Enterprise Information Systems

The omnipresence of event data and powerful process mining techniques make it possible to quickly learn process models describing what people and organizations really do. Recent breakthroughs in process mining resulted in powerful techniques to discover the real processes, to detect deviations from normative process models, and to analyze bottlenecks and waste. Process mining and other data science techniques can be used to improve processes within any organization. However, there are also great concerns about the use of data for such purposes. Increasingly, customers, patients, and other stakeholders worry about “irresponsible” forms of data science. Automated data decisions may be unfair or non-transparent. Confidential data may be shared unintentionally or abused by third parties. Each step in the “data science pipeline” (from raw data to decisions) may create inaccuracies, e.g., if the data used to learn a model reflects existing social biases, the algorithm is likely to incorporate these biases. These concerns could lead to resistance against the large-scale use of data and make it impossible to reap the benefits of process mining and other data science approaches. This paper discusses Responsible Process Mining (RPM) as a new challenge in the broader field of Responsible Data Science (RDS). Rather than avoiding the use of (event) data altogether, we strongly believe that techniques, infrastructures and approaches can be made responsible by design. Not addressing the challenges related to RPM/RDS may lead to a society where (event) data are misused or analysis results are deeply mistrusted.

Genres of communications significantly influence the evolution of a field of research. In the Information Systems (IS) domain, a debate has recently emerged on the chance to implement alternative genres in the representation of IS publication and practice. We hence propose to apply story-telling and narrative as alternative genres to write publications reporting IS research. By presenting a narration exemplifying the implementation of these genres, we argue that the incremental introduction of their principles would be beneficial to IS research, enabling a revisited representation of IS themes that extends the boundaries of canonical genres. In parallel, thanks to the peculiarity of the story narrated, we claim that story-telling and narrative are also powerful instruments supporting IS practice. The mediating role of action research in enhancing the link between story-telling and narrative used as writing genres and practices will also be recognized.

The global conditions for manufacturing are rapidly changing towards shorter product life cycles, more complexity and more turbulence. The manufacturing industry must meet the demands of this shifting environment and the increased global competition by ensuring high product quality, continuous improvement of processes and increasingly flexible organization. Technological developments towards smart manufacturing create big industrial data which needs to be leveraged for competitive advantages. We present a novel IT architecture for data-driven manufacturing, the Stuttgart IT Architecture for Manufacturing (SITAM). It addresses the weaknesses of traditional manufacturing IT by providing IT systems integration, holistic data analytics and mobile information provisioning. The SITAM surpasses competing reference architectures for smart manufacturing because it has a strong focus on analytics and mobile integration of human workers into the smart production environment and because it includes concrete recommendations for technologies to implement it, thus filling a granularity gap between conceptual and case-based architectures. To illustrate the benefits of the SITAM’s prototypical implementation, we present an application scenario for value-added services in the automotive industry.

Monitoring systems targeting to improve decision making in emergency scenarios are currently benefiting from crowdsourcing information. The main issue with such kind of data is that the gathered reports quickly become too similar among themselves. Hence, too much similar reports, namely near-duplicates, do not add valuable knowledge to assist crisis control committees in their decision making tasks. The current approaches to detect near-duplicates are usually based on a twofold processing, where the first phase relies on similarity queries or clustering techniques, whereas the second and most computationally costly phase refines the result from the first one. Aimed at reducing that cost and also improving the ability of near-duplication detection, we developed a framework model based on the similarity wide-join database operator. This paper extends the wide-join definition empowering it to surpass its restrictions and provides an efficient algorithm based on pivots that speeds up the entire process, whereas enabling to retrieve the most similar elements in a single-pass. We also investigate alternatives and propose efficient algorithms to choose the pivots. Experiments using real datasets show that our framework is up to three orders of magnitude faster than the competing techniques in the literature, whereas it also improves the quality of the result in about 35%.

In this paper we present an interaction technique for coordinating agents that use rewards generated by Reinforcement Learning algorithms. Agents that coordinate with each other by exchanging rewards need mechanisms to help them while they interact to discover action policies. Because of the peculiarities of the environment and the objectives of each agent, there is no guarantee that a coordination model can converge them to an optimal policy. One possibility is to take advantage of existing models so that a mechanism that is less sensitive to the system variables emerges. The technique described here is based on three models previously studied in which agents (i) share learning in a predefined cycle of interactions, (ii) cooperate at every interaction and (iii) cooperate when an agent reaches the goal-state. Traffic scenarios were generated as a way of validating the proposed technique. The results showed that even when the computational complexity was increased the gains in terms of convergence make the technique superior to classical Reinforcement Learning approaches.

In this article we propose a safest path route choice algorithm which determines the safest path directions for pedestrians in case of fire. We also propose an escape route system for emergency evacuation management. The model and the algorithms are implemented in an open source framework (JuPedSim) which is a research platform to simulate pedestrian dynamics. The proposed algorithm allows the even distribution of the evacuees to all available emergency exits. We simulate the evacuation of a shopping centre and show that the application of the algorithm can reduce the total evacuation time up to 63% depending on the settings of the algorithm. Based on those results we elaborate an escape route system for emergency evacuation. The system includes three modules and can be operated in several modes. The designed system allow not only significantly to reduce the evacuation time but also to ensure people’s safety during evacuation.

Production planning prepares companies to a future production scenario. The decision process followed to obtain the production plan considers real data and estimated data of this future scenario. However, these plans can be affected by unexpected events that alter the planned scenario and in consequence, the production planning. This is especially critical when the production planning is ongoing. Thus providing information about these events can be critical to reconsider the production planning. We herein propose an event monitoring system to identify events and to classify them into different impact levels. The information obtained from this system helps to build a risk matrix, which determines the significance of the risk from the impact level and the likelihood. A prototype has been built following this proposal.

The present work is concerned with the placement of router nodes in industrial environments for wireless sensor networks applications making use of artificial immune systems ideas. The motivation for using artificial immune systems lies on the properties of uniqueness, distributed sensing, learning and memory efficiency of such systems enabling the transmission of data from sensors to the gateway efficiently. As a matter of fact, that efficiency deals with a low rate of failure and other aspects such as minimizing retransmission issues done by the routers. The chosen criteria to be met are embedded in the so called affinity function which acts as an objective function. The router nodes positioning are accomplished in two modules which uses the immune systems concepts and related ideas. Different scenarios are considered for the presented examples based on oil and gas configurations and for criteria defined in the affinity function.

The paper suggests a new method of transiting from Traditional Software Development (TSD) to Agile Software Development (ASD) called non-disruptive transition. The novelty of the method consists of allowing to complete the major transition steps to the agile “mindset” while remaining in the frame of an already established TSD process. The method is being developed using a knowledge transformation perspective to identify the main features of ASD mindset and how it differs from the one of TSD. More specifically, it uses a version of Nonako’s SECI model to represent software development. To analyze the current mindset and plan the movement to the mindset that is more agile, the paper suggests using a process modelling technique that considers the software development process as a complex socio-technical system. The paper also discusses external conditions that might hinder going all the way to becoming agile and require the transition to stop, and how to become agile while developing complex systems.

In Software Engineering, Knowledge Mapping is a process to discover aspects or meanings through the analysis of relationships between artifacts or people. However, to create a knowledge map, we need a process for capturing and analyzing data, so that we can extract information that reflects those aspects. In this paper, we propose a knowledge mapping process that generates a knowledge map and a set of knowledge profiles considering each mapped member. We developed a new technique by improving existing techniques in literature. In addition, we planned and performed a pilot study in a Research and Development (R&D) group. In this paper, we present our findings regarding the application of the proposed technique and the analysis of the knowledge map for that group. Additionally, we generated links between the knowledge profiles and collected lessons learned for one of the projects that was performed by this R&D group.

Research projects are an integral part of each university’s activity. Recently many theorists and practitioners have started to consider how such projects should be managed (e.g. [13]). They analyse if it is better to use traditional project management (TPM) or agile project management (APM). In this paper we propose agile-similar approach based on project crashing and stakeholder analysis to manage research project. First we present a theoretical introduction. Then we present a model for the proposed approach and its Linear Programing (LP) implementation. Finally we present an example of using the proposed model and we formulate conclusions.

Web application developers are not all experts. Even if they use methods such as UWE (UML web engineering) and CASE tools, they are not always able to make good decisions regarding the content of the web application, the navigation schema, and/or the presentation of information. Literature provides them with many guidelines for these tasks. However this knowledge is disseminated in many sources and not structured. In this paper, we perform a knowledge capitalization of all these guidelines. The contribution is threefold: (i) we propose a meta-model allowing a rich representation of these guidelines, (ii) we propose a grammar enabling the description of existing guidelines, (iii) based on this grammar, we developed a guideline management tool. Future research will consist in enriching the UWE method with this knowledge base leading to a quality based approach. Thus, our tool enriches existing UWE-based Computer Aided Software Engineering prototypes with ad hoc guidance.

A cloud database is a database that typically runs on a cloud computing platform. There are two common deployment models: users can run databases on virtual machines hosted and managed by a infrastructure as a service provider, or they can purchase access to a database service, maintained by a software as a service provider, without physically launching a virtual machine instance for the database. In a database service, application owners do not have to install and maintain the database themselves. Instead, the database as a service provider takes responsibility for installing and maintaining the database, and application owners pay according to their usage. Thus, database services decrease the need for local data storage and the infrastructure costs. Nevertheless, hosting confidential data at a database service requires the transfer of control of the data to a semi-trusted external provider. Therefore, data confidentiality is an important concern from cloud service providers. Recently, three main approaches have been introduced to ensure data confidentiality in cloud services: data encryption; combination of encryption and fragmentation; and fragmentation. Besides, other strategies use a mix of these three main approaches. In this paper, we present i-OBJECT, a new mechanism to preserve data confidentiality in database service scenarios. The proposed mechanism uses information decomposition to split data into unrecognizable parts and store them in different cloud service providers. Additionally, i-OBJECT is a flexible mechanism since it can be used alone or together with other previously approaches in order to increase the data confidentiality level. Thus, a user may trade performance or data utility for a potential increase in the degree of data confidentiality. Experimental results show the potential efficiency of i-OBJECT.

In order to effectively manage technical debt (TD), a set of indicators has been used by automated approaches to identify TD items. However, some debt items may not be directly identified using only metrics collected from the source code. CVM-TD is a model to support the identification of technical debt by considering the developer point of view when identifying TD through code comment analysis. In this paper, we investigate the use of CVM-TD with the purpose of characterizing factors that affect the accuracy of the identification of TD, and the most chosen patterns by participants as decisive to indicate TD items. We performed a controlled experiment investigating the accuracy of CVM-TD and the influence of English skills and developer experience factors. We also investigated if the contextualized vocabulary provided by CVM-TD points to candidate comments that are considered indicators of technical debt by participants. The results indicated that CVM-TD provided promising results considering the accuracy values. English reading skills have an impact on the TD detection process. We could not conclude that the experience level affects this process. We identified a list of the 20 most chosen patterns by participants as decisive to indicate TD items. The results motivate us continuing to explore code comments in the context of TD identification process in order to improve CVM-TD.

In this article we present a platform, called DataCollector, which aims fostering of use and appropriateness of open data by different open data ecosystem actors. The DataCollector helps data consumers searching and consuming open data as well as allows data producers to easily register, update and refine their datasets. This platform also allows the collaboration between the actors by providing features to collect and manage feedback about the open data. The proposed platform was evaluated by its viability in cataloging 14 Brazilian open data portals, covering a total of 29,540 datasets. The preliminary results indicate the DataCollector offers a robust solution for cataloging and access to distributed datasets in multiple platforms for open data publication.

Ambient Assisted Living are equipped with ubiquitous technologies, and use sensors as their main element for environmental data collection, providing systems with updated information. Currently, there is a convergence combining systems for smart environments and uncertainty reasoning. Considering that the world population is aging, health-support issues are in evidence, and many dangerous situations concerning users in their living environment may arise. However, reasoning to detect situations taking into account uncertainty presents a great challenge. This paper describes a contextual model based on semantic web technologies that deals with uncertainty. This model may be used to detect unwanted situations with a certain grade of contextual uncertainty. The model was evaluated in scenario exhibiting the reasoning over uncertain data to predict unwanted or perhaps dangerous situations.

Tourist information support is very important due to the fact that a tourist has to make decisions in dynamic and unfamiliar environment. One of the popular types of tourist decision support is recommendations (of attractions to see, events, transportation routes, etc.). However, each of the classical approaches for making recommendations relies heavily on the availability of particular information. This paper proposes a multi-model approach to recommendation systems design in the domain of tourist information support. Specifically, it proposes to construct a recommendation system as a composition of loosely coupled modules, implementing both personalized and non-personalized methods of recommendations and a coordination module responsible for adaptation of the whole system to the specific tourist and situation context. The paper also presents some results on practical evaluation of the proposed models and an integration of the developed recommendation system into a mobile tourist guide (TAIS).

Enterprise Resource Planning (ERP) systems pose usability challenges to all but the most sophisticated of users. One challenge arises from complex menu structures that hinder system navigation. Another issue is the lack of support for discovering and exploring relationships between the data elements that underlie transactions performed with the system. We describe two dynamic, interactive visualizations, the Dynamic Task Map and the Association Map, which were designed to assist users in ERP system navigation and data exploration activities. We present two laboratory studies comparing the use of these visual components to SAP interfaces. Results from an initial empirical evaluation revealed performance gains when using the visual components compared to the default SAP interface. A follow-up study showed users’ overall preference for the visual interface, although no significant user performance differences were detected. User-reported mental effort associated with the visual interface was lower compared to the SAP table-based presentation.

Web-mediated conversations require treating intentions more explicitly. Literature lacks adequate design methods and interactive mechanisms to support users in the sharing of intentions. This research assumes that icons representing emotions play a central role as means for aiding users to convey intentions in communication tasks. This article proposes a method to specify emoticons for representing the users’ intentions, named “intenticons”. The work explores Speech Act Theory and Semiotics in a conceptual framework to structure classes of intentions. We conduct participatory activities to experiment the method with 40 users. The obtained intenticons were evaluated with a different set of users to reveal their effectiveness. The obtained results suggest the feasibility of the method to select and enhance emoticons for intention expression. Evaluations point out that most of the achieved intenticons indicate an acceptable degree of representativeness for the intention classes.

In our era of the service industry, information systems play a major place, even a vital position for businesses, organizations and individuals. Information systems are facing new ongoing security threats, more sophisticated and of different natures. In this context, it is important to prevent attackers from achieving their outcomes, manage the inevitable breaches, and minimize their impacts. Security practices must be conducted in an engineering framework; engineering of security has to be improved. For this, it is proposed to develop innovative and broad systemic approaches that operate together on several axes, by improving user experience. We track and solve Resilience, Security and Usability issues jointly in enterprise information systems. In this paper, we position socio-technical systems according to well-known information systems of enterprises and organizations. We treat the paradigms of socio-technical systems and we focus on the interplay between resilience, security and usability. A case study illustrates the proposed approach; it details the elaboration of design patterns for improving user experience.

To understand what an organization does one must comprehend the business model, which describes the way in which an organization acquires raw materials, transforms them into a product or service that is delivered to a client, and gains money in exchange. In consequence, it is possible to decompose the model into four core processes: supply, transformation, delivery, and monetization, which have both structural and behavioral dependencies among them. Unfortunately, identifying the business model demands an overall view of the business, and most representations focus only on the structural part leaving aside the interactions between core processes. The objective of this paper is twofold. Firstly, it presents a conceptualization and representation for business models that is capable of handling their components and interactions. Secondly, it uses the proposed representation to introduce a catalog of business patterns applicable in the design, portrayal, and analysis of business models. Each pattern includes the basic participants, resources, activities and interactions that must be accounted for in order to perform the core process. When different patterns are joined together, a complete business model can be portrayed.

During the last few years, interest towards Enterprise Architecture (EA) has increased, not least due to anticipated benefits resulting from adopting it. For instance, EA has been argued to provide cost reduction, technology standardisation, process improvement, and strategic differentiation. Despite these benefits, the EA adoption rate and maturity are still low. Consequently, EA benefits are not realised. A major reason hindering the adoption is that EA is not understood correctly. This paper aims for minimising the effect of the lack of understanding EA to adopting EA. Based on the research conducted in Finnish public sector, we propose an improved Enterprise Architecture Adoption Method (EAAM) to overcome the EA adoption challenges. EAAM is built using Design Science approach and evaluated using Delphi method. Some practical guidelines for applying EAAM are also provided to help organisations to overcome EA adoption challenges.

Evaluating the performance of processes is of vital importance if organizations are to seek continuous improvements. It is by measuring processes that data on their performance is provided, thus showing the evolution of the organization in terms of its strategic objectives. These results will serve as the basis for making better decisions, thereby leading to continuous improvement. The approach set out in this paper is prompted by the relative lack of empirical investigations into performance measures contained in the literature and the difficulties that organizations face when trying to verify the results of their business processes. Based on analyzing studies selected in a Systematic Review of the Literature, there it was found the need to propose a new approach to evaluating business processes that brings together elements and recommendations selected from the analyzed approaches. In the evaluation of the proposed approach, a case study is discussed, to verify its applicability.

The use of Ambient-Assisted Living (AAL) systems has been spreading across several countries, with the ultimate purpose of improving the quality of life of patients. These systems often reflect complex architectures including several components such as sensors, gateways, Information Systems or even actuators, as well as messaging and transmitting protocols. Failures in these systems can have severe impact on a monitored patient, and most components foresee some kind of compensation countermeasures to increase reliability. Nevertheless, these measures are often self-contained to a single component and do not address the overall AAL system reliability, disregarding precedent and successor activities and interactions that exist for each time a certain value is registered or a certain alert is triggered. In this paper, we propose a new approach to calculate the overall reliability of an AAL system. We take a Business Process Management (BPM) approach to model the activities and interactions between AAL components, using the Business Process Model and Notation (BPMN) standard. By extending the BPMN standard to include reliability information, we can derive the overall reliability of a certain AAL system. To prove this approach, we also present a reliability study considering scenarios with single and pairwise reliability variations of AAL system components. With this approach, healthcare managers can benefit from important overall reliability information of an AAL system, and better allocate the appropriate resources (including hardware or health care professionals) to improve responsiveness of care to patients.