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Giti Javidi, Ehsan Sheybani, and Lila Rajabion present a convincing argument for redistributing the cloud. They suggest that the cost and time associated with transmitting data to the cloud, processing it there, and returning the results back to the IoT devices are critical. Fog computing both enhances and complements the cloud by bringing the processing closer to a cluster of IoT devices, resulting in faster analytics.
Clustering large amounts of unstructured data into relatively smaller chunks based on some similarity is at the crux of unstructured data analytics. Here’s where “swarm intelligence” can play a role — an application that drives neural networks for clustering unstructured data. The authors offer an excellent example of swarm intelligence via a model that learns to classify Web documents. We can easily apply this same algorithm to business, medicine, defense, and supply chains, to name a few other areas.
The authors focus on the challenges of security in the Agile deployment of big data applications in the cloud. Security issues can make or break the deployment of otherwise complete solutions in the cloud. The authors begin by introducing the topic of “microsegmentation” — which allows “public cloud-based infrastructure as a service (IaaS) providers to offer software-centric or software-only solutions.” The value of this discussion to business lies in the opportunity to quickly deploy secure models for end-user consumption.
This article touches upon a crucial challenge in the big data space: identifying the right questions for it. As data continues to explode, not only are businesses struggling to find answers to business questions, they often cannot even determine what questions to ask of their data. The authors discuss a practical experiment on classifying genetic data using a colored de Bruijn graph and show the application of this technique in the business world.
Vince Kellen sheds light on the significant impact of big data in his domain of expertise: education. He then examines how universities can use big data to improve teaching and learning and describes the challenges involved. He concludes by offering suggestions for strategy development as universities incrementally apply big data to their core enterprise, education.
Santhosh Ravindran and Fiona Nah explore utilizing prescriptive analytics to enhance business processes, focusing on ML algorithms. While clarifying how “the foresight offered by prescriptive analytics enables organizations to make major decisions in a short time frame with greater accuracy,” Ravindran and Nah rightfully direct our attention on the importance of embedding such analytics carefully and iteratively within business processes.
The authors offer some insightful thoughts on how machine learning can help extract value from big data. The authors begin their discussion by “illustrating the limitations of current methods and human intellect across the 4 Vs (volume, velocity, variety, and veracity)” and the barriers that can block the extraction of the 5th V (value). Their article further highlights some excellent ML use cases at cutting-edge companies.
This issue of Cutter Business Technology Journal explores various angles to big data with a focus on the trends in predictive analytics, ML, IoT, and the cloud.