# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 9 Summary  
The literature discussed in Section 5 presents an overview of the development of the indexing techniques proposed over the two decades. These techniques have been varied according to the nature of the data, the space and the nature of the structure as shown in the proposed taxonomy presented in Figures 6-9. The Tables 2-6, 8, 10, 12, 14 and 16 show that most of the structures studied have strong and weak sides.  
The Tables 2-6, show that data independent hash methods are suffering from the high cost of space and time, making them effective for small data with low-dimensions and inefficient for large data with high-dimensions. For satisfactory results with data independent methods, many hash tables or long hash codes are required, which also makes them less effective in practice. Concerning data dependent hash techniques, supervised and semi-supervised hash methods take into account labeling information for training purposes, making them more effective than unsupervised hash methods because of the advantage of explicit semantic information in the data. On the other hand, they are much slower in terms of time/effort due to the higher cost of the training process, unlike unsupervised hash methods, which do not require any labelled data. Our study shows that unsupervised, supervised and semi-supervised hashes need new solutions to solve the problem of optimization to learn hash functions and hash codes mainly when data dimensionality increases. Due to the advantage of the higher power of the representation of the characteristics of the deep network, the deep hash achieves better performance than other hash methods. However, the non-optimal minimization of the quantization error is a major drawback that needs to redress [282; 283].  
The Bitmap index, in general, is intended for the optimization of search and retrieval of data with low variant, low cardinality and small distinct values (Family, Human, True,False, etc.) and not for complex data where no particular property related to the nature of the data can be exploited.  
The advantages and disadvantages/challenges of data indexing techniques in multidimensional and metric space are summarized in Tables 8, 10, 12, 14 and 16, where all approaches are discussed in Section 5. During the analysis of these indexing techniques, many factors influenced their operational and performance and proved their feasibility in certain applications.  
One of the most important of these factors is the degree of data overlap. When distances tend to be very close to each other, objects become almost indistinguishable and cannot be clearly partitioned, which leads to a lot of overlap. A high degree of overlap leads to insufficient research output in terms of time, resources and quality because of the complete analysis of all data. This is confirmed by several researches, among them [229]. A review of all the techniques, showing that the technique that addressed the problem of partition overlap suffered from the overhead problem which influences real-time applications and especially on big data and vice versa.  
The second major factor is the hardware. Despite all the advantages offered by the new high-performance computing such as cloud computing, fog computing and other emergence computations, there is not much work in the literature which adapts them. Knowing that It can provide many research opportunities to improve and develop more efficient indexing techniques (hashing, bitmap or tree). Which can take full advantage of the Graphics Processing Unit (GPU), Tensor Processing Unit (TPU) and Central Processing Unit (CPU) [64]. However, this does not mean that improving the performance of indexing structures depends only on increasing storage space and computing power.  
At the end of this analysis, it can be concluded that the universal indexing technique that can manage arbitrary datasets has not yet been found and the realisation of this one which satisfies all the constraints and requirements of Big Data indexing mentioned in Section 4 is quasi-impossible.