Scaling spatial big data in a location-based social network

2014 ◽  
Vol 5 (2) ◽  
pp. 141-155
Author(s):  
Maxwell Guimarães de Oliveira ◽  
Ana Gabrielle Ramos Falcão ◽  
Cláudio De Souza Baptista ◽  
Hugo Feitosa de Figueiredo ◽  
Daniel Farias Batista Leite
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Response Times ◽  
Smart Cities ◽  
High Volume ◽  
Geographic Region ◽  
Technological Advances ◽  
Shared Information ◽  
Spatial Big Data ◽  
Location Based Social Networks

The widespread of the World Wide Web has resulted in a high volume of volunteered generated information using different formats including text, photography and video. The technological advances of recent years enabled the emergence and the popularization of various mobile devices equipped with GPS and connectivity to the Internet. This scenario contributed to the advent of several location-based applications and aroused the interest of many users in the geographical context of the information. An example of such applications are the Location-Based Social Networks (LBSN), in which the users interact with information classified by geographic region, as in the context of Smart Cities, in which citizens can interact pinning their criticisms, opinions and comments on various topics related to their city or neighborhood. The LBSNs have increasingly attracted the interest of the population and have consequently registered an increase in both the number of users interacting and the volume of shared information. This popularity brings up concerns about scalability, since it is essential to provide an environment that maintains the users active and motivated for contributing. Thus, the LBSNs must ensure acceptable response times, especially in spatial queries performed by their users, otherwise such applications may collapse due to the abandonment of their faithful users. Among several proposals of LBSNs in the community, it is still difficult to find out approaches concerned in scalability. In this context, this paper proposes an approach based on Big Data technologies to provide scalability in LBSNs and thus handle large volumes of spatial data. Our approach exploits NoSQL databases, the Map/Reduce technique and the development of extensions for indexing and querying Spatial Big Data.

scholarly journals Major Big Data Challenges in Most Industries and Innovative Solutions

2019 ◽  
Vol 9 (2S2) ◽  
pp. 424-428
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Spatial Data ◽  
High Volume ◽  
High Definition ◽  
Management Tools ◽  
Social Media Networks ◽  
Massive Growth ◽  
Multiple Challenges ◽  
High Definition Tv

The term “Big data” refers to “the high volume of data sets that are relatively complex in nature and having challenges in processing and analyzing the data using conventional database management tools”. In the digital universe, the data volume and variety that, we deal today have grown-up massively from different sources such as Business Informatics, Social-Media Networks, Images from High Definition TV, data from Mobile Networks, Banking data from ATM Machines, Genomics and GPS Trails, Telemetry from automobiles, Meteorology, Financial market data etc. Data Scientists confirm that 80% of the data that we have gathered today are in unstructured format, i.e. in the form of images, pixel data, Videos, geo-spatial data, PDF files etc. Because of the massive growth of data and its different formats, organizations are having multiple challenges in capturing, storing, mining, analyzing, and visualizing the Big data. This paper aims to exemplify the key challenges faced by most organizations and the significance of implementing the emerging Big data techniques for effective extraction of business intelligence to make better and faster decisions

scholarly journals QOS MANAGEMENT IN REAL-TIME SPATIAL BIG DATA USING FEEDBACK CONTROL SCHEDULING

2015 ◽  
Vol II-3/W5 ◽  
pp. 243-248 ◽  
Author(s):  
S. Hamdi ◽  
E. Bouazizi ◽  
S. Faiz
Keyword(s):  
Big Data ◽  
Feedback Control ◽  
Real Time ◽  
Spatial Data ◽  
Large Scale ◽  
The Real ◽  
Remote Sensors ◽  
Spatial Big Data ◽  
Active Research ◽  
Access To Data

Geographic Information System (GIS) is a computer system designed to capture, store, manipulate, analyze, manage, and present all types of spatial data. Spatial data, whether captured through remote sensors or large scale simulations has always been big and heterogenous. The issue of real-time and heterogeneity have been extremely important for taking effective decision. Thus, heterogeneous real-time spatial data management has become a very active research domain. Existing research has principally focused on querying of real-time spatial data and their updates. But the unpredictability of access to data maintain the behavior of the real-time GIS unstable. In this paper, we propose the use of the real-time Spatial Big Data and we define a new architecture called FCSA-RTSBD (Feedback Control Scheduling Architecture for Real-Time Spatial Big Data). The main objectives of this architecture are the following: take in account the heterogeneity of data, guarantee the data freshness, enhance the deadline miss ratio even in the presence of conflicts and unpredictable workloads and finally satisfy the requirements of users by the improving of the quality of service (QoS).

Tools for the Storage and Analysis of Spatial Big Data

2017 ◽  
Author(s):  
Przemysław Lisowski ◽  
Adam Piórkowski ◽  
Andrzej Lesniak
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
File Systems ◽  
Large Datasets ◽  
Distributed File Systems ◽  
Data Systems ◽  
Data Production ◽  
Spatial Big Data ◽  
Big Data Systems ◽  
Access To Data

Storing large amounts of spatial data in GIS systems is problematic. This problem is growing due to ever- increasing data production from a variety of data sources. The phenomenon of collecting huge amounts of data is called Big Data. Existing solutions are capable of processing and storing large volumes of spatial data. These solutions also show new approaches to data processing. Conventional techniques work with ordinary data but are not suitable for large datasets. Their efficient action is possible only when connected to distributed file systems and algorithms able to reduce tasks. This review focuses on the characteristics of large spatial data and discusses opportunities offered by spatial big data systems. The work also draws attention to the problems of indexing and access to data, and proposed solutions in this area.

Data Mining Approaches for Geo-Spatial Big Data

2012 ◽  
Vol 3 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Spatial Data ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Heterogeneous Information ◽  
Data Mining Techniques ◽  
Rough Sets Theory ◽  
Spatial Big Data ◽  
Sets Theory

The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given

A Review on Spatial Big Data Analytics and Visualization

2018 ◽  
pp. 179-197
Author(s):  
Bangaru Kamatchi Seethapathy ◽  
Parvathi R
Keyword(s):  
Big Data ◽  
Mobile Phones ◽  
Spatial Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Climatic Conditions ◽  
Size Analysis ◽  
Spatial Big Data ◽  
Visualization Tools ◽  
Spatial Dataset

Spatial dataset, which is becoming nontraditional due to the increase in usage of social media sensor networks, gaming and many other new emerging technologies and applications. The wide variety of sensors are used in solving real time problems like natural calamities, traffic analysis, analyzing climatic conditions and the usage of GPS, GPRS in mobile phones all together creates huge amount of spatial data which really exceeds the traditional spatial data analytics platform and become spatial big data .Spatial big data provide new demanding situations for their size, analysis, and exploration. This chapter discusses about the analysis of spatial data and how it gets descriptive manipulation, so that one can understand how multi variant variables get interact with each other along with the different visualization tools which make the understanding of spatial data easier.

scholarly journals TIME-RELATED QUALITY DIMENSIONS OF URBAN REMOTELY SENSED BIG DATA

2018 ◽  
Vol XLII-4 ◽  
pp. 315-320 ◽  
Author(s):  
Z. Kugler ◽  
G. Szabó ◽  
H. M. Abdulmuttalib ◽  
C. Batini ◽  
H. Shen ◽  
...  
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Spatial Data ◽  
Urban Areas ◽  
Smart Cities ◽  
Remotely Sensed ◽  
Traffic Monitoring ◽  
Remotely Sensed Data ◽  
Related Quality ◽  
Quality Dimensions

<p><strong>Abstract.</strong> Our rapidly changing world requires new sources of image based information. The quickly changing urban areas, the maintenance and management of smart cities cannot only rely on traditional techniques based on remotely sensed data, but also new and progressive techniques must be involved. Among these technologies the volunteer based solutions are getting higher importance, like crowd-sourced image evaluations, mapping by satellite based positioning techniques or even observations done by unskilled people. Location based intelligence has become an everyday practice of our life. It is quite enough to mention the weather forecast and traffic monitoring applications, where everybody can act as an observer and acquired data – despite their heterogeneity in quality – provide great value. Such value intuitively increases when data are of better quality. In the age of visualization, real-time imaging, big data and crowd-sourced spatial data have revolutionary transformed our general applications. Most important factors of location based decisions are the time-related quality parameters of the used data. In this paper several time-related data quality dimensions and terms are defined. The paper analyses the time sensitive data characteristics of image-based crowd-sourced big data, presents quality challenges and perspectives of the users. The data quality analyses focus not only on the dimensions, but are also extended to quality related elements, metrics. The paper discusses the connection of data acquisition and processing techniques, considering even the big data aspects. The paper contains not only theoretical sections, strong practice-oriented examples on detecting quality problems are also covered. Some illustrative examples are the OpenStreetMap (OSM), where the development of urbanization and the increasing process of involving volunteers can be studied. This framework is continuing the previous activities of the Remote Sensing Data Quality Working Group (ICWGIII/IVb) of the ISPRS in the topic focusing on the temporal variety of our urban environment.</p>

Spatial Data Mining

2014 ◽  
Vol 10 (4) ◽  
pp. 50-70 ◽  
Author(s):  
Shuliang Wang ◽  
Hanning Yuan
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Spatial Data ◽  
Environmental Issues ◽  
Spatial Data Mining ◽  
Academic Community ◽  
Primary Role ◽  
Big Data Mining ◽  
Spatial Big Data ◽  
Heavy Pollution

Big data brings the opportunities and challenges into spatial data mining. In this paper, spatial big data mining is presented under the characteristics of geomatics and big data. First, spatial big data attracts much attention from the academic community, business industry, and administrative governments, for it is playing a primary role in addressing social, economic, and environmental issues of pressing importance. Second, humanity is submerged by spatial big data, such as much garbage, heavy pollution and its difficulties in utilization. Third, the value in spatial big data is dissected. As one of the fundamental resources, it may help people to recognize the world with population instead of sample, along with the potential effectiveness. Finally, knowledge discovery from spatial big data refers to the basic technologies to realize the value of big data, and relocate data assets. And the uncovered knowledge may be further transformed into data intelligences.

scholarly journals Understanding Cumulative Hazards in a Rustbelt City: Integrating GIS, Archaeology, and Spatial History

Urban Science ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 83
Author(s):  
Dan Trepal ◽  
Don Lafreniere
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Environmental Sustainability ◽  
Historical Record ◽  
Decision Makers ◽  
Landscape Model ◽  
Final Model ◽  
Data Infrastructure ◽  
Spatial History ◽  
Spatial Big Data

We combine the Historical Spatial Data Infrastructure (HSDI) concept developed within spatial history with elements of archaeological predictive modeling to demonstrate a novel GIS-based landscape model for identifying the persistence of historically-generated industrial hazards in postindustrial cities. This historical big data approach draws on over a century of both historical and modern spatial big data to project the presence of specific persistent historical hazards across a city. This research improves on previous attempts to understand the origins and persistence of historical pollution hazards, and our final model augments traditional archaeological approaches to site prospection and analysis. This study also demonstrates how models based on the historical record, such as the HSDI, complement existing approaches to identifying postindustrial sites that require remediation. Our approach links the work of archaeologists more closely to other researchers and to municipal decision makers, permitting closer cooperation between those involved in archaeology, heritage, urban redevelopment, and environmental sustainability activities in postindustrial cities.

scholarly journals Analysing the Sustainability of Urban Development: A review on the Potential Use of Volunteered Geographic Information

2015 ◽  
Vol II-2/W2 ◽  
pp. 169-174
Author(s):  
N. Ibrahim ◽  
U. Ujang ◽  
G. Desa ◽  
A. Ariffin
Keyword(s):  
Urban Development ◽  
Spatial Data ◽  
Low Cost ◽  
Volunteered Geographic Information ◽  
High Volume ◽  
Geographic Information ◽  
Sustainable Urban Development ◽  
Technological Advances ◽  
Available Information ◽  
Potential Use

The challenges of how to ensure sustainable urban development are currently one of the important agenda among governments around the world. The stakeholders require the latest and high volume of geographic information for the decision making process to efficiently respond to challenges, improve service delivery to citizens, and plan a successful future of the city. However, it is time-consuming and costly to get the available information and some of the information is not up-to-date. Recently, GeoWeb 2.0 technological advances have increased the number of volunteers from non-professional citizen to contribute to the collection, sharing, and distribution of geographic information. The information known as Volunteered Geographic Information (VGI) has generated another approach of spatial data sources that can give up-to-date, huge volume of data, and available geographic information in a low cost for various applications. With this in mind, this paper presents a review of literature based on the potential use of Volunteered Geographic Information (VGI) in measuring sustainability of urban development. The review highlighted that social, economic, and environment as three pertinent pillars relating to the use of VGI for measurement sustainable urban development.

Failing to engage? Big data, smart cities and the built environment sector: an analysis of international case studies

2018 ◽  
Author(s):  
Tim Dixon ◽  
Jorn Van De Wetering ◽  
Martin Sexton
Keyword(s):  
Big Data ◽  
Built Environment ◽  
Case Studies ◽  
Smart Cities
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