Designing like a Pro: The automated composition of workflow activities

To design a workflow process that is efficient, meaningful, and understandable it is important to properly consider how to compose the activities it will consist of. In this paper, guidelines are presented for this exact purpose. These focus on the elementary data-processing steps that are at the core of a workflow process. The guidelines help to determine the relative importance of these data-processing steps as well as their relatedness, such that activities can be composed in a fully automated manner. We implemented this approach in freely available software. A thorough evaluation that incorporates real-life workflow designs indicates that the use of these guidelines leads to activities that closely resemble those designed by experienced modelers. As such, the proposed guidelines provide a proper and automated alternative to what is otherwise a complex and time-consuming task.     

Integrating open innovation and business process innovation: Insights from a large-scale study on a transition economy

Open innovation and business process innovation (BPI) have been investigated by their respective research communities for decades. However, the important relationship between externally focused open innovation and internally implemented BPI is currently unexplored, particularly in transition economies. The main purpose of this study is to contribute toward closing this important research gap. This study describes the main findings of a research study of innovation practices of 224 companies operating in a transition economy.We propose and validate a comprehensive model of integrative innovation and offer some important insights into the relationship between externally focused R&D collaboration and a firm's internal process innovation.     

Wi-Fi fingerprinting based on collaborative confidence level training

Wi-Fi fingerprinting has been a popular indoor positioning technique with the advantage that infrastructures are readily available in most urban areas. However wireless signals are prone to fluctuation and noise, introducing errors in the final positioning result. This paper proposes a new fingerprint training method where a number of users train collaboratively and a confidence factor is generated for each fingerprint. Fingerprinting is carried out where potential fingerprints are extracted based on the confidence factor. Positioning accuracy improves by 40% when the new fingerprinting method is implemented and maximum error is reduced by 35%.     

Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering

There are types of information systems, like those that produce group recommendations or a market segmentation, in which it is necessary to aggregate big amounts of data about a group of users in order to filter the data. Group modeling is the process that combines multiple user models into a single model that represents the knowledge available about the preferences of the users in a group. In group recommendation, group modeling allows a system to derive a group preference for each item. Different strategies lead to completely different group models, so the strategy used to model a group has to be evaluated in the domain in which the group recommender system operates. This paper evaluates group modeling strategies in a group recommendation scenario in which groups are detected by clustering the users. Once users are clustered and groups are formed, different strategies are tested, in order to find the one that allows a group recommender system to get the best accuracy. Experimental results show that the strategy used to build the group models strongly affects the performance of a group recommender system. An interesting property derived by our study is that clustering and group modeling are strongly connected. Indeed, the modeling strategy takes the same role that the centroid has when users are clustered, by producing group preferences that are equally distant from the preferences of every user. This “continuity” among the two tasks is essential in order to build accurate group recommendations.     

Property specification,process verification,and reporting – A case study with vehicle-commissioning processes

Testing in the automotive industry is supposed to guarantee that vehicles are shipped without any flaw. Respective processes are complex, due to the variety of components and electronic devices in modern vehicles. To achieve error-free processes, their formal analysis is required. Specifying and maintaining properties the processes must satisfy in a user-friendly way is a core requirement on any verification system. We have observed that there are few property templates that testing processes must adhere to, and we describe these templates. They depend on the context of the processes, e.g., the components of the vehicle or testing stations. We have developed a framework that instantiates the templates of properties at verification time and then verifies the process against these instances. To allow an automatic verification we develop a transformation of the commissioning process to a Petri net. Using a novel approach, we are able to report the found violations to the user in a user-friendly way. Our empirical evaluation with the industrial partner has shown that our framework does detect property violations in processes. From expert interviews we conclude that our framework is user-friendly and well suited to operate in a real production environment.     

From Business Intelligence to semantic data stream management

The Semantic Web technologies are being increasingly used for exploiting relations between data. In addition, new tendencies of real-time systems, such as social networks, sensors, cameras or weather information, are continuously generating data. This implies that data and links between them are becoming extremely vast. Such huge quantity of data needs to be analyzed, processed, as well as stored if necessary. In this position paper, we will introduce recent work on Real-Time Business Intelligence combined with semantic data stream management. We will present underlying approaches such as continuous queries, data summarization and matching, and stream reasoning.     

The role of big data and cognitive computing in the learning process

In this paper, we investigate how the raise of big data and cognitive computing systems is going to redesign the labor market, also impacting on the learning processes. In this respect, we make reference to higher education and we depict a model of a smart university, which relies on the concepts that are at the basis of the novel smart-cities’ development trends. Thus, we regard education as a process so that we can find specific issues to solve to overcome existing criticisms, and provide some suggestions on how to enhance universities’ performances. We highlight inputs, outputs, and dependencies in a block diagram, and we propose a solution built on a new paradigm called smarter-university, in which knowledge grows rapidly, is easy to share, and is regarded as a common heritage of both teachers and students. Among the others, a paramount consequence is that there is a growing demand for competences and skills that recall the so called T-shape model and we observe that this is pushing the education system to include a blend of disciplines in the curriculums of their courses. In this overview, among the wide variety of recent innovations, we focus our attention on cognitive computing systems and on the exploitation of big data, that we expect to further accelerate the refurbishment process of the key components of the knowledge society and universities as well.     

Influence of group configuration on online discourse writing

This study examines the impact of different strategies for grouping students in online, discourse-intensive distance education courses. The mixed methods research focused on note writing productivity (based on 366 participants in 25 classes) and participants' perceptions (12 graduate students and 10 instructors) relating to three different class configurations (large whole class, small whole class, large class divided into subgroups). Each configuration exhibited advantages and disadvantages in terms of supporting note writing, however, the data analyses suggested that the advantages for writing in subgroups outweighed those of the other two conditions. Splitting larger classes into smaller subgroups appears to reduce information overload and encourages more focused, in-depth discussions. The research concludes with a list of pedagogical recommendations and suggests new software features that may help learning within specific group configurations. This study may have implications for both practitioners and researchers who wish to promote more fruitful online discussions.     

Analyzing water customer service expectations: A case study of the City of Guelph

The present study applies a business model framework to a water utility using a case study. A survey was developed and conducted with residential water customers in the City of Guelph, ON, Canada, with the objective of assessing their expectations in order to improve the business. Preferences differed by user type, underlining the benefit of distinguishing between customer segments (or user types) and customizing messaging. Users were not aware of all services covered by their water bill, and although expecting high quality services they did not support rate increases. Stakeholder collaborations could be used to improve awareness, offer decentralized solutions, and resolve issues collectively.