# Big Data Collection in Large-Scale Wireless Sensor Networks
## 3 Overview of Big Data
### Big Data Challenges
#### 3.5.1 Data Acquisition and Storage  
Data acquisition refers to data acquisition, storage, and preprocessing. Big Data are produced continuously, and volume is growing at an exponential rate. At this speed, the acquisition of these data is a real challenge for researchers and industrialists. Indeed, the nature of Big Data already presents several challenges [73]:  
* _Heterogeneity._ In general, data-mining algorithms and machine learning expect homogeneous data, and cannot understand the fine distinction between them. IT systems work the fastest if they can store multiple items that are the same size and structure. Thus, Big Data must be carefully structured as a first step in their acquisition process [74].
* _Scale._ The first thing everyone thinks about Big Data is size. The management of large volumes of data and high growth has been a major problem for many decades. The volume of data is increasing faster than IT resources. Unfortunately, parallel data-processing techniques that were useful in the past for processing data between nodes do not apply directly to intranode parallelism, since the architecture is very different.  
Figure 13: Big Data challenges.  
* _Delay._ The larger the dataset to be processed is, the longer it takes to analyze it. Designing a system that effectively treats size is also likely to result in a system capable to more quickly process a given dataset size. However, it is not only this speed that is generally heard when we talk about Velocity in the context of Big Data [75]. There are many situations in which the result of the analysis is immediately required. Given a broad dataset, it is often essential to find elements that meet a specific criterion. The analysis of all the data to find the appropriate elements is obviously impracticable [76].
* _Privacy and Security._ One of the key value propositions of Big Data is access to data from multiple and diverse domains. Security and privacy play a very important role in Big Data research and technology. In areas such as social media and health information, more data are collected on individuals. It is therefore feared that some organizations will know too much about individuals, developing algorithms that randomize personal data into a large dataset problem [77].  
In terms of data storage, relational databases remain the reference, but these systems find their limit against Big Data. These widely used tools guarantee the maintenance of ACID (Atomicity, Coherence, Isolation, and Durability) properties. The management of large volumes of data, especially in the context of data warehouses, always faithful to the relational model, solutions like Teradata rely on the distribution of data on different disks, allowing a parallelization of query execution. However, these solutions do not allow massive data management beyond a certain volume. Thus, different new solutions have emerged. All of these solutions rely on the distributed (partitioned) storage of cluster data. Given that [PERSON]'s theorem is quite easily proven, no distributed system can ensure consistency, availability, and the possibility of being partitioned. The consequence is that, in these new storage solutions, it is not possible to provide the ACID properties, and a loosening of these properties is necessary.