scholarly journals Verification and Validation Techniques for Streaming Big Data Analytics in Internet ofThings Environment

Author(s):  
Sudeep Tanwar ◽  
Aparna Kumari ◽  
Sudhanshu Tyagi ◽  
Neeraj Kumar

A strategy of examining immense dimensions of structured, un-structured, Semi-Structured data sets is referred as Big data Analytics. Streaming Big Data refers to data generated continuously from number of data sources like Internet-of-Things (IoT) devices, mobile applications, Embedded Sensors, web clicks and many more are needed to be store, processed and analyzed in a tiny interval of time in order to extract meaningful insights and take proper decisions in a timely fashion as the necessity arises. However analyzing streaming big data (continuous flow or unbounded data) is a very challenging problem. Continuous data streams have become essential prerequisite for numerous industrial and scientific applications, the current existing technology Hadoop-MapReduce is not appropriate for stream processing of big data. This paper discusses the challenges and benefits of streaming big data along with its architecture, and focuses on different open source streaming processing platforms that are existed to process the huge data at a high speed


Author(s):  
Sornalakshmi Krishnan ◽  
Kayalvizhi Jayavel

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.


2022 ◽  
pp. 722-757
Author(s):  
Sornalakshmi Krishnan ◽  
Kayalvizhi Jayavel

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.


2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke

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.


2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma

2017 ◽  
Vol 49 (004) ◽  
pp. 825--830
Author(s):  
A. AHMED ◽  
R.U. AMIN ◽  
M. R. ANJUM ◽  
I. ULLAH ◽  
I. S. BAJWA

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