Ad-hoc OLAP on Trajectory Data

Μαρκετοσ, Γερασιμοσ; Theodoridis, Yannis

doi:10.1109/mdm.2010.63

Cited by 10 publications

(2 citation statements)

References 14 publications

(44 reference statements)

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“…Related approaches are surveyed in Mahboubi et al (2009). Other examples include OLAP on trajectory data (Marketos & Theodoridis, 2010) and mobile OLAP (Maniatis, 2004).…”

Section: On-line Analytical Processingmentioning

confidence: 99%

Advances on Data Management and Information Systems

Darmont

Novikov²,

Wrembel

et al. 2022

Inf Syst Front

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The research and technological area of data management encompasses various concepts, techniques, algorithms and technologies, including data modeling, data integration and ingestion, transactional data management, query languages, query optimization, physical data storage, data structures, analytical techniques (including On-Line Analytical Processing -OLAP), as well as service creation and orchestration (Garcia-Molina et al., 2009). Data management technologies are core components of every information system, either centralized or distributed, deployed in an onpremise hardware architecture or in a cloud ecosystem. Data management technologies have been used in commercial, mature products for decades. They were originally developed for managing structured data (mainly expressed in the relational data model).Yet, the ubiquitous big data (Azzini et al., 2021) require development of new data management techniques, suitable for the variety of data formats (from structured, through semi-structured, to unstructured), overwhelming data volumes and velocity of big data generation. These new techniques draw upon the concepts applied to managing relational data.

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“…Related approaches are surveyed in Mahboubi et al (2009). Other examples include OLAP on trajectory data (Marketos & Theodoridis, 2010) and mobile OLAP (Maniatis, 2004).…”

Section: On-line Analytical Processingmentioning

confidence: 99%

Advances on Data Management and Information Systems

Darmont

Novikov²,

Wrembel

et al. 2022

Inf Syst Front

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“…Balzer et al [13] propose OLAP operators to group trajectories and present a tool for their visualization. In [14] the authors define a DW to store entire trajectories as measures and address the problem of their aggregation and visualization [15], e.g., the amount of trajectories that passed through an area. This allows users the visualization of high traffic areas.…”

Section: Introductionmentioning

confidence: 99%

Extracting Trajectories with the Map Cube Operator

Moreno¹,

Alzate²,

Hernndez³

2014

Appl. Math. Inf. Sci.

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OLAP (On Line Analytical Processing) is a set of techniques and operators to facilitate the analysis of data that are usually stored in a specialized database known as data warehouse. Data is analyzed in order to provide information about a particular business subject called fact, for example, crimes, sales, shipments, among others. In this paper, we extend the functionality of an OLAP operator, known as map cube, with the definition and incorporation of a function called Trajectory. This function allows the analysts extract the trajectory followed by a set of facts. For example, the trajectory of crimes of each suspect, starting from his/her oldest crime to his/her more recent one. In order to prove the expediency of our proposal, we developed a prototype that was applied to a case study of crimes in Pennsylvania. Although more extensive experiments are required, our results show patterns that can help to understand the modus operandi of suspects.

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Preparing for Mobility Data Exploration

Pelekis

Theodoridis

2014

Mobility Data Management and Exploration

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Ad-hoc OLAP on Trajectory Data

Cited by 10 publications

References 14 publications

Advances on Data Management and Information Systems

Advances on Data Management and Information Systems

Extracting Trajectories with the Map Cube Operator

Preparing for Mobility Data Exploration

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