Quantified self is self-knowledge through self-tracking with technology. In this aspect, one can collect daily activities data on his own in terms of inputs, states, and performance. For example, input means food consumption or quality of surrounding air, states means mood or blood pressure, and performance means mental or physical condition. To collect these data, we can use wearable sensors and life logging. Quantified self-process allows individuals to quantify biometrics that they never knew existed, as well as make data collection cheaper and more convenient. One can track their insulin and cortisol levels and sequence DNA. Using quantified self data, one can be cautious about one's overall health, diet, and level of physical activity.
These days, wearing self-tracking gadgets is rapidly increasing. If we pooled the quantified self-data of a specific group of people, we can apply predictive algorithms on this data to diagnose patients in that location. That means quantified self data is very useful in certain medication contexts.
In the following screenshot, we can see some electronics gadgets that gather quantitative data:
Interaction with the outside world is highly important in data analysis. Using sensors like Radio-Frequency Identification (RFID) or a smartphone to scan a QR code (Quick Response) code are easy ways of interacting directly with the customer, making recommendations, and analyzing consumer trends.
On the other hand, people are using their smartphones all the time, using their cameras as a tool. In Chapter 5, Similarity-Based Image Retrieval, we will use these digital images to perform a search by image. This can be used, for example, in face recognition or for finding recommendations of a restaurant just by taking a picture of the front door.
This interaction with the real world can give you a competitive advantage and a real-time data source directly from the customer.
Nowadays, the Internet brings people together in many ways (that is, using social media); for example, Facebook, Twitter, LinkedIn, and so on. Using these social networks, users are working, playing, socializing online, and demonstrating new forms of collaboration and more. Social networks play a crucial role in reshaping business models and opening up numerous possibilities of studying human interaction and collective behavior.
In fact, if we intended to understand how to identify key individuals in social systems, we can generate models using analytical techniques on social network data and extract the information mentioned previously. This process is called Social Network Analysis (SNA).
Formally, the SNA performs the analysis of social relationships in terms of network theory, with nodes representing individuals and ties representing relationships between the individuals. Social networks create groups of related individuals (friendships) based on different aspects of their interaction. We can find out important information such as hobbies (for product recommendation) or who has the most influential opinion in a group (centrality). We will present in Chapter 10, Working with Social Graphs, a project, Who is your closest friend?, and we will show a solution for Twitter clustering.
Social networks are strongly connected, and these connections are often asymmetric. This makes SNA computationally expensive, and so it needs to be addressed with high-performance solutions that are less statistical and more algorithmic. The visualization of a social network can help us gain a good insight into how people are connected. The exploration of a graph is done through displaying nodes and ties in various colors, sizes, and distributions. D3.js has animation capabilities that enable us to visualize a social graph with interactive animations. These help us to simulate behaviors like information diffusion or the distance between nodes.
Facebook processes more than 500 TB of data daily (images, text, video, likes, and relationships), and this amount of data needs non-conventional treatment like NoSQL databases and MapReduce frameworks. In this book, we will work with MongoDB, a document-based NoSQL database, which also has great functions for aggregations and MapReduce processing.