# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 5 Existing Indexing Techniques
### Multidimensional Indexing Techniques
#### 5.1.2 Tree-Based Technique  
Multidimensional data has a number of dimensions. In metric spaces, this notion disappears, not only does it disappear because the object is only considered as a whole and not as a set of components, but also because some objects are naturally without any perceptible dimension. This is the case of a sequence of characters, a set of elements of any description, a graph, etc. This section presents some tree indexing techniques. Reference books or syntheses on the subject have been proposed by several other authors. In addition, some authors have considered multidimensional indexing techniques as unsupervised classification methods. It is important to note that in a classification, the classes are not of the same cardinal and that, in a hierarchical classification, not all leaf classes are located at the same depth. Indexing techniques can be classified according to two main approaches:  
**No Partitioning of Space (Partitioning Data):** The primary idea of data partitioning consists of creating data packets or clusters, also called \"inclusion forms\". In the literature, there three data-partitioning methods: those whose Minimum Bounding Regions (MBR) are hyper-cubes, those whose MBRs are hyper-spheres and those whose MBRs are hyper-plane [124]. The main representative techniques of this approach include the B-tree [125] (and its and its variants: B\({}^{+}\)-tree [126], B\({}^{*}\)-tree, T-tree [127], UB-tree [128], BUB-tree [129], etc. ), R-tree [130], the X-tree [131] and the SR-tree [132]. R-tree is a hierarchical data structures based on B\({}^{+}\)-tree, where it is used to index spatio-temporal data of n-dimensions. R-tree generates several small minimum bounding rectangles (MBR) [130] to reduce dead spaces. R-tree is a balanced [133] and dynamic structure [130] that is very efficient for range requests [134]. The disadvantages of the R-tree structure reside in the increase in space, time and complexity of the calculations because of overlapping multiple MBR regions [135]. Because of the overlapping, R-tree is inefficient for point location queries which can lead to a degradation of the performance of the search process [136]. Several extensions have been proposed based on the R-tree structure to \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 Convolutional Neural Networks for Text Hashing & [114] & \({}^{\bullet}\) Better performance than traditional hashing methods & \({}^{\bullet}\) Unsuitable for all real-world domain databases \({}^{\bullet}\) Not sufficient to processes high dimensional data \({}^{\bullet}\) Performance decreases as the dimensionality of the data increases \\ \hline Hash coding with Deep Neural Net & [115] & \({}^{\bullet}\) Better performance \({}^{\bullet}\) Good search precision rate & \({}^{\bullet}\) Demand pairwise similarity labels \({}^{\bullet}\) Need a more complex configuration \({}^{\bullet}\) Required labeled data and considerable human efforts \\ \hline Bit-Scalable Deep Hashing & [116] & \({}^{\bullet}\) Better performance than traditional hashing methods & \({}^{\bullet}\) Required labeled data and considerable human efforts \({}^{\bullet}\) Learning the hash function only for query points \({}^{\bullet}\) High search precision rate & \({}^{\bullet}\) Performance decreases as the dimensionality of the data increases \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of advantage and disadvantage of deep hashing techniques. address the weaknesses of this structure mentioned above. Among these extensions, it can be noted: R\({}^{+}\)-tree [137], R\({}^{*}\)-tree [138], Hilbert R-tree [139] and SS-tree [140]. X-tree (eXtended node-tree) [131] is an R-tree based structure developed to prevent overlap between MBRs through the new proposed node type. These nodes are extended nodes of variable size called Super-nodes (eXtended node).