An Application of Big Data Analytics in Road Transportation

Data is being captured in all domains of society and one of the important aspects is transportation. Large amounts of data have been collected, which are detailed, fine-grained, and of greater coverage and help us to allow traffic and transportation to be tracked to an extent that was not possible in the past. Existing big data analytics for transportation is already yielding useful applications in the areas of traffic routing, congestion management, and scheduling. This is just the origin of the applications of big data that will ultimately make the transportation network able to be managed properly and in an efficient way. It has been observed that so many individuals are not following the traffic rules properly, especially where there are high populations, so to monitor theses types of traffic violators, this chapter proposes a work that is mainly based on big data analytics. In this chapter, the authors trace the vehicle and the data that has been collected by different devices and analyze it using some of the big data analysis methods.

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From Conventional Data Analysis Methods to Big Data Analytics

Big Data for Insurance Companies ◽

10.1002/9781119489368.ch2 ◽

2018 ◽

pp. 27-41

Author(s):

Gilbert Saporta

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Analytics ◽

Big Data Analytics ◽

Analysis Methods ◽

Data Analysis Methods

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Real-time stream processing for Big Data

it - Information Technology ◽

10.1515/itit-2016-0002 ◽

2016 ◽

Vol 58 (4) ◽

Cited By ~ 7

Author(s):

Wolfram Wingerath ◽

Felix Gessert ◽

Steffen Friedrich ◽

Norbert Ritter

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Sensor Data ◽

Distributed Data ◽

Qualitative Comparison ◽

Data Repositories ◽

Fine Grained ◽

Distributed Data Processing ◽

Trade Offs

AbstractWith the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However, while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the potential of today's Big Data repositories can be exploited using traditional batch-oriented approaches as the value of data often decays quickly and high latency becomes unacceptable in some applications. In the last couple of years, several distributed data processing systems have emerged that deviate from the batch-oriented approach and tackle data items as they arrive, thus acknowledging the growing importance of timeliness and velocity in Big Data analytics.In this article, we give an overview over the state of the art of stream processors for low-latency Big Data analytics and conduct a qualitative comparison of the most popular contenders, namely Storm and its abstraction layer Trident, Samza and Spark Streaming. We describe their respective underlying rationales, the guarantees they provide and discuss the trade-offs that come with selecting one of them for a particular task.

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Big Data Analytics for Network Congestion Management Using Flow-Based Analysis

Advances in Intelligent Systems and Computing - Artificial Intelligence and Evolutionary Computations in Engineering Systems ◽

10.1007/978-81-322-2656-7_41 ◽

2016 ◽

pp. 453-458

Author(s):

Yasmeen Arafath ◽

R. Ranjith Kumar

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Congestion Management ◽

Network Congestion

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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