# Big Data Analysis and Visualization: Challenges and Solutions
## 2 Challenges and Solutions for Big Data Analysis and Visualization
### Big Data Applications  
One of the application areas for big data analysis and visualization is in marine wildlife. Marine resources are generally valuable assets that are protected from _illegal_, _unreported_, _and unregulated_ (_IIUI_) _fishing and overfishing_. To detect IIUI fishing and overfishing, [PERSON] and [PERSON] [8] presented a convolutional neural network (CNN)-based method to identify fishing equipment--for the fishing ships in operation--from an automatic identification system (AIS)-based trajectory data of fishing ships. This deep learning-based fishing gear-type identification method identifies six fishing gear type groups (namely, drift gill net, longline, purse seine, single trawl, stow net, and traps) from AIS-based ship movement data and environmental data. More specifically, the method preprocesses and handles messages with different messaging interval lengths, contaminated messages, and missing messages for data trajectories. It uses a sliding window-based data slicing technique to capture complicated dynamic patterns in trajectories of fishing gear types and for generating the training data set. Then, the prediction module of this CNN-based method suggests a putative fishing gear type with features that can be extracted from the input trajectory data by the two feature extraction modules (which extract features from fishing ship movement data and from environmental data, respectively). The experimental results of a real-life trajectory data set containing 1380 fishing ships collected over a year show that this CNN-based method led to a higher daily performance index (DPI)--i.e., higher accuracy--than the existing support vector machine (SVM)-based models in identifying the types of fishing gears. In addition to protecting marine wildlife from IUU fishing and overfishing, it is also important to protect Internet users from harmful online contents. For instance, [PERSON] and [PERSON] [9] presented a multimodal stacking scheme to detect harmful online pornographic content. The stacking scheme uses a bi-directional recurrent neural network (RNN) with a 16-layered dilated convolutional network called VGG-16 (from Oxford's Visual Geometry Group (VGG)) to implicitly express the signal change patterns over time within each input and to extract the implicative auditory and visual features. It trains an audio classifier by using only the implicative auditory features; it also trains a video classifier by using only the implicative visual features. Moreover, it trains a fusion classifier by using both the auditory and visual features. Afterwards, these three classifiers are stacked--in a serial order of the fusion classifier, video classifier, and audio classifier--in the ensemble. Consequently, the resulting stack reduces the false negative errors scheme for quick online detections. The experimental results show that this stacking scheme led to a higher true positive rate and accuracy, a lower false negative rate, and a shorter detection time than an existing detection scheme. A third big data analysis and visualization application is career choice prediction. Career choice plays an important role in life planning of university/college students. Traditionally, professional career appraisers made use of questionnaires or diagnoses in quantifying potential factors that influence career choices. However, given the diversity of goals and ideas of each student, this traditional approach can be changing for proper forecast of their career choices. Observing that the behavioral data of students may reflect their career choices, [PERSON] et al. [10] presented a model called ACCBOX (i.e., Approach Cluster Centers Based On eXtreme gradient boosting (XGBoost)) to mine the potential behavior of college students from campus big data and predict their career choices. The experimental results from 13 million behavioral data records of over 4000 students show that ACCBOX outperformed existing prediction models with a higher accuracy, F1 score, precision, and recall.