysis for communication-intensive websites

Web application development still needs to employ effective methods to accommodate some distinctive aspects of the requirements analysis process: capturing high-level communication goals, considering several user profiles and stakeholders, defining hypermedia-specific requirements (concerning navigation, content, information structure and presentation aspects), and reusing requirements for an effective usability evaluation. Techniques should be usable by both stakeholders and the design team, require little training effort, and show relative advantage to project managers. Over the last few years, requirements methodologies applied to web-based applications have considered mainly the transactional and operational aspects typical of traditional information systems. The communicational aspects of web sites have been neglected in regards to systematic requirements methods. This thesis, starting from key achievements in Requirements Engineering (hereafter RE), introduces a model (AWARE) for defining and analyzing requirements for web applications mainly conceived as strategic communication means for an institution or organization. The model extends traditional goal and scenario-based approaches for refining highlevel goals into website requirements, by introducing the analysis of ill-defined user goals, stakeholder communication goals, and a hypermedia requirement taxonomy to facilitate web conceptual design, and paving the way for a systematic usability evaluation. AWARE comprises a conceptual toolkit and a notation for effective requirements documentation. AWARE concepts and notation represent a useful communication and analysis conceptual tool that may support in the elicitation, negotiation, analysis and validation of requirements from the relevant stakeholders (users included). The empirical validation of the model is carried out in two ways. Firstly, the model has been employed in web projects on the field. These case studies and the lessons learnt will be presented and discussed to assess advantages and limits of the proposal. Secondly, a sample of web analysts and designers has been asked to study and apply the model: the feedback gathered is positive and encouraging for further improvement.Lo sviluppo di applicazioni web necessita di strumenti efficaci per gestire alcuni aspetti essenziali del processo di analisi dei requisiti: l'identificazione di obiettivi di comunicazione strategici, la presenza di una varietà di profili utente e di stakeholders, le definizione di requisiti ipermediali (riguardanti navigazione, interazione, contenuto e presentazione), e il riuso dei requisiti per una pianificazione efficace della valutazione dell'usabilità. Sono necessarie tecniche usabili sia dagli stakeholders che dai progettisti, che richiedono un tempo breve per essere appresi ed usati con efficacia, mostrando vantaggi significativi ai gestori di progetti complessi. La tesi definisce AWARE (Analysis of Web Application Requirements) - una metodologia per l'analisi dei requisiti specifica per la gestione di siti web (ed applicazioni interattive) con forti componenti comunicative. La metodologia estende le tecniche esistenti dell''analisi dei requisiti basate su approcci goal-oriented e scenario-based, introducendo una tassonomia di requisiti specifica per siti web (che permette di dare un input strutturato all'attività di progetazione), strumenti per l'identificazione e l'analisi di obiettivi ill-defined (generici o mal-definiti) e di obiettivi comunicativi e supporto metodologico per la valutazione dell'usabilità basata sui requisiti dell'applicazione. La metodologia AWARE è stata valutata sul campo attraverso progetti con professionisti del settore (web designers e IT managers), e grazie ad interventi di formazione in aziende specializzate nella comunicazione su web

Fuzzy Inference Systems for Risk Appraisal in Military Operational Planning

Advances in computing and mathematical techniques have given rise to increasingly complex models employed in the management of risk across numerous disciplines. While current military doctrine embraces sound practices for identifying, communicating, and mitigating risk, the complex nature of modern operational environments prevents the enumeration of risk factors and consequences necessary to leverage anything beyond rudimentary risk models. Efforts to model military operational risk in quantitative terms are stymied by the interaction of incomplete, inadequate, and unreliable knowledge. Specifically, it is evident that joint and inter-Service literature on risk are inconsistent, ill-defined, and prescribe imprecise approaches to codifying risk. Notably, the near-ubiquitous use of risk matrices (along with other qualitative methods), are demonstrably problematic at best, and downright harmful at worst, due to misunderstanding and misapplication of their quantitative implications. The use of fuzzy set theory is proposed to overcome the pervasive ambiguity of risk modeling encountered by today’s operational planners. Fuzzy logic is adept at addressing the problems caused by imperfect and imprecise knowledge, entangled causal relationships, and the linguistic input of expert opinion. To this end, a fuzzy inference system is constructed for the purpose of risk appraisal in military operational planning

Model-based Approach for Product Requirement Representation and Generation in Product Lifecycle Management

The requirement specification is an official documentation activity, which is a collection of certain information to specify the product and its life-cycle activities in terms of functions, features, performance, constraints, production, maintenance, disposal process, etc. It contains mainly two phases; product requirement generation and representation. Appropriate criteria for the product design and further life-cycle activities are determined based on the requirement specification as well as the interrelations of product requirements with other life-cycle information such as; materials, manufacturing, working environments, finance, and regulations. The determination of these criteria is normally error-prone. It is difficult to identify and maintain the completeness and consistency of the requirement information across the product life-cycle. Product requirements are normally expressed in abstract and conceptual terms with document base representation which yields unstructured and heterogeneous information base and it is unsuitable for intelligent machine interpretations. Most of the time determination of the requirements and development of the requirement specification documents are performed by the designers/engineers based on their own experiences that might lead to incompleteness and inconsistency. This research work proposes a unique model-based product requirement representation and generation architecture to aid designers/engineers to specify product requirements across the product life-cycle. A requirement knowledge management architecture is developed to enhance the capabilities of the current Product Life-cycle Management (PLM) platforms in terms of product requirement representation and generation. After a systematic study on the categorization of product requirements, an ontological framework is developed for the specification of the requirements and related product life-cycle domain information. The ontological framework is embedded in an existing PLM system. A computational platform is developed and integrated into the PLM system for the intelligent machine processing of the product requirements and related information. This architecture supports product requirement representation in terms of the ontological framework and further information retrieval, inference, and requirement text generation activities

Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning

Power outages and fluctuations represent serious crisis situations in energy-intensive process industry like glass and paper production, where substances such as oil, gas, wood fibers or chemicals are processed. Power disruptions can interrupt chemical reactions and produce tons of waste as well as damage of machine parts. But, despite of the obvious criticality, handling of outages in manufacturing focuses on commissioning of expensive proprietary power plants to protect against power outages and implicit gut feeling in anticipating potential disruptions. With AISOP, we introduce a model for AI-based scenario planning for predicting crisis situations. AISOP uses conceptual, well-defined scenario patterns to capture entities of crisis situations. Data streams are mapped onto these patterns for determining historic crisis scenarios and predicting future crisis scenarios by using inductive knowledge and machine learning. The model was exemplified within a proof of concept for energy-driven disruption prediction. We were able to evaluate the proposed approach by means of a set of data streams on weather and outages in Germany in terms of performance in predicting potential outages for manufacturers of paper industry with promising results

ON THE USE OF THE DEMPSTER SHAFER MODEL IN INFORMATION INDEXING AND RETRIEVAL APPLICATIONS

The Dempster Shafer theory of evidence concerns the elicitation and manipulation of degrees of belief rendered by multiple sources of evidence to a common set of propositions. Information indexing and retrieval applications use a variety of quantitative means - both probabilistic and quasi-probabilistic - to represent and manipulate relevance numbers and index vectors. Recently, several proposals were made to use the Dempster Shafes model as a relevance calculus in such applications. The paper provides a critical review of these proposals, pointing at several theoretical caveats and suggesting ways to resolve them. The methodology is based on expounding a canonical indexing model whose relevance measures and combination mechanisms are shown to be isomorphic to Shafer's belief functions and to Dempster's rule, respectively. Hence, the paper has two objectives: (i) to describe and resolve some caveats in the way the Dempster Shafer theory is applied to information indexing and retrieval, and (ii) to provide an intuitive interpretation of the Dempster Shafer theory, as it unfolds in the simple context of a canonical indexing model.Information Systems Working Papers Serie