# 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
**Semi-Supervised hashing:** Due to complexities of the exhaustive search of data labels in the database, semi-supervised hashing methods can use hash functions capable of training on two types of data, whether labeled or unlabeled data (partially labeled). In other words, semi-supervised hashing is a combination of unsupervised and supervised hashing [95]. The goal of the semi-supervised hashing method is to minimize the \begin{table}
\begin{tabular}{p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt}} \hline \hline
**Proposition** & **Refs** & **Advantages** & **Disadvantages** \\ \hline Minimal Loss Hashing & [99] & • Efficient and adapts well to long code lengths & • Training speed very slow Difficult to optimize & • Difficulty of finding the labeling of all data in the database \\ \hline Linear Discriminant Analysis Hash & [97] & • Effective compact hashing & • Loss memory consumption and calculation cost & • Slower because of the extraction of SIFT descriptors & • Much slower in terms of time and effort compared to unsupervised techniques \\ \hline Kernel Based Supervised Hashing & [96] & • Efficient hash functions & • Not sufficient for high-dimensional descriptors & • Much slower in terms of time and effort compared to unsupervised techniques \\ \hline Fast Supervised Hashing & [91] & • Suboptimal & Not use all training points due to the complexity & • Integium quality \\ \hline Fast Supervised Discrete Hashing & [100] & • Fast ANN search & • Integium quality & • Integium quality \\ \hline Supervised Discrete Hashing & [100] & • Highly efficient & • Very fast and high precision & • Require a significant degree of effort in large-scale applications \\ \hline Supervised Discrete Hashing & [98] & • Effective binary code learning & • Expensive training time & • Integium quality & • Integium quality \\ \hline Column sampling based discrete supervised hashing & [101] & • Capable to use all training data points & • Integium quality & • Integium quality \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of advantage and disadvantage of supervised hashing techniques. \begin{table}
\begin{tabular}{p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt}} \hline \hline
**Proposition** & **Refs** & **Advantages** & **Disadvantages** & **Disadvantages** \\ \hline Spectral Hashing & [78] & • Does not require any labeled data & • The assumption of a uniform distribution of data is usually not applicable in most cases of real-world data  
empirical error of labelled datasets and improve the binary encoding performance. Semi-supervised hashing methods are able to handle semantic similarity and dissimilarity between data [102] based on non-weighted distance and simple linear mapping. Representative methods include the Semi-Supervised Hashing (SSH) [102; 103], which is considered one of the most popular methods, along with Label-regularized Max-margin Partition (LAMP) [104], Semi-supervised Discriminant Hashing (SDH) [105], Bootstrap Sequential Projection Learning for Semi-supervised Nonlinear Hashing (Bootstrap-NSPLH) [106] and Semi-supervised Topology-Preserving Hashing (STPH) [107; 108]. Lately, [PERSON] and [PERSON] in [109] presented a new semi-supervised hashing named Semi-Supervised Multi-view Discrete Hashing (SSMDH). SSMDH minimizes the loss jointly when using relaxation on learning hashing codes on multi-view data. SSMDH reduces the loss of regression on a portion of the labeled samples, which increases the discrimination ability of the learned hash codes. Table 5 compares several semi-supervised hashing techniques. **Deep Hashing Methods:** Several studies have used deep learning techniques such as in the image classification [110; 111] and object detection [112; 113] methods.