Method Support of Information Requirements Analysis for Analytical Information Systems: State of the Art, Practice Requirements, and Research Agenda

Due to specific characteristics of analytical information systems, their development varies significantly from transaction-oriented systems. Specific method support is particularly needed for requirements engineering and its information-related component, information requirements analysis. The paper at hand first evaluates the state of the art and identifies necessary method support extensions. On this basis, method support requirements for information requirements engineering are identified. The survey is structured along the five core activities of traditional requirements engineering. It reveals a need for further research especially on information requirements elicitation, validation, and management. It further contributes to a discussion of aspects that should be considered by any method support. Due to comparatively long life cycles of analytical information systems, the introduction of a process perspective is discussed in order to ensure the continuous elicitation, documentation, and management of information requirement

Method Support of Information Requirements Analysis for Analytical Information Systems State of the Art, Practice Requirements, and Research Agenda

Due to specific characteristics of analyticalinformation systems, their developmentvaries significantly from transaction-oriented systems. Specific methodsupport is particularly needed forrequirements engineering and itsinformation-related component, informationrequirements analysis. The paperat hand first evaluates the state ofthe art and identifies necessary methodsupport extensions.On this basis,methodsupport requirements for informationrequirements engineering are identified.The survey is structured alongthe five core activities of traditional requirementsengineering. It reveals aneed for further research especially oninformation requirements elicitation,validation, and management. It furthercontributes to a discussion of aspectsthat should be considered by anymethod support. Due to comparativelylong life cycles of analytical informationsystems, the introduction of a processperspective is discussed in order to ensurethe continuous elicitation, documentation,and management of informationrequirements

Ingeniería de requerimientos orientado a objetivos en almacenes de datos: un estudio comparativo

Data warehouses provide historical information about the organization that needs to be analyzed by the decision makers; therefore, it is essential to develop them in the context of a strategic business plan. In recent years, a number of engineering approaches for goal-oriented requirements have been proposed, which can obtain the information requirements of a data warehouse using traditional techniques and the objectives of the modeling. This paper provides an overview and a comparative study of the treatment of the requirements in the existing approaches to serve as a starting point for further research.Los almacenes de datos proveen información histórica de la organización que requiere ser analizada por los tomadores de decisiones, por lo que es primordial desarrollarlos en el contexto del plan estratégicos del negocio. En los últimos años se han propuesto una serie de enfoques de ingeniería de requerimientos orientada a objetivos que permiten obtener los requisitos de información, a cubrir por el almacén de datos, mediante técnicas tradicionales y del modelado de objetivos. Este trabajo, ofrece una visión general y un estudio comparativo del tratamiento de los requisitos en los actuales enfoques con el fin de servir de punto de inicio a posteriores trabajos de investigación.This work was financed by the Universidad de La Frontera. DIU-FRO Project DI13-0047

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Designing Business Analytics Solutions - A Model-Driven Approach

The design and development of data analytics systems, as a new type of information systems, has proven to be complicated and challenging. Model based approa- ches from information systems engineering can potentially provide methods, techniques, and tools for facilitating and supporting such processes. The contribution of this paper is twofold. Firstly, it introduces a conceptual modeling framework for the design and development of advanced analytics systems. It illustrates the framework through a case and provides a sample methodological approach for using the framework. The paper demonstrates potential benefits of the framework for requirements elicitation, clarification, and design of analytical solutions. Secondly, the paper presents some observations and lessons learned from an application of the framework by an experienced practitioner not involved in the original development of the framework. The findings were then used to develop a set of guidelines for enhancing the understandability and effec- tive usage of the framework

A proposal for the management of data driven services in smart manufacturing scenarios

205 p.This research work focuses on Industrial Big Data Services (IBDS) Providers, a specialization of ITServices Providers. IBDS Providers constitute a fundamental agent in Smart Manufacturing scenarios,given the wide spectrum of complex technological challenges involved in the adoption of the requireddata-related IT by manufacturers aiming at shifting their businesses towards Smart Manufacturing. Theoverarching goal of this research work is to provide contributions that (a) help the business sector ofIBDS Providers to manage their collaboration projects with manufacturing partners in order to deploy therequired data-driven services in Smart Manufacturing scenarios, and (b) adapt and extend existingconceptual, methodological, and technological proposals in order to include those practical elements thatfacilitate their use in business contexts. The main contributions of this dissertation focus on three specificchallenges related to the early stages of the data lifecycle, i.e. those stages that ensure the availability ofnew data to exploit, coming from monitored manufacturing facilities: (1) Devising a more efficient datastorage strategy that reduces the costs of the cloud infrastructure required by an IBDS Provider tocentralize and accumulate the massive-scale amounts of data from the supervised manufacturingfacilities; (2) Designing the required architecture for the data capturing and integration infrastructure thatsustains an IBDS Provider's platform; (3) The collaborative design process with partnering manufacturersof the required data-driven services for a specific manufacturing sector

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

Dynamic process modelling for business engineering and information systems evaluation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is concerned with the pre-implementation evaluation of investments in Information Systems (IS). IS evaluation is important as organisations need to assess the financial justifiability of business change proposals that include (but usually are not limited to) the introduction of IS applications. More specifically, this research addresses the problem of benefits assessment within IS evaluation. We contend that benefits assessment should not be performed at the level of the IS application, as most extant evaluation methods advocate. Instead, to study the dynamics and the interactions of the IS applications with their surrounding environment, we propose to adopt the business process as the analytic lens of evaluation and to assess the impacts of IS on organisational, rather than on technical, performance indicators. Drawing on these propositions, this research investigates the potential of dynamic process modelling (via discrete-event simulation) as a facilitator of IS evaluation. We argue that, in order to be effective evaluation tools, business process models should be able to explicitly incorporate the effects of IS introduction on business performance, an issue that is found to be under-researched in previous literature. The above findings serve as the central theme for the development of a design theory of IS evaluation by simulation. The theory provides prescriptive elements that refer both to the design products of the evaluation and the design process by which these products can come into reality. The theory draws on a set of kernel theories from the business engineering domain and proposes a set of meta-requirements that should be satisfied by business process models, a meta-design structure that meets these requirements, and a design method that provides guidance in applying the theoretical propositions in practice. The design theory is developed and empirically tested by means of two real-life case studies. The first study is used to complement the findings of a literature review and to drive the development of the design theory's components, while the second study is employed to validate and further enhance the theory's propositions. The research results support the arguments for simulation-assisted IS evaluation and demonstrate the contribution of the design theory to the field

Verifying goal-oriented specifications used in model-driven development processes

[EN] Goal-oriented requirements engineering promotes the use of goals to elicit, elaborate, structure, specify, analyze, negotiate, document, and modify requirements. Thus, goal-oriented specifications are essential for capturing the objectives that the system to be developed should achieve. However, the application of goal oriented specifications into model-driven development (MDD) processes is still handcrafted, not aligned in the automated flow from models to code. In other words, the experience of analysts and designers is necessary to manually transform the input goal-oriented models into system models for code generation (models compilation). Some authors have proposed guidelines to facilitate and partially automate this translation, but there is a lack of techniques to assess the adequacy of goal-oriented models as starting point of MDD processes. In this paper, we present and evaluate a verification approach that guarantees the automatic, correct, and complete transformation of goal-oriented models into design models used by specific MDD solutions. In particular, this approach has been put into practice by adopting a well-known goal-oriented modeling approach, the i* framework, and an industrial MDD solution called Integranova.This work has been developed with the support of FONDECYT under the projects AMoDDI 11130583 and TESTMODE 11121395.This work is also supported by EOSSAC project, funded by the Ministry of Economy and Competitiveness of the Spanish government (TIN2013-44641-P).Giachetti Herrera, GA.; Marín, B.; López, L.; Franch, X.; Pastor López, O. (2017). Verifying goal-oriented specifications used in model-driven development processes. Information Systems. 64:41-62. https://doi.org/10.1016/j.is.2016.06.011S41626