Spatial OLAP modeling: an overview base on spatial objects changing over time

Pestana, Gabriel; Silva, M.M. da; Bédard, Yvan

doi:10.1109/icccyb.2005.1511565

Cited by 14 publications

(12 citation statements)

References 14 publications

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“…For example, while they use a similar file tree hierarchy, Linux uses I‐nodes to implement the links between a directory and its files and a file and its blocks, and Windows may use a File Allocation Table, a chain of addresses, to manage the links between directories, files, and disk blocks. Links between elements can also be found in Spatial OnLine Analytical Processing systems . However, they mostly use existing database systems to store links between elements, and their internal structure is not clearly known.…”

Section: Discussionmentioning

confidence: 99%

Multi‐scale navigation of large trace data: A survey

Ezzati‐Jivan

Dagenais

2017

Concurrency and Computation

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Summary Dynamic analysis through execution traces is frequently used to analyze the runtime behavior of software systems. However, tracing long running executions generates voluminous data, which are complicated to analyze and manage. Extracting interesting performance or correctness characteristics out of large traces of data from several processes and threads is a challenging task. Trace abstraction and visualization are potential solutions to alleviate this challenge. Several efforts have been made over the years in many subfields of computer science for trace data collection, maintenance, analysis, and visualization. Many analyses start with an inspection of an overview of the trace, before digging deeper and studying more focused and detailed data. These techniques are common and well supported in geographical information systems, automatically adjusting the level of details depending on the scale. However, most trace visualization tools operate at a single level of representation, which are not adequate to support multilevel analysis. Sophisticated techniques and heuristics are needed to address this problem. Multi‐scale (multilevel) visualization with support for zoom and focus operations is an effective way to enable this kind of analysis. Considerable research and several surveys are proposed in the literature in the field of trace visualization. However, multi‐scale visualization has yet received little attention. In this paper, we provide a survey and methodological structure for categorizing tools and techniques aiming at multi‐scale abstraction and visualization of execution trace data and discuss the requirements and challenges faced to be able to meet evolving user demands.

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Section: Discussionmentioning

confidence: 99%

Multi‐scale navigation of large trace data: A survey

Ezzati‐Jivan

Dagenais

2017

Concurrency and Computation

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“…However, since AIRNET platform is still being implemented we have to deal with problems of missing and erroneous data. The use of interpolation function will help at solving the problems of missing or erroneous data at the higher levels of hierarchy [7]. The conventional multidimensional model needs also to be extended to support the spatiotemporal continuity in terms of storage optimization and fast query responses [10] which is under on-going work.…”

Section: Discussionmentioning

confidence: 99%

“…The multidimensional spatial data modulation follows the methodology presented in [7]. Following this methodology we add support for spatiotemporal properties and by enabling the modeling of spatial objects' geometric evolution over time we provide decision-makers with the possibility to backtrack traffic data analysis within the spatial context they occur.…”

Section: The Ground Processing Decision Support Modulementioning

confidence: 99%

An airport decision support system for mobiles surveillance & alerting

Pestana

Silva²,

Casaca

et al. 2005

Proceedings of the 4th ACM International Workshop on Data Engineering for Wireless and Mobile Access

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The improvement of safety on airport surface movements requires an integrated management of the entire airport, which is hard because usually that management is performed independently. This scenario sometimes compromises the overall efficiency of airport operations. The continuous increase of airport traffic has also lead to an escalating number of accidents and incidents on airport surface movements. In case of crisis situations, airport operators involved in the decision-making process have a limited knowledge of the overall traffic situation, thus leading to ground movement hazards. Therefore, the safety of vehicles on the movement area is seen as a major issue. The AIRNET project contributes to the solution of this problem by developing a new low-cost and modular platform for safety management on airport surface movements.The paper presents the AIRNET platform architecture focussing into its innovative characteristics to provide, in an airport environment, the surveillance, control, guidance, and most of all the decision support services for airport operators. The paper also outlines the dataflow within the Ground System and how the data are send/received to/from the on-board systems, which correspond to a simplified version of the corresponding modules on the Ground System.

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“…Bédard et al [1,10] define three types of spatial dimensions: the non-geometric spatial dimensions, the geometric spatial dimensions and the mixed spatial dimensions. In the first type is a descriptive/conventional dimension.…”

Section: Related Workmentioning

confidence: 99%

Towards a more straightforward and more expressive metamodel for SDW modeling

Aguila

Fidalgo

Mota

2011

Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP

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There are works that propose metamodels for SDW modeling. However, we observe that most of these works defines metamodels that mix concepts of DW modeling with concepts of the OLAP cube modeling. We disagree with this view, because we understand that a DW is essentially a database, which can be analyzed/queried by any data analysis tool. Moreover, we also note other limitations. For example: the most proposed metamodels 1) do not support important techniques for DW modeling and 2) represent the spatiality in a SDW stereotyping the dimensions and fact tables as spatial or hybrid, rather than simply stereotyping the attributes/measures as spatial. Aiming to solve these problems we propose a more straightforward and more expressive metamodel for SDW modeling, named Spatial Data Warehouse Metamodel (SDWM), which describes the constructors and the restrictions needed to model and validate a SDW schema. As a proof of concept, we implemented a CASE tool according to our metamodel and, with this CASE tool, we designed a SDW for the Meteorological Laboratory from the State of Pernambuco, in Brazil.

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Spatial OLAP modeling: an overview base on spatial objects changing over time

Cited by 14 publications

References 14 publications

Multi‐scale navigation of large trace data: A survey

Multi‐scale navigation of large trace data: A survey

An airport decision support system for mobiles surveillance & alerting

Towards a more straightforward and more expressive metamodel for SDW modeling

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