On the Support of Mobility in ORDBMS

2014 ◽  
Vol 4 (1) ◽  
pp. 38-64
Author(s):  
Nikos Pelekis ◽  
Elias Frentzos ◽  
Nikos Giatrakos ◽  
Yannis Theodoridis
Keyword(s):  
Spatial Data ◽  
Data Types ◽  
Temporal Semantics ◽  
Movement Data ◽  
Data Framework ◽  
Object Relational ◽  
Data Definition ◽  
Object Databases ◽  
Spatio Temporal ◽  
Manipulation Language

Composition of space and mobility in a unified data framework results into Moving Object Databases (MOD). MOD management systems support storage and query processing of non-static spatial objects and provide essential operations for higher level analysis of movement data. The goal of this paper is to present Hermes MOD engine that supports the aforementioned functionality through appropriate data types and methods in Object-Relational DBMS (ORDBMS) environments. In particular, Hermes exploits on the extensibility interface of ORDBMS that already have extensions for static spatial data types and methods that follow the Open Geospatial Consortium (OGC) standard, and extends the ORDBMS by supporting time-varying geometries that change their position and/or extent in space and time dimensions, either discretely or continuously. It further extends the data definition and manipulation language of the ORDBMS with spatio-temporal semantics and functionality.

HERMES

2016 ◽  
pp. 717-742
Author(s):  
Nikos Pelekis ◽  
Elias Frentzos ◽  
Nikos Giatrakos ◽  
Yannis Theodoridis
Keyword(s):  
Query Language ◽  
Expressive Power ◽  
Location Based Services ◽  
Temporal Semantics ◽  
Mobility Data ◽  
Object Relational ◽  
Data Definition ◽  
Spatio Temporal ◽  
Manipulation Language ◽  
Processing Techniques

This chapter presents HERMES, a prototype DB engine that defines a powerful query language for trajectory databases, which enables the support of mobility-centric applications, such as Location-Based Services (LBS). HERMES extends the data definition and manipulation language of Object-Relational DBMS (ORDBMS) with spatio-temporal semantics and functionality based on advanced spatio-temporal indexing and query processing techniques. Its implementation over two ORDBMS and its utilization in various domains proves the expressive power and applicability of HERMES in different application domains where knowledge regarding mobility data is essential. As a proof-of-concept, in this chapter HERMES is applied to a case study related with vehicle traffic analysis, demonstrating its flexibility and usefulness for delivering custom-defined LBS.

HERMES

2015 ◽  
Vol 5 (2) ◽  
pp. 19-41 ◽  
Author(s):  
Nikos Pelekis ◽  
Elias Frentzos ◽  
Nikos Giatrakos ◽  
Yannis Theodoridis
Keyword(s):  
Query Language ◽  
Expressive Power ◽  
Location Based Services ◽  
Temporal Semantics ◽  
Mobility Data ◽  
Object Relational ◽  
Data Definition ◽  
Spatio Temporal ◽  
Manipulation Language ◽  
Processing Techniques

This paper presents HERMES, a prototype DB engine that defines a powerful query language for trajectory databases, which enables the support of mobility-centric applications, such as Location-Based Services (LBS). HERMES extends the data definition and manipulation language of Object-Relational DBMS (ORDBMS) with spatio-temporal semantics and functionality based on advanced spatio-temporal indexing and query processing techniques. Its implementation over two ORDBMS and its utilization in various domains proves the expressive power and applicability of HERMES in different application domains where knowledge regarding mobility data is essential. As a proof-of-concept, in this paper HERMES is applied to a case study related with vehicle traffic analysis, demonstrating its flexibility and usefulness for delivering custom-defined LBS.

Object-Relational Spatial Indexing

2011 ◽  
pp. 49-80
Author(s):  
Hans-Peter Kriegel ◽  
Martin Pfeifle ◽  
Marco Potke ◽  
Thomas Seidl ◽  
Jost Enderle
Keyword(s):  
Spatial Data ◽  
Concurrency Control ◽  
Buffer Management ◽  
Database Systems ◽  
Data Types ◽  
Spatial Access ◽  
Access Methods ◽  
Object Relational ◽  
Relational Database Systems ◽  
Spatial Access Methods

In order to generate efficient execution plans for queries comprising spatial data types and predicates, the database system has to be equipped with appropriate index structures, query processing methods and optimization rules. Although available extensible indexing frameworks provide a gateway for seamless integration of spatial access methods into the standard process of query optimization and execution, they do not facilitate the actual implementation of the spatial access method. An internal enhancement of the database kernel is usually not an option for database developers. The embedding of a custom, block-oriented index structure into concurrency control, recovery services and buffer management would cause extensive implementation efforts and maintenance cost, at the risk of weakening the reliability of the entire system. The server stability can be preserved by delegating index operations to an external process, but this approach induces severe performance bottlenecks due to context switches and inter-process communication. Therefore, we present the paradigm of object-relational spatial access methods that perfectly fits to the common relational data model, and is highly compatible with the extensible indexing frameworks of existing object-relational database systems, allowing the user to define application-specific access methods.

A Multidimensional Methodology with Support for Spatio-Temporal Multigranularity in the Conceptual and Logical Phases

2011 ◽  
pp. 194-230 ◽  
Author(s):  
Concepción M. Gascueña ◽  
Rafael Guadalupe
Keyword(s):  
Spatial Data ◽  
Hierarchical Structures ◽  
The Other ◽  
Fe Model ◽  
Multidimensional Databases ◽  
Object Relational ◽  
Spatio Temporal ◽  
The One ◽  
Spatial Semantics ◽  
Thematic Data

The Multidimensional Databases (MDB) are used in the Decision Support Systems (DSS) and in Geographic Information Systems (GIS); the latter locates spatial data on the Earth’s surface and studies its evolution through time. This work presents part of a methodology to design MDB, where it considers the Conceptual and Logical phases, and with related support for multiple spatio-temporal granularities. This will allow us to have multiple representations of the same spatial data, interacting with other, spatial and thematic data. In the Conceptual phase, the conceptual multidimensional model—FactEntity (FE)—is used. In the Logical phase, the rules of transformations are defined, from the FE model, to the Relational and Object Relational logical models, maintaining multidimensional semantics, and under the perspective of multiple spatial, temporal, and thematic granularities. The FE model shows constructors and hierarchical structures to deal with the multidimensional semantics on the one hand, carrying out a study on how to structure “a fact and its associated dimensions.” Thus making up the Basic factEnty, and in addition, showing rules to generate all the possible Virtual factEntities. On the other hand, with the spatial semantics, highlighting the Semantic and Geometric spatial granularities.

Modelling Seismicity in California as a Spatio-Temporal Point Process Using inlabru: Insights for Earthquake Forecasting

2020 ◽  
Author(s):  
Mark Naylor ◽  
Kirsty Bayliss ◽  
Finn Lindgren ◽  
Francesco Serafini ◽  
Ian Main
Keyword(s):  
Point Process ◽  
Spatial Data ◽  
Point Processes ◽  
Earthquake Forecasting ◽  
Data Sets ◽  
Computationally Efficient ◽  
Data Types ◽  
Initial Application ◽  
Spatio Temporal ◽  
Diverse Data

<p>Many earthquake forecasting approaches have developed bespokes codes to model and forecast the spatio-temporal eveolution of seismicity. At the same time, the statistics community have been working on a range of point process modelling codes. For example, motivated by ecological applications, inlabru models spatio-temporal point processes as a log-Gaussian Cox Process and is implemented in R. Here we present an initial implementation of inlabru to model seismicity. This fully Bayesian approach is computationally efficient because it uses a nested Laplace approximation such that posteriors are assumed to be Gaussian so that their means and standard deviations can be deterministically estimated rather than having to be constructed through sampling. Further, building on existing packages in R to handle spatial data, it can construct covariate maprs from diverse data-types, such as fault maps, in an intutitive and simple manner.</p><p>Here we present an initial application to the California earthqauke catalogue to determine the relative performance of different data-sets for describing the spatio-temporal evolution of seismicity.</p>

scholarly journals K-Prototypes Segmentation Analysis on Large-Scale Ridesourcing Trip Data

2020 ◽  
Vol 2674 (9) ◽  
pp. 383-394 ◽  
Author(s):  
Jason Soria ◽  
Ying Chen ◽  
Amanda Stathopoulos
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Weather Conditions ◽  
Data Types ◽  
Adverse Weather Conditions ◽  
Segmentation Analysis ◽  
Adverse Weather ◽  
Mobility System ◽  
Spatio Temporal

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.

scholarly journals Geosmartness for Personalized and Sustainable Future Urban Mobility

2021 ◽  
pp. 59-83
Author(s):  
Martin Raubal ◽  
Dominik Bucher ◽  
Henry Martin
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Information Technologies ◽  
Large Scale ◽  
Smart Cities ◽  
Mobile Technologies ◽  
Urban Mobility ◽  
Mobility Patterns ◽  
Movement Data ◽  
Spatio Temporal

AbstractUrban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.

Shifted Maps: Revealing spatio-temporal topologies in movement data

2018 ◽  
Author(s):  
Heike Otten ◽  
Lennart Hildebrand ◽  
Till Nagel ◽  
Marian Dork ◽  
Boris Muller
Keyword(s):  
Movement Data ◽  
Spatio Temporal

Study of the Spatio-Temporal Data Model in Sea Battlefield

2012 ◽  
Vol 246-247 ◽  
pp. 744-748
Author(s):  
Yue Lin Sun ◽  
Lei Bao ◽  
Yi Hang Peng
Keyword(s):  
Data Model ◽  
Object Oriented ◽  
Space Time ◽  
Comprehensive Analysis ◽  
Temporal Data ◽  
Data Types ◽  
Spatio Temporal ◽  
The Right ◽  
Effective Analysis

An effective analysis of the battlefield situation and spatio-temporal data model in a sea battlefield has great significance for the commander to perceive the battlefield situation and to make the right decisions. Based on the existing spatio-temporal data model, the present paper gives a comprehensive analysis of the characteristics of sea battlefield data, and chooses the object-oriented spatio-temporal data model to modify it; at the same time this paper introduces sea battlefield space-time algebra system to define various data types formally, which lays the foundation for the establishment of the sea battlefield spatio-temporal data model.

scholarly journals Pertinent spatio-temporal scale of observation to understand suspended sediment yield control factors in the Andean region: the case of the Santa River (Peru)

2013 ◽  
Vol 17 (11) ◽  
pp. 4641-4657 ◽  
Author(s):  
S. B. Morera ◽  
T. Condom ◽  
P. Vauchel ◽  
J.-L. Guyot ◽  
C. Galvez ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Spatial Data ◽  
Sediment Yield ◽  
Water Discharge ◽  
Daily Rainfall ◽  
Sediment Concentration ◽  
Suspended Sediment Yield ◽  
Control Factors ◽  
Daily Water ◽  
Spatio Temporal

Abstract. Hydro-sedimentology development is a great challenge in Peru due to limited data as well as sparse and confidential information. This study aimed to quantify and to understand the suspended sediment yield from the west-central Andes Mountains and to identify the main erosion-control factors and their relevance. The Tablachaca River (3132 km2) and the Santa River (6815 km2), located in two adjacent Andes catchments, showed similar statistical daily rainfall and discharge variability but large differences in specific suspended-sediment yield (SSY). In order to investigate the main erosion factors, daily water discharge and suspended sediment concentration (SSC) datasets of the Santa and Tablachaca rivers were analysed. Mining activity in specific lithologies was identified as the major factor that controls the high SSY of the Tablachaca (2204 t km2 yr−1), which is four times greater than the Santa's SSY. These results show that the analysis of control factors of regional SSY at the Andes scale should be done carefully. Indeed, spatial data at kilometric scale and also daily water discharge and SSC time series are needed to define the main erosion factors along the entire Andean range.

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