Skyline-Based Sorting Approach for Rail Transit Stations Visualization

Abstract

Urban rail transit is an essential part of the urban public transportation system. The reasonable spatial data visualization of urban rail transit stations can provide a more intuitive way for the majority of travelers to arrange travel plans and find destinations. The map service of rail transit stations generated by data visualization has gradually become indispensable information guidance in the rail transit system. The existing map service icons block each other when the scale changes, and new stations cannot be displayed dynamically when users drag the map. This paper uses filtering and sorting methods to dynamically query and visualize the relatively more important transportation stations within the users’ visible range, so as to solve the above problems and provide people with better transportation services. Our method introduces three constraints: spatial diversity, time-sharing passenger flow analysis and whether it is a transit station, and calculates the scores of constraint relationships of feature objects to evaluate stations. On the basis of the skyline query, the scores of feature objects are combined and sorted to obtain an ordered object set of the most interesting k points(top-k POIs), and the rail transit stations are dynamically retrieved and visualized. Before sorting POIs, we filter out POIs that need to be fitted, so that only the k most representative POIs in the currently visible range are displayed. When the map scale changes, the displayed POIs are updated. Finally, through the statistics of efficiency calculation of this method under different scales and centers, combined with users’ evaluations, it was proved that our method could better display critical information and improve user experience.

A semantics-enabled approach for personalised Data Lake exploration

“…In the literature, binary preferences have been studied in recommender systems under a twofold perspective: quantitative [33][34][35], relying upon a scoring function to determine a total order of results; qualitative [13,36,37], using binary relations to express a (strict) partial order of results. In the scope of this paper, we focused on qualitative preferences, yielding a higher expressiveness with respect to quantitative ones.…”

“…Authors in [37] propose a framework implementing a technique that translates SPARQL qualitative preference queries directly into queries that can be evaluated by a relational database management system. Instead, the approach in [34] implements sorting methods to dynamically query and visualise the relatively more important transportation stations within the users' visible range. Therein, a quantitative-based preference approach is used to obtain an ordered objects set, containing the most interesting transportation stations.…”

“…In fact, preferences allow to identify in advance indicators of interest for the user, and only at a later time indicators values are inspected, thus saving cost and resources required to query Data Lake sources. The approach in [34] employs preferences for data exploration, despite users' profiles are not exploited to harness exploration contexts for the different categories of users. Instead, other approaches, even though mentioning the usage of preference-based systems, disregard user-related exploration issues [35,37] and do not focus on multi-dimensional data exploration.…”