Despite these difficulties, breeding programs generate large volumes of phenotypic data, which poses a challenge for databases to efficiently store, query and analyze these data.
Inference for dynamic systems and conformational sampling for protein folding are two problems motivated by applied data, which pose computational challenges from a statistical perspective.
However, traditional wireless technologies cannot be directly applied to VANETs because of several inherent features like high mobility, restriction of road setup, frequent topology variations, enough energy storage, failed network links, and timely data communication, which pose a major challenge in the routing of information [6 10].
However, recent studies have revealed that the network elements consume 10 20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance.
In addition, certain clinical conditions, such as those that are rare and acute in nature, may never accumulate sufficient data over time, which poses a challenge to establishing accurate and informative statistical models.
Additionally, standard machine learning algorithms tend to break down and become ineffective when dealing with data of this size, which poses a problem when trying to apply these algorithms for review spam detection [4].
Pseudonymised data - A key linking the data to an identifiable individual exists, which poses a further risk beyond re-identification through reverse engineering.
In order to cover all the desired emotions, a significant amount of data is required, which poses an additional time-consuming data collection challenge.
Typically, GBS data contain high proportions of missing genotypes, which poses a real challenge.
