Big Data: Issues and Challenges Moving Forward

By S. Kaisler, F. Armour, J. A. Espinosa, W. Money. Hawaii International Conference on System Sciences, 2013

953 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/a9a84242cf78a9b277fce869f8bbfa7bfacfbc38

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

Citations in the corpus

Big data analytics in smart grids: a review (2018, background): “The big data can be understood as amount of data beyond technology ’ s capability to store, manage and process efficiently in (Kaisler et al., 2012) as the data size increasing along with the evolvement of ICT technologies.”
Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives (2016, background): “It is estimated that roughly 635 years are needed to process 1 K Petabytes, assuming a processor expends 100 instructions on one block at 5 G (Kaisler et al., 2013).”
Social big data: Recent achievements and new challenges (2015, background): “to different aspects as knowledge representation, data management, data processing, data analysis, and data visualisation [17].”
Big Data: Survey, Technologies, Opportunities, and Challenges (2014, methodology): “Five common issues are volume, variety, velocity, value, and complexity according to [4, 12].”
The rise of "big data" on cloud computing: Review and open research issues (2015, background): “The rate at which new data are being generated is staggering [3].”
CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications (2016, methodology): “The data explosion problem is pushing the limits of the Data Mining technologies, as conﬁrmed in [21].”
The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations (2020, methodology): “As such, IoT data is often referred to as “Big” data, having volume, variety and velocity (Kaisler, Armour, Espinosa, & Money, 2013).”
A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions (2020, background): “omputational models with the highest level of abstraction. Apart from diversified sources and massive volume, the critical feature of big data is its dynamically changing data (real-time information) [116]. The big data processing systems are complex enough in handling the inherent complexity of the”
Big data analytics in supply chain management between 2010 and 2016: Insights to industries (2018, background): “It exceeds the space of technical ability of storage system, processing, managing, interpreting and visualizing of a traditional system (Kaisler et al., 2013).”
Critical analysis of Big Data challenges and analytical methods (2017, background): “Kaisler et al. (2013) also claim that data ownership presents a critical and continuing challenge, speci ﬁ cally in the social media context such as who owns the data on Facebook, Twitter or MySpace – are the users who update their status or tweet or have any account in these…”
Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study (2017, background): “…so accurate business analysis can be done; experimental analysis support for decision making processes; marketing segmentation based on customer and markets; automated and real-time analysis; product innovation with the use of sensors that monitor customer reactions etc. (Kaisler et al. 2013).”