# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 5 Existing Indexing Techniques
### Multidimensional Indexing Techniques
#### 5.1.1. Hashing-Based Technique
In addition, some hashing methods available in the literature have focused on the adaptation of deep learning techniques and in particular Deep Artificial neural Networks (DANS) to take advantage of deep learning, such as Convolutional Neural Networks for Text Hashing (CNNTH) [114], Simultaneous Feature Learning and Hash Coding with Deep Neural Networks [115], Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification (DRSDH) [116], Asymmetric Deep Supervised Hashing (ADSH) [117] and hashing as tie-aware learning to rank [118]. Due to the automatic learning ability of the deep learning methods, deep hashing methods have shown better performance than traditional hashing methods [119]. Hash methods that adapt to in-depth learning can be based on unsupervised or supervised learning, but most of these methods are supervised, with supervised information given with triplet labels [120]. [PERSON] and [PERSON] [117] proposed Asymmetric Deep Supervised Hashing (ADSH) for large-scale nearest neighbor search. ADSH learns a deep hash function only for query points, while the hash codes for database points are directly learned to reduce the training time complexity. Table 6 below shows a comparison of some key advantages and drawbacks of deep hashing techniques. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Proposition** & **Refs** & **Advantages** & **Disadvantages** \\ \hline Semi-supervised Hashing & [102; 103] & \(\bullet\) Empirical Error Minimization \(\bullet\) Variance and independence of binary codes maximized & \\ \hline Label-regularized Max-margin Partition & [104] & \(\bullet\) High-quality hash functions & \(\bullet\) Not suitable for high dimensional data & \(\bullet\) Much slower in terms of time and effort compared to unsupervised techniques \\ \hline Semi-supervised Discriminant Hashing & [105] & \(\bullet\) Good separation between data labeled in different classes & \\ \hline Bootstrap-NSPLH & [106] & \(\bullet\) Balanced partitioning of data points \(\bullet\) Higher performance & \(\bullet\) Expensive training time \(\bullet\) Require storage space and a large amount of computation & \\ \hline Semi-supervised multi-view discrete hashing & [109] & \(\bullet\) Minimizes the loss jointly on multi-view features when using relaxation on learning hashing codes & \(\bullet\) Not suitable for high dimensional data \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of advantage and disadvantage of semi-supervised hashing techniques. Between unsupervised hashing and (semi-)supervised hashing, the most significant difference is the availability of label information for learning hash functions [121]. Compared to unsupervised hash methods, supervised methods are much slower in terms of time and effort due to the overload of the training process and due to the absence of label information. Thus, the unsupervised methods have a potential value for practical applications as they do not require any labeled information [122; 123]. On the other hand, supervised techniques take into account the advantage of explicit semantic labels of the data, which provides a higher efficiency than unsupervised hashing techniques [64]. In general, to achieve satisfactory performance with data independent methods, many hash tables or long hash codes are required, which often makes them less effective in practice than data dependent methods. For data dependent, hashing methods (unsupervised, supervised and semi-supervised hashing) are needed to new solutions to address the problem of optimization to learn hash functions and hash codes.