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Moving objects databases (MOD) have been receiving increasing attention from the database community in recent years, mainly due to the wide variety of applications that technology allows nowadays. Trajectories of moving objects like cars or pedestrians, can be reconstructed by means of samples describing the locations of these objects at certain po...
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Context 1
... di- mension is composed of a set of hierarchies, each one de- scribing a set of geometries in a thematic layer. Figure 2 shows a GIS dimension schema (there is also the Time dimension, which we will comment later), with three hi- erarchies, located in three different layers, following our running example: rivers (L r ), schools (L s ), and neigh- borhoods (L n ) (other layers are represented analogously). We define three sectors, denoted the Algebraic part, the Geometric part, and the Classical OLAP or Application part. ...
Context 2
... Time Dimension Besides the static information rep- resenting geometric components (i.e., the GIS), there will be a Time dimension (actually, there could be more than one Time dimensions, supporting, for example, different notions of time). Figure 2 shows a configuration of a Time dimension following the standard OLAP convention. Note that, of course, the application part could contain the time dimension, but, since it is essential for addressing moving objects, we believe that we must consider it as a special kind of dimension. ...
Context 3
... that, of course, the application part could contain the time dimension, but, since it is essential for addressing moving objects, we believe that we must consider it as a special kind of dimension. Figure 2, the level polygon in layer L n is associated with two application-dependent categories, neighborhood and city, such that neighborhood → city ("A → B" means that there is a rollup function from level A to level B in the application part). Each category may even have attributes associated, like population, number of schools, and so on. ...
Citations
... Nowadays, thanks to the ubiquity of telecommunication and sensor technologies, such data are now available in the form of GPS trajectories and mobile phone user data at decreasing cost. Movements are continually recorded as trajectories, which are sequences of geo-located and time-stamped points, often with associated information (Kuijpers and Vaisman 2007). GPS, mobile phone service and location-based app data are typical examples of these new datasets. ...
Datasets collecting the ever-changing position of moving individuals are usually big and possess high spatial and temporal resolution to reveal activity patterns of individuals in greater detail. Information about human mobility, such as ‘when, where and why people travel’, is contained in these datasets and is necessary for urban planning and public policy making. Nevertheless, how to segregate the users into groups with different movement and behaviours and generalise the patterns of groups are still challenging. To address this, this article develops a theoretical framework for uncovering space-time activity patterns from individual’s movement trajectory data and segregating users into subgroups according to these patterns. In this framework, individuals’ activities are modelled as their visits to spatio-temporal region of interests (ST-ROIs) by incorporating both the time and places the activities take place. An individual’s behaviour is defined as his/her profile of time allocation on the ST-ROIs she/he visited. A hierarchical approach is adopted to segregate individuals into subgroups based upon the similarity of these individuals’ profiles. The proposed framework is tested in the analysis of the behaviours of London foot patrol police officers based on their GPS trajectories provided by the Metropolitan Police.
... The analysis of moving object databases is a field of research that has received significant attention in recent years (Anwar Hossain and Bazlur Rashid, 2012;Vaisman, 2012;Sistla et al., 1997;Wolfson et al., 1998;Güting and Schneider, 2005;Benetis et al., 2006;Pedersen, 2006, 2009;Kuijpers and Vaisman, 2007;Leonardi et al., 2010;Gómez et al., 2008). Typical applications of this discipline are location-based services (Saltenis and Jensen, 2002;Yim et al., 2011), traffic control (Papadias et al., 2001), transport logistics (Ding and Guting, 2004), wild life tracking (Laube and Imfeld, 2002;Li et al., 2011), and epidemiology (Sinha and Mark, 2005). ...
In this paper, we present an OLAP framework for moving object data. We introduce a new operator GROUP_TRAJECTORIES for group-by operations on moving object data and present two implementation alternatives for computing groups of moving objects for group-by aggregation: group by overlap and group by intersection. We also present an interactive OLAP environment for resolution drill-down/roll-up on sets of trajectories and parameter browsing. We evaluate the performance of our GROUP_TRAJECTORIES operator by using generated as well as real life moving object datasets.
... Na análise orientada a trajetórias o objetivo é analisar o deslocamento dos objetos móveis entre as regiões em termos de origem-destino do movimento. Os trabalhos de Baltzer et al. (2008), Gomez et al. (2008), Kuijpers e Vaisman (2007), e Spaccapietra et al. (2008) consideram esse tipo de análise. Dependendo do modelo adotado, os dois tipos de análise estão disponíveis. ...
... Data Warehouses Espaciais [Bédard et al., 2001] são empregados por Kuijpers e Vaisman (2007), e Gomez et al. (2008) para analisar trajetórias. Segundo seus autores, o uso de medidas e dimensões espaciais aumenta o poder de expressividade do modelo, além de simplificar a construção e processamento de algumas consultas. ...
... Em geral, os trabalhos na literatura permitem apenas análise orientada a tráfego, e alguns destes conseguem resolver bem o problema da grande quantidade dos dados de trajetória, como Marketos et al. (2008) e Orlando et al. (2007. Entretanto, poucos trabalhos proporcionam análise orientada a trajetórias, sendo que estes não permitem analisar a direção dos movimentos no estilo OLAP, tais como Baltzer et al. (2008), Gomez et al. (2008), Kuijpers e Vaisman (2007), e Spaccapietra et al. (2008). Dos trabalhos analisados, apenas Gomez, et al. (2007) e Spaccapietra et al. (2008) distingue paradas de movimentos em trajetórias, o que é fundamental para a análise correta de trajetórias. ...
Este trabalho propõe um modelo conceitual para Data Warehouse de Trajetórias que permite analisar o comportamento dos objetos móveis sobre e entre regiões no espaço e no tempo, de acordo com diferentes níveis de granularidade, através do uso de agregações. O modelo permite a segmentação de trajetórias em componentes, tais como paradas e movimentos. Estes componentes podem transportar informações semânticas que dão significado a partes da trajetória. Para amenizar o problema da grande quantidade de dados, as trajetórias são armazenadas de forma compactada, sumarizando-se suas paradas e movimentos. Experimentos foram realizados para avaliar o nível de compactação obtido para esses dados.
... Enquanto a análise orientada a trajetórias tem como objetivo analisar o deslocamento dos objetos móveis entre as regiões em termos de origem-destino do movimento. Ela é adotada por Baltzer et al. (2008), Gomez et al. (2008), Kuijpers e Vaisman (2007), e Spaccapietra et al. (2008). Dependendo do modelo adotado, ambos os tipos de análise estão disponíveis. ...
... Data Warehouses Espaciais [Bédard et al., 2001] são empregados por Kuijpers e Vaisman (2007), e Gomez et al. (2008) para analisar trajetórias. Segundo os autores, o uso de medidas e dimensões espaciais aumenta o poder de expressividade do modelo, além de simplificar a construção e processamento de algumas consultas. ...
... Em geral, os trabalhos na literatura proporcionam apenas análise orientada a tráfego, e alguns destes conseguem resolver o problema da grande quantidade dos dados de trajetória, como Braz et al. (2007), Marketos et al. (2008. Entretanto, poucos trabalhos proporcionam análise orientada a trajetórias, tais como Baltzer et al. (2008), Gomez et al. (2008), Kuijpers e Vaisman (2007), e Spaccapietra et al. (2008), sendo que nenhum deles permitem analisar a direção dos movimentos das trajetórias no estilo OLAP. Além disso, dos trabalhos analisados, apenas Gomez et al. (2007) e Spaccapietra et al. (2008) distinguem paradas de movimentos, o que é fundamental para a análise correta de trajetórias, como será visto na Seção 4. ...
This work proposes a conceptual model for Trajectory Data Ware-houses that allows the analysis of moving objects over and between regions in different levels of granularity. The model enables the segmentation of trajecto-ries into components, such as stop and movement, carrying semantic informa-tion that assign meaning to the trajectory. To reduce the amount of data, the trajectories are stored compactly through the summarization of stops and movements. Resumo. Neste trabalho é proposto um modelo conceitual para Data Ware-house de Trajetórias que permite analisar o comportamento dos objetos mó-veis sobre e entre regiões sobre diferentes níveis de granularidade. O modelo permite a segmentação de trajetórias em componentes, tais como parada e movimento, os quais podem transportar informações semânticas que dão sig-nificado à trajetória. Para amenizar o problema da grande quantidade de da-dos, as trajetórias são armazenadas de forma compactada através da sumari-zação de suas paradas e movimentos.
... Na análise orientada a trajetórias o objetivo é analisar o deslocamento dos objetos móveis entre as regiões em termos de origem-destino do movimento. Os trabalhos de Baltzer et al. (2008), Gomez et al. (2008), Kuijpers e Vaisman (2007), e Spaccapietra et al. (2008) consideram esse tipo de análise. Dependendo do modelo adotado, os dois tipos de análise estão disponíveis. ...
... Data Warehouses Espaciais [Bédard et al., 2001] são empregados por Kuijpers e Vaisman (2007), e Gomez et al. (2008) para analisar trajetórias. Segundo seus autores, o uso de medidas e dimensões espaciais aumenta o poder de expressividade do modelo, além de simplificar a construção e processamento de algumas consultas. ...
... Em geral, os trabalhos na literatura permitem apenas análise orientada a tráfego, e alguns destes conseguem resolver bem o problema da grande quantidade dos dados de trajetória, como Marketos et al. (2008) e Orlando et al. (2007. Entretanto, poucos trabalhos proporcionam análise orientada a trajetórias, sendo que estes não permitem analisar a direção dos movimentos no estilo OLAP, tais como Baltzer et al. (2008), Gomez et al. (2008), Kuijpers e Vaisman (2007), e Spaccapietra et al. (2008). Dos trabalhos analisados, apenas Gomez, et al. (2007) e Spaccapietra et al. (2008) distingue paradas de movimentos em trajetórias, o que é fundamental para a análise correta de trajetórias. ...
This work proposes a conceptual model for Trajectory Data Ware-houses that allows analyzing the behavior of moving objects under and be-tween regions in space and time, according to different levels of granularity, through the use of aggregations. The model enables the segmentation of tra-jectories into components such as stops and movements. These components can transport semantic information that assign meaning to parts of the trajec-tory. To reduce the amount of data, the trajectories are stored compactly, summarizing their stops and movements. Experiments were performed to evaluate the level of compaction obtained in the data. Resumo. Este trabalho propõe um modelo conceitual para Data Warehouse de Trajetórias que permite analisar o comportamento dos objetos móveis so-bre e entre regiões no espaço e no tempo, de acordo com diferentes níveis de granularidade, através do uso de agregações. O modelo permite a segmenta-ção de trajetórias em componentes, tais como paradas e movimentos. Estes componentes podem transportar informações semânticas que dão significado a partes da trajetória. Para amenizar o problema da grande quantidade de dados, as trajetórias são armazenadas de forma compactada, sumarizando-se suas paradas e movimentos. Experimentos foram realizados para avaliar o ní-vel de compactação obtido para esses dados.
Revista Brasileira de Sistemas de Informação (iSys), Vol. 4
... Line Analytical Processing)과 GIS를 통합 하려는 연구노력이 필요하다[16]. ...
Due to the development of information technology and business related to geographical location of customer, the need for the storage and analysis of geographical location data is increasing rapidly. Geographical location data have a spatio-temporal nature which is different from typical business data. Therefore, different methods of data storage and analysis are required. This paper proposes a multi-dimensional data model and data visualization to analyze geographical location data efficiently and effectively. Purchase order data of an online farm products brokerage business was used to build prototype datamart. RFM scores are calculated to classify customers and geocoding technology is applied to display information on maps, thereby to enhance data visualization.
... Our approach for spatial aggregation is described in [8] and its implementation discussed in [4] 2 . Kuijpers and Vaisman [15] presented a taxonomy of aggregate queries on moving object data. The model and query language we present here covers the different types of aggregation queries in this taxonomy. ...
... Different types of aggregation can be added to the language. The list below, although not complete, covers the most interesting and usual cases (see [15] for an extensive list of examples of moving object aggregation queries). ...
The study of moving objects has been capturing the attention of Geo- graphic Information System (GIS) researchers. Moving objects, carrying location- aware devices, produce trajectory data in the form of a sample of (Oid,t,x,y)-tuples, that contain object identifier and time-space information. Recently, the notion of stops and moves was introduced. Intuitively, if a moving object spends a sufficient amount of time in a certain geographic place (which we denote a place of interest of an application), this place is considered a stop of the object's trajectory. In-between stops, a trajectory has moves. In this paper we study how moving object data analy- sis can benefit from replacing raw trajectory data by a sequence of stops and moves. We first propose a formal model and query language (denoted Lmo) to express com- plex queries involving spatial data stored in a GIS, non-spatial data (stored in a data warehouse) and moving object data. This query language also supports different forms of aggregation. We then study the compression of trajectory data produced by moving objects, using the concepts of stops and moves. We show that stops and moves are expressible in Lmo and that there exists a fragment of this language (that can be expressed by means of regular expressions) allowing to talk about tempo- rally ordered sequences of stops and moves. We use this fragment to perform data mining over trajectory data. We present an implementation and a case study, and discuss different applications of our approach.
... We commented above the work of Papadias et al (2002), about indexing of historical aggregate information about moving objects. Kuijpers and Vaisman (2007) presented a taxonomy of aggregate queries on moving object data. The model and query language we present here covers the different types of aggregation queries in this taxonomy. ...
Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community. However, not much has been done in providing moving object databases with OLAP functionality. In the first part of this article we survey the SOLAP literature. We then move to Spatio-Temporal OLAP, in particular addressing the problem of trajectory analysis. We finally provide an in-depth comparative analysis between two proposals introduced in the context of the GeoPKDD EU project: the Hermes-MDC system, and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina.
... ST 2 ODMGe assumes and extends previous work on computing efficiently historical aggregates for the On-Line Analytical Processing (OLAP) of spatio-temporal data streams [16, 13, 12, 11]. Zhang et al. [16] defined a spatio-temporal extension of the SB-Tree [15] structure, that, like our previous work [6], proposes an aggregated indexing approach whereby older data are stored using coarser granularities than recent data. ...
... Tao and Papadias [13], relying on a seminal work on aggregate R * -trees [10], presented over the years several indexing structures for the efficient historical aggregation of spatio-temporal data. Recent work focuses on the issues of aggregates on moving objects trajectories [12, 11]. Unlike those approaches, ST 2 ODMGe supports different time granularities and multiple levels of aggregation and refinement, that is, different indexing forms; moreover the appropriate level can be selected on a per-attribute basis thus supporting different semantics (i.e., different queries). ...
In applications involving spatio-temporal modelling, granularities of data may have to adapt according to the evolving semantics and significance of data. In this paper we define ST
2_ODMGe, a multigranular spatio-temporal model supporting evolutions, which encompass the dynamic adaptation of attribute granularities, and the deletion of attribute values. Evolutions are specified as Event - Condition - Action rules and are executed at run-time. The event, the condition, and the action may refer to a period of time and a geographical area. The evolution may also be constrained by the attribute values. The ability of dynamically evolving the object attributes results in a more flexible management of multigranular spatio-temporal data but it requires revisiting the notion of object consistency with respect to class definitions and access to multigranular object values. Both issues are formally investigated in the paper.
... Efficient evaluation if OLAP queries requires, more often than not, the use of precalculation tech- niques [4]. Our proposal is aimed at integrating these two different worlds in a single framework [2, 9], allowing, for instance, to evaluate queries like " total income in provinces crossed by at least one river " , where income information is stored in the data warehouse and provinces and rivers information is stored in a GIS. Moreover, the results will be navigated in the usual OLAP way, through operations like roll-up and drill-down. ...
... The implementation we present in this work is based in the data model introduced in [2, 9] . There, the authors proposed model where GIS, OLAP, and moving objects data, are integrated in a single framework. ...
Data aggregation in Geographic Information Systems (GIS) is a desirable feature, although only marginally present in commercial systems, which also fail to provide integration between GIS and OLAP (On Line Analytical Processing). With this in mind, we have developed Piet, a system that makes use of a novel query processing technique: first, a process called sub-polygonization decomposes each thematic layer in a GIS, into open convex polygons; then, another process computes and stores in a database the overlay of those layers for later use by a query processor. We describe the implementation of Piet, and provide experimental evidence that overlay precomputation can outperform GIS systems that employ indexing schemes based on R-trees.
