Abstract
Despite years of research and the long-lasting promise of pervasiveness of an "Internet of Things", it is only recently that a truly convincing number of connected things have been deployed in the wild. New services are now being built on top of these things and allow to realize the IoT vision. However, integration of things in complex and interconnected systems is still only in the hands of their manufacturers and of Cloud providers supporting IoT integration platforms. Several issues associated with data privacy arise from this situation. Not only do users need to trust manufacturers and IoT platforms for handling their data, but integration between heterogeneous platforms is still only incipient. In this position paper, we chart a new IoT architecture, SAFETHINGS, that aims at enabling data privacy by design, and that we believe can serve as the foundation for a more comprehensive IoT integration. The SAFETHINGS architecture is based on two simple but powerful conceptual component families, the cleansers and blenders, that allow data owners to get back the control of IoT data and its processing.

Abstract
This paper presents a framework to support the full life-cycle of extended manufacturing enterprises, from creation to operation and dissolution phases. The deployment and operation of such enterprises can be compared with the concept of 'plug-and-play', as the internal processes and legacy systems of the companies involved are smoothly integrated within an overall business process designed, validated and executed according to a specific business opportunity. During the plug phase, the specific business requirements are elicited and integrated with the design of the extended business processes. On the other hand, in the play phase, those predefined processes are executed in order to run the extended enterprise successfully. The paper describes an application case regarding an engineer-to-order and one-of-a-kind engineering product. This scenario is common to a large number of technology-driven SMEs, and illustrates the value of the framework to exploit business opportunities that require a combination of skills and resources that do not exist in-house. The case shows how the platform addresses the two main challenges in the deployment of an extended enterprise. The first challenge is finding the right set of partners to address a new business opportunity and the design of the underlying collaborative processes. The second challenge is mostly technical, and focuses on the integration of the legacy systems of the partners participating in the network so that cooperation can take place quickly and seamlessly.

Abstract
Olive fruits, as well as their corresponding oil, represent an interesting source of phytochemicals, mainly phenolic compounds, which arise as secondary metabolites, resulting from the plant's response to biotic and abiotic stresses. Therefore, olive fruits from three distinct cultivars ("Cobrancosa," "Galega Vulgar," and "Picual") grown in Portugal and displaying different degree of resistance to biotic and abiotic stresses have been studied in relation to the concentration of total phenolic compounds, orthodiphenols and flavonoids, besides antioxidant capacity (DPPH, ABTS, FRAP, and ORACFL), in three maturation stages and two distinct harvest seasons (2012-2013). Generally, a decrease of all phenolic contents throughout the maturation stages has been observed, while, concerning cultivar, green olives of "Cobrancosa" showed the highest values for all contents assessed, denoting a strong influence of the genetic background. The same trend has not been observed regarding antioxidant activity, since Cobrancosa and Galega Vulgar cultivars presented the highest values only for the DPPH and ORACFL assays. Moreover, multivariate analyses pointed to the preponderance of the cultivars' phenolic composition in the semiripe stage for the resistance to biotic stress, with "Galega" the most susceptible cultivar, presenting the lowest contents at this maturation stage, whereas "Picual" displayed the most pronounced phytochemical response.

Abstract
A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

Abstract
Networks are powerful in representing a wide variety of systems in many fields of study. Networks are composed of smaller substructures (subgraphs) that characterize them and give important information related to their topology and functionality. Therefore, discovering and counting these subgraph patterns is very important towards mining the features of networks. Algorithmically, subgraph counting in a network is a computationally hard problem and the needed execution time grows exponentially as the size of the subgraph or the network increases. The main goal of this paper is to contribute towards subgraph search, by providing an accessible and scalable parallel methodology for counting subgraphs. For that we present a dynamic iterative MapReduce strategy to parallelize algorithms that induce an unbalanced search tree, and apply it in the subgraph counting realm. At the core of our methods lies the g-trie, a state-of-the-art data structure that was created precisely for this task. Our strategy employs an adaptive time threshold and an efficient work-sharing mechanism to dynamically do load balancing between the workers. We evaluate our implementations using Spark on a large set of representative complex networks from different fields. The results obtained are very promising and we achieved a consistent and almost linear speedup up to 32 cores, with an average efficiency close to 80%. To the best of our knowledge this is the fastest and most scalable method for subgraph counting within the MapReduce programming model. Copyright 2017 ACM.

Abstract
The challenge of creating user experiences involves a panoply of multidisciplinary competences in terms of knowing tools, processes and the business itself. This study intends to identify and synthesize the main approaches and principles adopted by web design and e-business companies to create immersive user experiences. In order to achieve this objective, the authors conducted six semistructured interviews with web design and e-business companies. They highlight that companies adopt specific-device design, responsive design and adaptive design approaches. Companies considered fundamental to attend technology life-cycle, heterogeneity of technologies and devices, identification of customer needs, relevance of test phase, scalability of applications, and accessibility and usability issues. Additionally, the customer experience testing is considered a key element to measure user experiences and companies expect that business and technological dimensions will be two key factors with future impact in their business.