This work is a clustering point of view about how deduce interesting places considering the point... more This work is a clustering point of view about how deduce interesting places considering the points and its speed in a trajectory context. The author explain a clustering trajectory technique and associates the discovered clusters with the trajectory context. The objective is to find out interesting places related to the person/object that generated the points of the trajectory looking mainly at the physical features of the trajectory, i.e. its speed. The clustering technique is extended to consider the spatio-temporal data instead use only the position (spatial) approach.
Researches concerning Temporal Databases are being developed for more than 20 years. However, ver... more Researches concerning Temporal Databases are being developed for more than 20 years. However, very few implemented systems are available. Several temporal data models were proposed, extending traditional data models in a way to capture also the temporal features. A feasible way of implementing a Temporal Database is using a conventional commercial database mapping the temporal data model to it. This mapping shall provide the explicit representation of the temporal information related to the intended temporal data model. A Temporal Management System is presented in this paper. Data and rules related to a temporal data model are managed by this system, implemented on a conventional DBMS.
Enormous quantities of trajectory data are collected from many sources, as GPS devices and mobile... more Enormous quantities of trajectory data are collected from many sources, as GPS devices and mobile phones, as sequences of points. These data can be used in many application domains such as traffic management, urban planing, tourism, and bird migration. However, in most applications a higher level of abstraction should be used instead of sample points. In this paper we present an extension of the classical open source data mining toolkit Weka to support automatic trajectory data preprocessing in order to mining trajectories in a higher abstraction level. We propose Weka-STPM which is interoperable with all databases constructed under OGC specifications. We tested Weka-STPM with geographic databases and trajectory data stored into Postgresql/PostGIS, which is an open source GDBMS implemented according to OGC standards.
Geographic data preprocessing is the most effort and time consuming step in spatial data mining. ... more Geographic data preprocessing is the most effort and time consuming step in spatial data mining. In order to facilitate geographic data preprocessing and increase the practice of spatial data mining, this paper presents Weka-GDPM, an interoperable module that supports automatic geographic data preprocessing for spatial data mining. GDPM is implemented into Weka, which is a free and open source classical data mining toolkit that has been widely used in academic institutions. GDPM follows the Open GIS specifications to support interoperability with Geographic Information Systems. It automatically generates data at two granularity levels without using prior knowledge and provides support for both distance and topological spatial relationships.
This work is a clustering point of view about how deduce interesting places considering the point... more This work is a clustering point of view about how deduce interesting places considering the points and its speed in a trajectory context. The author explain a clustering trajectory technique and associates the discovered clusters with the trajectory context. The objective is to find out interesting places related to the person/object that generated the points of the trajectory looking mainly at the physical features of the trajectory, i.e. its speed. The clustering technique is extended to consider the spatio-temporal data instead use only the position (spatial) approach.
Researches concerning Temporal Databases are being developed for more than 20 years. However, ver... more Researches concerning Temporal Databases are being developed for more than 20 years. However, very few implemented systems are available. Several temporal data models were proposed, extending traditional data models in a way to capture also the temporal features. A feasible way of implementing a Temporal Database is using a conventional commercial database mapping the temporal data model to it. This mapping shall provide the explicit representation of the temporal information related to the intended temporal data model. A Temporal Management System is presented in this paper. Data and rules related to a temporal data model are managed by this system, implemented on a conventional DBMS.
Enormous quantities of trajectory data are collected from many sources, as GPS devices and mobile... more Enormous quantities of trajectory data are collected from many sources, as GPS devices and mobile phones, as sequences of points. These data can be used in many application domains such as traffic management, urban planing, tourism, and bird migration. However, in most applications a higher level of abstraction should be used instead of sample points. In this paper we present an extension of the classical open source data mining toolkit Weka to support automatic trajectory data preprocessing in order to mining trajectories in a higher abstraction level. We propose Weka-STPM which is interoperable with all databases constructed under OGC specifications. We tested Weka-STPM with geographic databases and trajectory data stored into Postgresql/PostGIS, which is an open source GDBMS implemented according to OGC standards.
Geographic data preprocessing is the most effort and time consuming step in spatial data mining. ... more Geographic data preprocessing is the most effort and time consuming step in spatial data mining. In order to facilitate geographic data preprocessing and increase the practice of spatial data mining, this paper presents Weka-GDPM, an interoperable module that supports automatic geographic data preprocessing for spatial data mining. GDPM is implemented into Weka, which is a free and open source classical data mining toolkit that has been widely used in academic institutions. GDPM follows the Open GIS specifications to support interoperability with Geographic Information Systems. It automatically generates data at two granularity levels without using prior knowledge and provides support for both distance and topological spatial relationships.
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Papers by Andrey Palma