A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching

Laska, Marius; Herlé, Stefan; Klamma, Ralf; Blankenbach, Jörg

doi:10.3390/ijgi7070238

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…In the IoT, spatial data can appear in different formats. Mobile sensor applications store data related to trajectories (Laska, Herle, Klamma, & Blankenbach, 2018;Li et al, 2019;Limkar & Jha, 2019;Lwin, Takeuchi, Sekimoto, & Zettsu, 2019;Mello et al, 2019;Zhou, Li, & Tu, 2020), sensor location (Işikdağ, 2020), buildings (Yang et al, 2020) and mobile devices (Li et al, 2020;You, Tunçer, Zhu, Xing, & Yuen, 2019). Furthermore, locations related to abstract events can be stored, such as events (Finogeev et al, 2019) and social media content (Qader and Hristidis, 2017).…”

Section: Rq3 What Are the Main Data Formats Stored?mentioning

confidence: 99%

“…Spark (Al Jawarneh et al, 2019;Limkar and Jha, 2019;Zaharia, Chowdhury, Franklin, Shenker, & Stoica, 2010) offers an engine for distributed massive data processing. Storm (Apache Software Foundation, 2011b; Laska et al, 2018;Rathore et al, 2016) is a system for distributed computing of real-time data streams. Kafka (Apache Software Foundation, 2011a; Kraft et al, 2019;Laska et al, 2018) is a distributed platform for streaming.…”

Section: Rq4 Which Artifacts Are Most Used To Deal With Geospatial Bi...mentioning

confidence: 99%

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Applications of geospatial big data in the Internet of Things

Silva

Holanda

2021

Transactions in GIS

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The Internet of Things (IoT) has emerged as a communication paradigm that allows the interconnection of devices that interact with the environment through sensors and actuators, collect data from it, and transmit these data over a network without requiring human interaction (Armstrong, Wang, & Zhang, 2019). IoT architectures can demand database management systems capable of handling high data generation and transmission rates, bringing the research on the IoT closer to the studies on big data (Ge,

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Section: Rq3 What Are the Main Data Formats Stored?mentioning

confidence: 99%

Section: Rq4 Which Artifacts Are Most Used To Deal With Geospatial Bi...mentioning

confidence: 99%

Applications of geospatial big data in the Internet of Things

Silva

Holanda

2021

Transactions in GIS

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“…A real-time stream processing pipeline and current research activities in real-time spatiotemporal data domain are highlighted and compared by [81] and [80] respectively. Apache Storm, Apache Kafka and GeoMQTT broker are utilized as core tools for the development of pipeline architecture in former study that is capable for real-time processing of spatiotemporal data streams.…”

Section: B Assessment Of Rq2: Which Challenges Have Been Faced Durinmentioning

confidence: 99%

Challenges and Solutions for Processing Real-Time Big Data Stream: A Systematic Literature Review

Mehmood

Anees

2020

IEEE Access

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Contribution: Recently, real-time data warehousing (DWH) and big data streaming have become ubiquitous due to the fact that a number of business organizations are gearing up to gain competitive advantage. The capability of organizing big data in efficient manner to reach a business decision empowers data warehousing in terms of real-time stream processing. A systematic literature review for real-time stream processing systems is presented in this paper which rigorously look at the recent developments and challenges of real-time stream processing systems and can serve as a guide for the implementation of real-time stream processing framework for all shapes of data streams. Background: Published surveys and reviews either cover papers focusing on stream analysis in applications other than real-time DWH or focusing on extraction, transformation, loading (ETL) challenges for traditional DWH. This systematic review attempts to answer four specific research questions. Research Questions: 1)Which are the relevant publication channels for real-time stream processing research? 2) Which challenges have been faced during implementation of real-time stream processing? 3) Which approaches/tools have been reported to address challenges introduced at ETL stage while processing real-time stream for real-time DWH? 4) What evidence have been reported while addressing different challenges for processing real-time stream? Methodology: A systematic literature was conducted to compile studies related to publication channels targeting real-time stream processing/joins challenges and developments. Following a formal protocol, semi-automatic and manual searches were performed for work from 2011 to 2020 excluding research in traditional data warehousing. Of 679,547 papers selected for data extraction, 74 were retained after quality assessment. Findings: This systematic literature highlights implementation challenges along with developed approaches for real-time DWH and big data stream processing systems and provides their comparisons. This study found that there exists various algorithms for implementing real-time join processing at ETL stage for structured data whereas less work for un-structured data is found in this subject matter. INDEX TERMS Real-time stream processing, big data streaming, structured/un-structured data, ETL, systematic literature review Publication source Channel References No. %age IEEE Transactions on Knowledge and Data Engineering

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“…Rieke et al (2018) presented a selection of approaches developed in different research projects to overcome the gaps that retain certain geospatial applications from using real-time information. Laska et al (2018) presented an architecture for real-time processing of spatiotemporal IoT stream data. Pozzebon et al (2018) proposed a wireless sensor network framework for realtime monitoring of height and volume Variations on sandy beaches and dunes.…”

Section: Geoinformation and Iotmentioning

confidence: 99%

An IOT architecture for facilitating integration of geoinformation

Işıkdağ

2020

International Journal of Engineering and Geosciences

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Background: GeoInformation, is very valuable for a range of fields ranging from location based services and navigation to smart cities and homes. On the other hand today many fields benefit from Internet of Things (IoT) implementations, where the machine-to-machine and machine-to-human transmission of GeoInformation frequently occurs. This transmission usually occurs in multi-source/multi-target and multiplatform IoT environments. Problem Statement: In many cases real-time GeoInformation stays in its own island of automation, and thus its real value cannot be uncovered. This happens mainly due to inefficiencies and problems that occur in the storage, sharing and exchange of real-time GeoInformation as a result of multi-source/multi-target and multi-platform nature of the IoT architectures. Research Approach: Integration appears as a critical paradigm which should be focused in order to store, manage and transfer of GeoInformation efficiently in these complex environments. In this context, the focus of the study was to test the applicability of different technologies and integration methods for acquisition, transmission and visualisation of multi-source GeoInformation through implementing an IoT Integration Testbed Architecture which is utilizing low-cost hardware (to acquire information), graph databases(to store information) and standard IoT protocols (to exchange information). The implementation explained in this paper covers acquisition of real time GeoInformation from a set of real and virtual sensors, storage of this GeoInformation in Graph Databases, exchange of information through two different communication models (request/response and publish/subscribe) based on standard IoT protocols, and visualization of information by web pages, web mapping services and using a GIS software. Results: The implementation results demonstrated a proof-ofconcept on how multi-source GeoInformation acquired from different type of IoT nodes can be integrated, stored and visualised on different platforms by utilising a standard IoT communication paradigms and multiple communication models.

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A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching

Cited by 19 publications

References 20 publications

Applications of geospatial big data in the Internet of Things

Applications of geospatial big data in the Internet of Things

Challenges and Solutions for Processing Real-Time Big Data Stream: A Systematic Literature Review

An IOT architecture for facilitating integration of geoinformation

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