scholarly journals QB4MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 265
Author(s):  
Irya Wisnubhadra ◽  
Safiza Kamal Baharin ◽  
Nurul A. Emran ◽  
Djoko Budiyanto Setyohadi
Keyword(s):  
Semantic Web ◽  
Spatial Data ◽  
Moving Objects ◽  
Open Data ◽  
Moving Object ◽  
Online Analytical Processing ◽  
Multidimensional Data ◽  
Trajectory Data ◽  
Advanced Analysis ◽  
Analytical Processing

The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.

scholarly journals Multidimensional enrichment of spatial RDF data for SOLAP

Semantic Web ◽  
2021 ◽  
pp. 1-35
Author(s):  
Nurefşan Gür ◽  
Torben Bach Pedersen ◽  
Katja Hose ◽  
Mikael Midtgaard
Keyword(s):  
Semantic Web ◽  
Spatial Data ◽  
Spatial Information ◽  
Complex Geometry ◽  
Open Data ◽  
Data Cube ◽  
Multidimensional Data ◽  
Wide Range ◽  
Advanced Analysis ◽  
Rdf Data

Large volumes of spatial data and multidimensional data are being published on the Semantic Web, which has led to new opportunities for advanced analysis, such as Spatial Online Analytical Processing (SOLAP). The RDF Data Cube (QB) and QB4OLAP vocabularies have been widely used for annotating and publishing statistical and multidimensional RDF data. Although such statistical data sets might have spatial information, such as coordinates, the lack of spatial semantics and spatial multidimensional concepts in QB4OLAP and QB prevents users from employing SOLAP queries over spatial data using SPARQL. The QB4SOLAP vocabulary, on the other hand, fully supports annotating spatial and multidimensional data on the Semantic Web and enables users to query endpoints with SOLAP operators in SPARQL. To bridge the gap between QB/QB4OLAP and QB4SOLAP, we propose an RDF2SOLAP enrichment model that automatically annotates spatial multidimensional concepts with QB4SOLAP and in doing so enables SOLAP on existing QB and QB4OLAP data on the Semantic Web. Furthermore, we present and evaluate a wide range of enrichment algorithms and apply them on a non-trivial real-world use case involving governmental open data with complex geometry types.

scholarly journals Mobility Data Warehouses

2019 ◽  
Vol 8 (4) ◽  
pp. 170 ◽  
Author(s):  
Alejandro Vaisman ◽  
Esteban Zimányi
Keyword(s):  
Spatial Data ◽  
Moving Objects ◽  
Moving Object ◽  
Data Warehouses ◽  
Mobility Analysis ◽  
Trajectory Data ◽  
Data Types ◽  
Seamless Integration ◽  
Moving Object Database ◽  
Mobility Data

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB.

Mining Partners in Trajectories

2020 ◽  
Vol 16 (1) ◽  
pp. 22-38
Author(s):  
Diego Vilela Monteiro ◽  
Rafael Duarte Coelho dos Santos ◽  
Karine Reis Ferreira
Keyword(s):  
Moving Objects ◽  
R Package ◽  
Moving Object ◽  
Spatiotemporal Data ◽  
Data Preparation ◽  
Trajectory Data ◽  
Gps Satellites ◽  
Object Trajectories ◽  
Moving Object Trajectory ◽  
Moving Object Trajectories

Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.

scholarly journals High-Fidelity Aerothermal Engineering Analysis for Planetary Probes Using DOTNET Framework and OLAP Cubes Database

2009 ◽  
Vol 2009 ◽  
pp. 1-21 ◽  
Author(s):  
Prabhakar Subrahmanyam
Keyword(s):  
User Interface ◽  
Application Programming Interface ◽  
Google Earth ◽  
Online Analytical Processing ◽  
Trajectory Data ◽  
Atmospheric Entry ◽  
Flight Vehicles ◽  
Application Programming ◽  
Analytical Processing ◽  
Programming Interface

This publication presents the architecture integration and implementation of various modules inSpartaframework.Spartais a trajectory engine that is hooked to an Online Analytical Processing (OLAP) database for Multi-dimensional analysis capability. OLAP is an Online Analytical Processing database that has a comprehensive list of atmospheric entry probes and their vehicle dimensions, trajectory data, aero-thermal data and material properties like Carbon, Silicon and Carbon-Phenolic based Ablators. An approach is presented for dynamic TPS design. OLAP has the capability to run in one simulation several different trajectory conditions and the output is stored back into the database and can be queried for appropriate trajectory type. An OLAP simulation can be setup by spawning individual threads to run for three types of trajectory:Nominal,Undershoot and Overshoot trajectory. Sparta graphical user interface provides capabilities to choose from a list of flight vehicles or enter trajectory and geometry information of a vehicle in design. DOTNET framework acts as a middleware layer between the trajectory engine and the user interface and also between the web user interface and the OLAP database. Trajectory output can be obtained in TecPlot format, Excel output or in a KML (Keyhole Markup Language) format. Framework employs an API (application programming interface) to convert trajectory data into a formatted KML file that is used by Google Earth for simulating Earth-entry fly-by visualizations.

scholarly journals APDS: A framework for discovering movement pattern from trajectory database

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816
Author(s):  
Guan Yuan ◽  
Zhongqiu Wang ◽  
Zhixiao Wang ◽  
Fukai Zhang ◽  
Li Yuan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Moving Objects ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Movement Pattern ◽  
Moving Object ◽  
Periodic Pattern ◽  
Trajectory Data ◽  
Discovery System ◽  
Spatiotemporal Data Mining

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

scholarly journals A Survey on Big Data for Trajectory Analytics

2020 ◽  
Vol 9 (2) ◽  
pp. 88
Author(s):  
Damião Ribeiro de Almeida ◽  
Cláudio de Souza Baptista ◽  
Fabio Gomes de Andrade ◽  
Amilcar Soares
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Moving Objects ◽  
Research Field ◽  
Temporal Data ◽  
Trajectory Data ◽  
Management Technology ◽  
Spatial Big Data ◽  
Analytical Processing ◽  
Spatio Temporal

Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.

scholarly journals An Overview of Moving Object Trajectory Compression Algorithms

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Penghui Sun ◽  
Shixiong Xia ◽  
Guan Yuan ◽  
Daxing Li
Keyword(s):  
Moving Objects ◽  
Moving Object ◽  
Trajectory Data ◽  
Compression Algorithms ◽  
Valuable Data ◽  
Implementation Processes ◽  
Exponential Increase ◽  
Validation Criteria ◽  
Gps Devices ◽  
Moving Object Trajectory

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining.

scholarly journals Rancang Bangun Data Warehouse Untuk Pembuatan Laporan dan Analisis pada Data Kunjungan Pasien Rawat Jalan Rumah Sakit Universitas Airlangga Berbasis Online Analytical Processing (OLAP)

2017 ◽  
Vol 3 (1) ◽  
pp. 40
Author(s):  
Nur Ardista ◽  
Purbandini Purbandini ◽  
Taufik Taufik
Keyword(s):  
Data Warehouse ◽  
Medical Record ◽  
Process Analysis ◽  
Health Care Facilities ◽  
Multidimensional Analysis ◽  
Online Analytical Processing ◽  
Multidimensional Data ◽  
Microsoft Excel ◽  
Number Of Patients ◽  
Analytical Processing

Abstrak— Rumah Sakit Universitas Airlangga (RSUA) merupakan sarana pelayanan kesehatan yang dikelola di bawah naungan Universitas Airlangga. Seiring berjalannya proses bisnis, jumlah pasien RSUA yang semakin bertambah menyebabkan data kunjungan pasien rawat jalan yang harus dikelola oleh bagian rekam medis semakin banyak. Data tersebut dikelola untuk digunakan dalam pembuatan laporan. Informasi dalam laporan dihasilkan melalui perhitungan secara manual atau menggunakan formula Microsoft Excel menjadi kendala dalam pembuatan laporan selain adanya kebutuhan laporan dengan format beragam dan analisis multidimensional. Data warehouse berbasis Online Analytical Processing (OLAP) dapat diterapkan untuk menangani masalah tersebut. Tujuan penelitian ini adalah merancang dan membangun data warehouse berbasis OLAP agar dapat digunakan oleh bagian rekam medis RSUA dalam pembuatan laporan. Data warehouse dibangun melalui tujuh tahap yaitu analisis, desain, proses ETL (Extraction, Transformation, and Loading), penerapan OLAP, uji coba, eksplorasi untuk hasil laporan dan analisis, serta evalusi. Perancangan data warehouse menggunakan Nine Step Methodology dengan pemodelan berupa fact constellation schema. Hasil implementasi data warehouse adalah aplikasi OLAP yang dapat digunakan untuk membantu kinerja bagian rekam medis RSUA dalam pembuatan laporan, baik berupa tabel pivot maupun grafik. Penilaian pengguna terhadap sistem data warehouse menunjukkan kategori baik dengan hasil penilaian sebesar 73.61 persen. Kata Kunci— Data Warehouse, Rawat Jalan, ETL, Nine Step Methodology, OLAPAbstract— Airlangga University Hospital is a health care facilities managed by the auspices of Airlangga University. Increasing number of patients in RSUA caused more outpatients’ visits data must be managed by the medical record unit. The data was used to report making. The information in the reports generated through manual calculation or used function of Microsoft Excel became a problem of report making in addition to their reporting needs with diverse formats and multidimensional analysis. Data warehouse based on Online Analytical Processing (OLAP) could implemented to solved the problem. The goal of this research were to designing and implementing the data warehouse based on OLAP so it could be used by medical record unit to making report. Data warehouse was implemented in seven process : analysis, design, ETL (Extraction, Transformation, and Loading), implementing OLAP, trial, explore the report and analysis, and evaluation. Design of data warehouse were using Nine Step Methodology and fact constellation schema model.The outcome of this research was an OLAP application that can used to help the task of RSUA medical record unit to making report using pivot table or chart. User ratings against the data warehouse system showed good category with the results of 73.61 percent in assessment. Keywords— Data Warehouse, Outpatient, ETL, Nine Step Methodology, OLAP

A State-of-the-Art in Spatio-Temporal Data Warehousing, OLAP and Mining

Data Mining ◽  
2013 ◽  
pp. 2021-2056
Author(s):  
Leticia Gómez ◽  
Bart Kuijpers ◽  
Bart Moelans ◽  
Alejandro Vaisman
Keyword(s):  
Moving Objects ◽  
Data Warehousing ◽  
Route Planning ◽  
Geographic Information ◽  
Moving Object ◽  
Trajectory Data ◽  
Attribute Data ◽  
Object Databases ◽  
Spatio Temporal ◽  
The University

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, although not much work has been done to provide moving object databases with OLAP capabilities. In the first part of this paper we survey the SOLAP literature. We then address the problem of trajectory analysis, and review recent efforts regarding trajectory data warehousing and mining. We also provide an in-depth comparative study between two proposals: the GeoPKDD project (that makes use of the Hermes system), and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina. Finally, we discuss future directions in the field, including SOLAP analysis over raster data.

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