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Author: search.filters.author.Elmasri, Ramez

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    A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using sql ecosystem and deep learning
    (2020-01-01)
    Jitkajornwanich, Kulsawasd
    ;
    Pant, Neelabh
    ;
    Fouladgar, Mohammadhani
    ;
    Elmasri, Ramez
    Spatio-temporal data serves as a foundation for most location-based applications nowadays. To handle spatio-temporal data, an appropriate methodology needs to be properly followed, in which space and time dimensions of data must be taken into account ‘altogether’ –unlike spatial (or temporal) data management tools which consider space (or time) separately and assumes no dependency on one another. In this paper, we conducted a survey on spatial, temporal, and spatio-temporal database research. Additionally, to use an original example to illustrate how today’s technologies can be used to handle spatio-temporal data and applications, we categorize the current technologies into two groups: (1) traditional, mainstay tools (e.g. SQL ecosystem) and (2) emerging, data-intensive tools (e.g. deep learning). Specifically, in the first group, we use our spatio-temporal application based on SQL system, ‘hydrological rainstorm analysis’, as an original example showing how analysis and mining tasks can be performed on the conceptual storm stored in a spatio-temporal RDB. In the second group, we use our spatio-temporal application based on deep learning, ‘users’ future locations prediction based on historical trajectory GPS data using hyper optimized ANNs and LSTMs’, as an original example showing how deep learning models can be applied to spatio-temporal data.
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    A Survey of Spatio-Temporal Database Research
    (2018-01-01)
    Pant, Neelabh
    ;
    Fouladgar, Mohammadhani
    ;
    Elmasri, Ramez
    ;
    Jitkajornwanich, Kulsawasd
    The main purpose of spatio-temporal database systems is combining the spatial and temporal features of data. Almost all spatio-temporal applications—such as mobile communication systems, traffic control systems, and GIS with moving objects—have a common basis, which is the requirement to handle both space and time characteristics of the data. Similar to other data types, spatio-temporal data are required to be accurately modeled, structured, and queried efficiently. In this paper, we survey data models, related operations, data structures and access methods for spatial, temporal, and spatio-temporal data types. These access methods basically are enhanced variations of the well-known R-tree.
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    Proving completeness of open GIS SQL spatial relationships and operations
    (2017-10-19)
    Jitkajornwanich, Kulsawasd
    ;
    Elmasri, Ramez
    OpenGIS SQL is a standard for incorporating GIS and spatial concepts into SQL. Two types of methods are part of OpenGIS SQL: (1) boolean methods for topological relationships among spatial objects, and (2) methods for specifying spatial operations. We prove that the complete set of 2-dimensional spatial relationships defined by Egenhofer can all be specified using OpenGIS SQL operations and relationships. Our proof shows how each spatial relationship can be specified using OpenGIS SQL and thus provides users with the means of determining how each spatial relationship can be written in SQL. This provides users with enhanced usability and flexibility by providing the specific OpenGIS conditions for every possible 2- dimensional spatial relationship.
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    Adapting K-means clustering to identify spatial patterns in storms
    (2016-01-01)
    Gupta, Upa
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Elmasri, Ramez
    ;
    Fegaras, Leonidas
    This paper extends our previous work on deriving meaningful storm patterns from very large rainfall data. In an earlier work, we described MapReduce-based algorithms to identify three types of the storms: local, hourly and overall storms. In general, local storms have temporal characteristics of the storms at a particular site, hourly storms have spatial characteristics of the storms at a particular hour and overall storms have both spatial and temporal characteristics of the storm. We aim to find meaningful patterns and predict trajectories in the spatio-temporal data (i.e. overall storms which are sets of geographically overlapping, consecutive hourly storms). In this paper, we adapt K-Means clustering to find different types of hourly storms based on their shapes and sizes. Since the rainfall data are typically larger than the memory capacity of a single computer, we have implemented this clustering algorithm in Apache Spark, which is a distributed data processing framework, and have run our experiments on a computer cluster.
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