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IT Project Portfolio Management has been implemented in most organizations to effectively manage complex portfolios of IT projects and balance them with business strategy. Several standards for portfolio management have been published, but the scientific literature still lacks a theoretically grounded and practically validated reference model for analyzing the implementation of IT Project Portfolio Management in an organization. Therefore, this study designs and validates a reference model for systematically analyzing IT Project Portfolio Management design choices in an organization in terms of processes, roles, responsibilities, and authority. Organizations can use the reference model to systematically assess their local implementation of IT Project Portfolio Management and identify areas for improvement.

Aha moment is when users realize the value of using the software product, which is a key to driving revenue, mainly for B2B SaaS vendors. According to the Acquisition-Activation-Retention-Referral-Revenue (AARRR) model, the aha moment can refer to activation, and the following customer phase is retention. This research aims to find customers’ ”aha moment” through process mining. Since the customers in retention are obligated to experience activation before, the research first identifies milestone actions in terms of product features and user roles to cluster the retention customers. Evaluation is performed through the data of the clustered customers from the time before they move to the retention phase. The event log analysis is discussed based on multiple dimensions: product solution, time, and user roles. The research mainly applies the process mining technique, heuristic miner, to discover the customer’s behavior patterns. Apart from marketing funnels, Moreover concept of human-computer interaction is focused on event classification and data cleaning, which is practical for cleaning UI logs. The discovered processes and aha moment can guide future product development and value proposition re-stratigizing.

Enterprises always do their business within some restrictions. In a team of enterprise architects, the restrictions are transformed into the modelling conventions and the corresponding modelling constraints that should be consistently applied across all enterprise models. This paper presents an approach for refining and formali\-zing modeling conventions into modelling constraints and using them for assessment of enterprise models by a software component called ArchiChecker. The specifics of the proposed approach is that the modeling conventions are first visualized and formalized using the types of elements and relationships of the ArchiMate modeling language, that is also used for modelling of enterprise views. The ArchiMate elements and relationships serve as types to formulate constraints. The elements and relationships in an ArchiMate model are instances of the ArchiMate elements and relationships. Using these types and instances the ArchiChecker automatically generates the lists of violations of modeling conventions in the enterprise models. Each violation shows how a specific enterprise view deviates from a given modeling convention. The paper reports a case study of application of the proposed approach to enterprise modelling views and modelling conventions used in a medical center. The case study is used to discuss the added value of formalization and automated assessment of modelling constraints in enterprise modelling.

CRM platforms heavily depend on high-quality data, where poor-quality data can negatively influence its adoption. Additionally, these platforms are increasingly interconnected and complex to meet growing needs of customers. Hence, delivery and consultancy of CRM platforms becomes highly complex. In this study, we propose a CRM data quality management framework that supports CRM delivery and consultancy firms to improve data quality management practices within their projects. The framework should also improve data quality within CRM solutions for their clients. We extract best practices for CRM data quality management by means of a literature study on data quality definition and measurement, data quality challenges, and data quality management methods. In a case study at an IT consultancy company, we investigate how CRM delivery and consultancy projects can benefit from the incorporation of data quality management practices. The design of the framework is validated by means of confirmatory focus groups and a questionnaire. The results translate into a framework that provides a high-level overview of data quality management practices incorporated in CRM delivery and consultancy projects. It includes the following components: Client profiling, project definition, preparation, migration/integration, data quality definition, assessment, and improvement.

Process mining derives knowledge of the execution of processes through analyzing behavior as observed from real-life events. While benefits of process mining are widely acknowledged, finding an adequate level of detail at which a mined process model is suitable for a specific stakeholder is still an ongoing challenge. Process models can be mined at different levels of abstraction, often resulting in either highly complex or highly abstract process models. This may have an important impact on the comprehensibility of the process model, which can also differ from the perspective of a particular stakeholder. To address this problem from a stakeholder-centric perspective, we propose a methodology for determining an appropriate level of process model abstraction. To this end, we use quantitative metrics on process models as well as a qualitative evaluation by using a technology acceptance model (TAM). A logistics case study involving the fuzzy process mining discovery algorithm shows init ial evidence that the use of appropriate abstraction levels is key when considering the needs of various stakeholders.

Entity resolution in databases focuses on detecting and merging entities that refer to the same real-world object. Collective resolution is among the most prominent mechanisms suggested to address this challenge since the resolution decisions are not made independently, but are based on the available relationships within the data. In this paper, we introduce a novel resolution approach that combines the essence of collective resolution with rules and transformations among entity attributes and values. We illustrate how the approach’s parameters are optimized based on a global optimization algorithm, i.e., simulated annealing, and explain how this optimization is performed using a small training set. The quality of the approach is verified through an extensive experimental evaluation with 40M real-world scientific entities from the Patstat database.

Chaos emerges with the ever growing amounts of data and information within organisations. But it is problematic to manage these valuable assets and also remain accountable and compliant for them because there is no agreement about even their definitions. Our objective is to propose a coherent set of definitions for data governance and information governance within and across organisations in relation with data and information as underlying concepts. As a research method, we explore elements from existing definitions in literature about the Data-Information-Knowledge-Wisdom pyramid and about data governance and information governance. Classification of these elements and coding them in concepts during discussions among peers resulted in a new vocabulary. This forms the basis for formulation and design of an original coherent set of definitions for data, information, meaning, data governance and information governance. This research is grounded, goal oriented and uses multiple accepted literature review methods. But it is limited to the literature found and the IS domain.

Organizations around the world today face many challenges, and not all of them are unique. Such repeatable problems can themselves be solved by reusable solutions. This study focuses on Enterprise Architecture Patterns, reusable solutions to repeating challenges organizations face. Through a systematic literature review, we aim to gather an exhaustive recollection of all such patterns in literature. In order to be as comprehensive as possible, the sample included related fields also researching patterns. The result is a collection of 593 patterns and their respective analysis, which will help practitioners tackle their challenges.

We propose in this paper a conceptual framework for the hierarchical decomposition of Non-Functional Requirements (NFRs) from the business process level to the web service level. This framework seeks to reduce the dependence on a particular IT expert’s knowledge by simplifying the dialog between the business and IT areas. The proposed framework relies on a structure of NFRs interdependence. The main reference was the ISO/IEC 25010 Product Quality Model, extended by additional software quality models and particular QoS attributes. This framework is accompanied by an extensive dictionary of non-functional requirements for both business processes and web services that can serve as a reference for researchers and industry practitioners. We assume that orchestrating web services to run business processes requires a rigorous definition of the functional requirements and NFRs of these web services. Web service NFRs are often defined as Quality of Service (QoS) attributes, which is done at the implementation level by IT teams. The definition of QoS attributes should consider the business process NFRs, since misinterpretations of web service NFRs may affect the behavior of the web services and hence achieving the business goals. The approaches proposed so far in the literature are still heavily dependent on an IT expert’s knowledge to identify the appropriate QoS attributes required to meet particular business process NFRs. However, defining appropriate QoS attributes without reference to business process-level NFRs may be a costly, time-consuming task.

Business monitoring approaches usually address indicators associated with processes only at the service level; i.e., related to the services implementing the processes. Monitoring at the service level raises technical measures geared to Information Technology (IT) managers. Monitoring of Key Performance Indicators (KPIs) is usually carried out at a higher level, but transversely to the organization’s processes, i.e., uncoupled from the processes. We present a component designed to aid in strategic alignment between business and IT by monitoring Non-Functional Requirements (NFR) of processes based on Quality of Service attributes. This component aims to allow business managers to monitor process executions by focusing on the indicators that truly respond to the execution of such processes. We evaluated the component via a proof of concept.

Non-functional Requirements (NFRs) of web services are defined by IT teams at the implementation level often as Quality of Service (QoS) attributes. Orchestrating web services to run business processes requires a rigorous definition of the NFRs of such web services. The definition of QoS attributes should consider the business process NFRs since misinterpretations of web service NFRs may affect the behavior of the web services and hence achieving the business goals. The approaches proposed so far are still heavily dependent on an IT expert’s knowledge to identify the appropriate QoS attributes required to meet particular business process NFRs. Defining appropriate QoS attributes without reference to business process-level NFRs may be a costly, time-consuming task. We propose a conceptual framework for the hierarchical decomposition of NFRs from the business process level to the web service level. This framework seeks to reduce the dependence on a particular IT expert’s knowledge by simplifying the dialog between the business and IT areas. The proposed framework relies on a structure of NFRs interdependence. The main reference was the ISO/IEC 25010 Product Quality Model, extended by additional software quality models and particular QoS attributes.

As data analysis techniques progress, the focus shifts from simple tabular data to more complex data at the level of business objects. Therefore, the evaluation of such data analysis techniques is far from trivial. However, due to confidentiality, most researchers are facing problems collecting available real data to evaluate their techniques. One alternative approach is to use synthetic data instead of real data, which leads to unconvincing results. In this paper, we propose a framework to automatically operate information systems (supporting operational processes) to generate semi-real data (i.e., “operations related data” exclusive of images, sound, video, etc.). This data have the same structure as the real data and are more realistic than traditional simulated data. A plugin is implemented to realize the framework for automatic data generation.

This paper aims to map issues (risks and problems) at the interface of agile and traditional development approaches in hybrid organizations which have an impact on coordination and cooperation. Successfully combining agile and traditional development methods appears to be quite a challenge for many hybrid organizations. Both methods have their own strengths and added value but also bring their own culture and conditions. Combining these can lead to problems. If we want to handle such problems, we first need to understand the issues that can cause such problems. This study is aimed at identifying and validating an overview of these issues. Based on an exploration of literature a preliminary overview of issues was derived. These were classified into a coherent set. The result was validated in a case study within a large financial institute in the Netherlands. The resulting list of twenty-four issues can be used as a starting point for handling the problem area.

Software developing organizations nowadays have a wide choice when it comes to sourcing software components. This choice ranges from developing or adapting in-house developed components via buying closed source components to utilizing open source components. This study aims at structured decision support in this type of decision. As a basis for this study an initial set of criteria is taken, that has been identified and validated in a particular software development environment in a previous study on the subject (Kusters et al, 2016). In the paper at hand we report on the results of the application and validation of the initial set of sourcing criteria in a completely different case study environment, namely a software environment in that. medical embedded software is being developed. In addition, and based on the outcomes of our case study, a further step is made towards structured decision support for sourcing decisions by the development of an initial sourcing criteria framework.

The Scaled Agile Framework (SAFe) has emerged over the last years as an approach which supports the improvement of software and systems development. SAFe claimes solutions for business challenges, such as shortening cycle's times, improving product quality, increasing team members' satisfaction, and involving the customer in product development. However, regarding customer involvement, there is limited research, both in SAFe and in real-life agile software development projects. In previous work we developed an initial conceptual customer involvement model for the SAFe domain in Philips Medical Systems. In this paper this initial model will be extended and enriched on the basis of a case study in an industrial company.

In this paper, we discuss how to apply an autoencoder to detect anomalies in payment data derived from an Real-Time Gross Settlement system. Moreover, we introduce a drill-down procedure to measure the extent to which the inflow or outflow of a particular bank explains an anomaly. Experimental results on real-world payment data show that our method can detect the liquidity problems of a bank when it was subject to a bank run with reasonable accuracy.

Most of the Brazilian universities employ teaching-learning methodologies based on classic frontal lectures. The Medicine Programme of the Federal University of S˜ao Carlos (UFSCar) is an exception, since it employs active learning methodologies. The Educational and Academic Management System for Courses Based on Active Learning Methodologies (EAMS-CBALM) was built and it is currently used to support this programme, and has been made available for other programmes as well. This system was developed using Scrum, but during its development project it was often necessary to reconsider system behaviour scenarios, and consequently the product backlog items, mainly due to poor communication between the Product Owner (PO) and the development team. This paper discusses a case study in which Behaviour-Driven Development (BDD) has been used in combination with Scrum to redesign some EAMS-CBALM components. The paper demonstrates that the communication between the PO and the development team can be improved by using BDD as a communication platform to unambiguously define system requirements and automatically generate test suites.

In this paper, we discuss how to apply an autoencoder to detect anomalies in payment data derived from an Real-Time Gross Settlement system. Moreover, we introduce a drill-down procedure to measure the extent to which the inflow or outflow of a particular bank explains an anomaly. Experimental results on real-world payment data show that our method can detect the liquidity problems of a bank when it was subject to a bank run with reasonable accuracy.