Abstract
The 2nd workshop on User Interfaces for Spatial-Temporal Data Analysis (UISTDA2019)(1) took place in conjunction with the 24th Annual Meeting of the Intelligent Interfaces community (ACM IUI2019) in Los Angeles, USA on March 20, 2019. The goal of this workshop is to share latest progress and developments, current challenges and potential applications for exploring and exploiting large amounts of spatial and temporal data. Four papers and a keynote talk were presented in this edition of the workshop.

Abstract
This paper considers the problem of scheduling jobs in a permutation flow shop with the objective of minimizing total earliness and tardiness. Unforced idle time is considered in order to reduce the earliness of jobs. It is shown how unforced idle time can be inserted on the final machine. Several dispatching heuristics that have been used for the problem without unforced idle time were modified and tested. Several procedures were also developed that conduct a second pass to develop a sequence using dispatching rules. These procedures were also tested and were found to result in better solutions.

Abstract
The two-machine permutation flow shop scheduling problem with the objective of minimizing total earliness and tardiness is addressed. Unforced idle time can be used to complete jobs closer to their due dates. It is shown that unforced idle time only needs to be considered on the second machine. This result is then used to extend a lower bound and dominance conditions for the single-machine problem to the two-machine permutation flow shop problem. Two branch-and-bound algorithms are developed for the problem utilizing the lower bound and dominance conditions. The algorithms are tested using instances that represent a wide variety of conditions.

Abstract
Information and Communication Technologies (ICT) have revolutionised the tourism domain, providing a wide set of new services for tourists and tourism businesses. Both tourists and tourism businesses use dedicated tourism platforms to search and share information generating, constantly, new tourism crowdsourced data. This crowdsourced information has a huge influence in tourist decisions. In this context, the paper proposes a stream recommendation engine supported by crowdsourced information, adopting Stochastic Gradient Descent (SGD) matrix factorisation algorithm for rating prediction. Additionally, we explore different (i) profiling approaches (hotel-based and theme-based) using hotel multi-criteria ratings, location, value for money (VfM) and sentiment value (StV); and (ii) post-recommendation filters based on hotel location, VfM and StV. The main contribution focusses on the application of post-recommendation filters to the prediction of hotel guest ratings with both hotel and theme multi-criteria rating profiles, using crowdsourced data streams. The results show considerable accuracy and classification improvement with both hotel-based and theme-based multi-criteria profiling together with location and StV post-recommendation filtering. While the most promising results occur with the hotel-based version, the best theme-based version shows a remarkable memory conciseness when compared with its hotel-based counterpart. This makes this theme-based approach particularly appropriate for data streams. The abstract completely needs to be rewritten. It does not provide a clear view of the problem and its solutions the researchers proposed. In addition, it should cover five main elements, introduction, problem statement, methodology, contributions and results. Done.

Abstract
Wiki-based crowdsourced repositories have increasingly become an important source of information for users in multiple domains. However, as the amount of wiki-based data increases, so does the information overloading for users. Wikis, and in general crowdsourcing platforms, raise trustability questions since they do not generally store user background data, making the recommendation of pages particularly hard to rely on. In this context, this work explores scalable multi-criteria profiling using side information to model the publishers and pages of wiki-based crowdsourced platforms. Based on streams of publisher-page-review triads, we have modelled publishers and pages in terms of quality and popularity using different criteria and user-page-view events collected via a wiki platform. Our modelling approach classifies statistically, both page-review (quality) and pageview (popularity) events, attributing an appropriate rating. The quality-related information is then merged employing Multiple Linear Regression as well as a weighted average. Based on the quality and popularity, the resulting page profiles are then used to address the problem of recommending the most interesting wiki pages per destination to viewers. This paper also explores the parallelisation of profiling and recommendation algorithms using wiki-based crowdsourced distributed data repositories as data streams via incremental updating. The proposed method has been successfully evaluated using Wikivoyage, a tourism crowdsourced wiki-based repository.

Abstract
Nowadays, with the widely usage of on-line stream video platforms, the number of media resources available and the volume of crowd-sourced feedback volunteered by viewers is increasing exponentially. In this scenario, the adoption of recommendation systems allows platforms to match viewers with resources. However, due to the sheer size of the data and the pace of the arriving data, there is the need to adopt stream mining algorithms to build and maintain models of the viewer preferences as well as to make timely personalised recommendations. In this paper, we propose the adoption of optimal individual hyper-parameters to build more accurate dynamic viewer models. First, we use a grid search algorithm to identify the optimal individual hyper-parameters (IHP) and, then, use these hyper-parameters to update incrementally the user model. This technique is based on an incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations. © 2019 for this paper by its authors.