scholarly journals Trajectory databases: Data models, uncertainty and complete query languages

2010 ◽  
Vol 76 (7) ◽  
pp. 538-560 ◽  
Author(s):  
Bart Kuijpers ◽  
Walied Othman
Keyword(s):  
Query Languages ◽  
Data Models

scholarly journals Data Portability Among Providers o Platform as a Service

2013 ◽  
Vol 21 (Special-Issue) ◽  
pp. 7-11
Author(s):  
Darko Androcec
Keyword(s):  
Data Storage ◽  
Query Languages ◽  
Data Models ◽  
Service Model ◽  
Data Types ◽  
Data Manipulation ◽  
Platform As A Service ◽  
Cloud Providers ◽  
Data Portability ◽  
Lock In

Abstract Platform as a service model has certain obstacles, including data lock-in. It is expensive and time-consuming to move data to the alternative providers. This paper presents data storage options in platform as a service offers and identifies the most common data portability problems between various commercial providers of platform as a service. There are differences among their storage models, data types, remote APIs for data manipulation and query languages. Representing data models of platform as a service and data mappings by means of ontology can provide a common layer to achieve data portability among different cloud providers.

Data Models and Query Languages for Linked Geospatial Data

2012 ◽  
pp. 290-328 ◽  
Author(s):  
Manolis Koubarakis ◽  
Manos Karpathiotakis ◽  
Kostis Kyzirakos ◽  
Charalampos Nikolaou ◽  
Michael Sioutis
Keyword(s):  
Query Languages ◽  
Data Models ◽  
Geospatial Data

scholarly journals Data models and query languages for spatial databases

1998 ◽  
Vol 25 (1-2) ◽  
pp. 29-53 ◽  
Author(s):  
Jan Paredaens ◽  
Bart Kuijpers
Keyword(s):  
Spatial Databases ◽  
Query Languages ◽  
Data Models

Graph data models, query languages and programming paradigms

2018 ◽  
Vol 11 (12) ◽  
pp. 2106-2109 ◽  
Author(s):  
Alin Deutsch ◽  
Yannis Papakonstantinou
Keyword(s):  
Query Languages ◽  
Data Models ◽  
Graph Data ◽  
Programming Paradigms

Indexing Techniques for Spatiotemporal Databases

2011 ◽  
pp. 1911-1916
Author(s):  
George Lagogiannis ◽  
Christos Makris ◽  
Yannis Panagis ◽  
Spyros Sioutas ◽  
Evangelos Theodoridis ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Environmental Science ◽  
Query Languages ◽  
Data Models ◽  
Time Dependent ◽  
Abstract Data Type ◽  
Spatiotemporal Databases ◽  
Geometric Properties ◽  
Research Areas ◽  
Database Research

We can define as spatiotemporal any database that maintains objects with geometric properties that change over time, where usual geometric properties are the spatial position and spatial extent of an object in a specific d-dimensional space. The need to use spatiotemporal databases appears in a variety of applications such as intelligent transportation systems, cellular communications, and meteorology monitoring. This field of database research collaborates tightly with other research areas such as mobile telecommunications, and is harmonically integrated with other disciplines such as CAD/CAM, GIS, environmental science, and bioinformatics. Spatiotemporal databases stand at the crossroad of two other database research areas: spatial databases (Güting, 1994; Gaede & Gunther, 1998) and temporal databases (Salzberg & Tsotras, 1999). The efficient implementation of spatiotemporal databases needs new data models and query languages and novel access structures for storing and accessing information. In Güting, Bohlen, Erwig, Jensen, Lorentzos, Schneider, and Vazirgiannis (2000) a data model and a query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects, were proposed; the basic idea was to represent time-dependent geometries as attribute data types and to provide an abstract data type extension to the traditional database data models and query languages. In that paper, it was also discussed how various temporal and spatial models could possibly be extended to be spatiotemporal models.

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