Abstract
E-commerce website owners rely heavily on analysing and summarising the behaviour of costumers, making efforts to influence user actions and optimize success metrics. Machine learning and data mining techniques have been applied in this field, greatly influencing the Internet marketing activities. When faced with a new e-commerce website, the data scientist starts a process of collecting real-time and historical data about it, analysing and transforming this data in order to get a grasp into the website and its users. Data scientists commonly resort to tracking domain-specific events, requiring code modification of the web pages. This paper proposes an alternative approach to retrieve information from a given e-commerce website, collecting data from the site's structure, retrieving semantic information in predefined locations and analysing user's access logs, thus enabling the development of accurate models for predicting users' future behaviour. This is accomplished by the application of a web mining process, comprehending the site's structure, content and usage in a pipeline, resulting in a web graph of the website, complemented with a categorization of each page and the website's archetypical user profiles. 1877-0509 (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

Abstract
Multiple software agent-based solutions have been developed during the last decades, and applied with varying success to different domains offering control, reconfiguration, diagnosis, monitoring, etc. However, the promise that they once posed in terms of a new alternative decentralized approach offering modularity, flexibility and robustness, is only partially fulfilled. This paper investigates some key factors, i.e., design, technology, intelligence/algorithms, standardization, hardware, challenges, application and cost, which are hypothesized to be linked to the Industrial Agent acceptance. Empirical data was acquired via a conducted survey, and statistically analyzed to investigate the support of the posed hypotheses. The results indicate that all the factors are seen important issues that play a role toward deciding for or against an industrial agent solution.

Abstract
Meta-heuristic search methods are used to find near optimal global solutions for difficult optimization problems. These meta-heuristic processes usually require some kind of knowledge to overcome the local optimum locations. One way to achieve diversification is to start the search procedure from a solution already obtained through another method. Since this solution is already validated the algorithm will converge easily to a greater global solution. In this work, several well-known meta-heuristics are used to solve the problem of electricity markets participation portfolio optimization. Their search performance is compared to the performance of a proposed hybrid method (ad-hoc heuristic to generate the initial solution, which is combined with the search method). The addressed problem is the portfolio optimization for energy markets participation, where there are different markets where it is possible to negotiate. In this way the result will be the optimal allocation of electricity in the different markets in order to obtain the maximum return quantified through the objective function.

Abstract
Purpose - The purpose of this paper is to understand value cocreation in service ecosystems from a multilevel perspective, uncovering value cocreation factors and outcomes at the micro, meso, and macro levels. Design/methodology/approach - A Grounded Theory approach based on semi-structured interviews is adopted. The sample design was defined to enable the ecosystem analysis at its different levels. At the macro level was the Portuguese Health Information ecosystem. Embedded meso level units of analysis comprised eight health care organizations. A total of 48 interviews with citizens and health care practitioners were conducted at the micro level. Findings - Study results enable a detailed understanding of the nature and dynamics of value cocreation in service ecosystems from a multilevel perspective. First, value cocreation factors are identified (resource access, resource sharing, resource recombination, resource monitoring, and governance/institutions generation). These factors enable actors to integrate resources in multiple dynamic interactions to cocreate value outcomes, which involve both population well-being and ecosystem viability. Study results show that these value cocreation factors and outcomes differ across levels, but they are also embedded and interdependent. Practical implications - The findings have important implications for organizations that are ecosystem actors (like the Portuguese Ministry of Health) for understanding synergies among value cocreation factors and outcomes at the different levels. This provides orientations to better integrate different actor roles, technology, and information while facilitating ecosystem coordination and co-evolution. Originality/value - This study responds to the need for a multilevel understanding of value cocreation in service ecosystems. It also illuminates how keystone players in the ecosystem should manage their value propositions to promote resource integration for each actor, fostering resource density and ecosystem viability. It also bridges the high-level conceptual perspective of Service-Dominant logic with specific empirical findings in the very important context of health care.

Abstract
Mobile, self-organising robots are seen to be a possible solution to overcome the current limitations of fixed, dedicated automation systems particularly in the area of large structure assembly. Two of the key challenges for traditional dedicated automation systems in large structure assembly are considered to be the transportation of products and the adaptation of manufacturing processes to changes in requirements. In order to make dynamic, self-organising systems a reality, several challenges in the process dynamics and logistical control need to be solved. In this paper, we propose a Multi-Agent System (MAS) approach to self-organise mobile robots in large structure assembly. The model is based on fixed-priority pre-emptive scheduling and uses a blackboard agent as a central information source and to facilitate more common goal directed distributed negotiation and decision making between agents representing the different needs of products and available mobile resources (robots).