Forthcoming and Online First Articles

International Journal of Spatio-Temporal Data Science

International Journal of Spatio-Temporal Data Science (IJSTDS)

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International Journal of Spatio-Temporal Data Science (2 papers in press)

Regular Issues

  • Business Knowledge Database Structure and Inferencing: An Oracle Prototype   Order a copy of this article
    by Rajeev Kaula 
    Abstract: Business knowledge is a repository of insights and methods that can guide business operations, enable businesses to grasp customer needs and preferences, and provide guidance for growth in the marketplace. Often such knowledge may not be expressed in one format and repository, but instead may be spread in multiple repositories. This paper outlines an approach to structure business knowledge rules in relational database and facilitate their subsequent retrieval through backward chaining and forward chaining using traditional database techniques. The approach is implemented through a prototype that illustrates the creation of knowledge structures through a sample set of business knowledge rules, along with their retrieval through inferencing from an Oracle database.
    Keywords: Business Knowledge; Database; Knowledge Base; Backward Chain; Forward Chain; Oracle; PL/SQL.

  • Station-level bike rental prediction in bike sharing systems   Order a copy of this article
    by Rouzbeh Forouzandeh Jonaghani, Monica Wachowicz 
    Abstract: Predicting ridership is critical for the efficient operation of Bike Sharing Systems (BSSs). This paper proposes a framework for station-level Origin-Destination (OD) flow and bike rental (check-in/out) prediction in a BSS using a two-step Adaptive k-Nearest Neighbor (AkNN) model. While previous works mostly aimed to predict a particular variable in a BSS in isolation (e.g. number of check-ins), in this study, we predict all possible station-to-station flows (i.e., bike flow network) in a BSS. We illustrate how the discontinuities and anomalies in bike ridership are related to the structure of the bike flow network at origin and destination stations and meteorological factors. We also consider the spatial and temporal closeness of the bike ridership in the prediction model. The proposed method is evaluated on the Divvy Trips dataset from April to September 2019 in the City of Chicago. The results show an improvement in the prediction accuracy from baseline methods.
    Keywords: Bike-Sharing System; Evolutionary Network Analysis; Adaptive k-Nearest Neighbor.