# 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  
This is a more popular technique in the field of multidimensional data indexing due to its ability to transform a data item into a low-dimensional representation (short code composed of a few bits) [63]. Hashing-based indexing structures are more efficient in terms of time and storage space [64] and can detect duplicate data in a large dataset [65]. There are many methods based on the hashing technique applied to several real applications, such as computer vision, information retrieval and analysis (e.g., images, videos, documents) [66]. According to Figure 7, hash-based indexing structures can be classified into two main streams: _data independent hashing_ and _data dependent hashing_ (or learning-based hashing). **Data Independent Hashing:** Among the data independent hashing methods, the Locality-Sensitive Hashing (LSH) developed by [PERSON] et al. [67] is the most popular in the literature. It allows retrieving the sufficient set of Approximate Nearest Neighbors (ANNs) in high dimensional space. One of the main criteria of the LSH techniques family is the hash function which returns with high probabilities, the same bit for close data points in the original space [68]. Since LSH's proposal, several variants have been proposed to improve the SLH method as: MultiProbe LSH [69,70], BayesLSH [71], Boosted LSH [72], Superbit LSH [73], Non-metric LSH [74], Kernelized LSH (KLSH) [75] and Asymmetric LSH (ALSH) [76]. However, LSH-based techniques suffer from problems of increasing storage costs and search time due to the long binary codes and high hash functions required when the recovery precision is improved [68]. In general, data-independent hash methods are well suited for small data, but they are not sufficient to handle large data. Table 2 shows a summary of the advantages and disadvantages of the above methods, as well as their challenges. **Data Dependent Hashing:** In the hash stream depending on the data, several methods have been proposed to overcome the problems and limitations of data independent hash methods. These methods are classified into three categories according to the degree of supervision, namely: (i) unsupervised hashing, (ii) supervised hashing, (iii) semi-supervised hashing, and (iii) deep hashing. Figure 7: Taxonomy of hashing-based indexing techniques. **Unsupervised Hashing:** For higher precision in the design of compact hash codes, unsupervised hashing methods aim to integrate data properties such as distributions and multiple structures [77]. Reference methods include spectral hashing [78], graph hashing [79, 80, 81], multiple hashing [82], spherical hashing [82, 83], shared hashing [84], manifold hashing [85, 86], etc. Recently, a novel unsupervised online hashing method for online image retrieval was proposed by [PERSON] et al. [87], called Online Dynamic Multi-View Hashing (ODMVH or DMVH) capable of adaptively increasing hash codes according to dynamic changes in the image. These hashing techniques also use multi-view features to achieve more efficient hashing performance. DMVH has limited performance because it is an unsupervised method and has not exploited any discriminative semantic information [88]. [PERSON] et al. [89] developed a novel unsupervised hashing approach, named Robust Discrete Spectral Hashing (RDSH) to facilitate large-scale semantic indexing of image data. RDSH can simultaneously learn discrete binary codes and robust hash functions in a unified model. Due to the difficulty of the latter, the authors included the offline process for the learning binary codes as well as the coding functions and the online procedure for indexing images with semantic annotations. Initially, the real value representation is learned from the original space of the entities using methods such as Spectral Hashing (SH). Then, the real representation is transformed into binary codes through binarization based on learning.