Abstract
Nowadays, humanity generates and contributes to form large and complex datasets, going from documents published on media outlets, posts on social media or location-based information. The generated information tends to be complex, heterogeneous (texts, images, videos, etc.) and is growing at an incredible pace, with much of this data having a strong spatial and temporal focus. This steady increase in the availability of such a volume of information, forces the development of more effective user interfaces that would assist users in efficient visualization, analysis and exploration of the data. This half-day workshop on User Interfaces for Spatial and Temporal Data Analysis (UISTDA) held in conjunction with the IUI2018 conference on March 11th, aimed at sharing the latest progress and developments, current challenges and potential applications for exploiting large amounts of spatial and temporal data. In this paper we provide an overview of the workshop goals together with its main contributions. © 2018 Copyright for the individual papers remains with the authors.

Abstract
The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the total tardiness criterion, which is aimed towards the satisfaction of customers in a make-to-order scenario. Although several approximate algorithms have been proposed for this problem in the literature, recent contributions for related problems suggest that there is room for improving the current available algorithms. Thus, our contribution is twofold: First, we propose a fast beam-search-based constructive heuristic that estimates the quality of partial sequences without a complete evaluation of their objective function. Second, using this constructive heuristic as initial solution, eight variations of an iterated-greedy-based algorithm are proposed. A comprehensive computational evaluation is performed to establish the efficiency of our proposals against the existing heuristics and metaheuristics for the problem.

Abstract
Scheduling jobs on a set of identical parallel machines using efficient heuristics when the objective is to minimize total weighted squared tardiness is considered. Two efficient heuristics and an improvement procedure are presented for the problem. These heuristics and other heuristics are tested using problem sets that represent a variety of conditions. The results show that one of the heuristics consistently performs better than the other heuristics tested. It is also shown how these heuristics can be incorporated into other procedures such as the existing Lagrangian relaxation procedure or meta-heuristics to obtain improved solutions for medium sized problems.

Abstract
This paper presents a platform for real and simulated autonomous sailing competitions, which can also be used as a research tool to test and assess navigation algorithms. The platform provides back-end services – competition server, boat modelling and data storage – and supports external browsers and software agents as front-end clients. The back-end adopts the Multi-Agent System (MAS) paradigm for the internal modelling of sailing boats and offers a Web Service Application Programming Interface (API) for the external software agents and a Web application for Web browsers. As a whole, the platform offers tracking (real competitions) and simulation (simulated competitions) modes. The testing and assessment of navigation algorithms and boat models correspond to private simulated competitions. In simulation mode, the back-end internal boat agent implements a simplified physical model, including the weight, sail area, angle of the sail and rudder, velocity and direction of the wind and position and velocity of the hull, whereas the front-end external boat agent implements the navigation algorithm on the team side, ensuring the privacy of strategic knowledge. The Web application allows the configuration and launching of competitions, the registration of teams and researchers, the uploading of boat physical features for simulation as well as the live or playback viewing of real and simulated competitions. The simulation mode is illustrated with the help of a case study. The proposed platform, which is open, scalable, modular and distributed, was designed for the research community to prepare, run and gather data from real and simulated autonomous sailing competitions.

Abstract
The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master-worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances.

Abstract
Sustainability is gaining interest among academics and practitioners due to increased stakeholder awareness of environmental and social issues. However, relatively little research has been conducted on the extent to which firms have integrated social sustainability aspects into the management of their supply chains in emerging economies. The purpose of this article is to explore the social issues pertinent to suppliers and to identify measures and dimensions related to social sustainability in emerging economies. Further, it explores the benefits suppliers and buyers gain by effectively managing such social issues. For this purpose, first, in-depth interviews were conducted with 27 supply chain managers. Further, a survey was conducted in Indian manufacturing industries and co-variance-based structural equation modeling was used to test the hypothesized model. The findings reveal that there are 18 validated supplier social sustainability measures underlying five social dimensions: labor rights, safety and health, societal responsibility, diversity, and product responsibility. The results also suggest a positive relationship between supplier social sustainability practices and supply chain performance mediated by supplier performance. In addition, the role of the buyer's commitment and investment moderates both suppliers and supply chain performance. These results are relevant because they not only identify the social issues plaguing supply chains in emerging economies, but also have practical implications for organizations trying to build socially sustainable supply chains for competitive advantage.