After researchers had done specific studies to understand the human action and patterns of life including their environments, lives, behaviors, and motivations, they need to know how to present the obtained information and create a design that will result in successful representation. Individuals act differently from one another; thus, it is possible to collect different conversations and observations from various people to access human behavior and action. The user profiling and modeling are some of the examples that have been used as an evaluating system to predict the user’s behaviors for a given period. In user profiling, the personas represent various types and groups of the subject to enable the designers to develop appropriate solutions in reiterative processes. Although clear guidelines for using computer derive ad-hoc dynamic persona-types to classify life patterns and behaviors has not been established, an idea can be developed to guide on the same. The paper aims to reflect on the concept of using computer derived ad-hoc dynamic persona-types that relate to social networking, experience, and human actions.

Applied Behavioral Modeling

The construction of the profile and applied behavioral modeling for the users is based on studying their behavior patterns, cognitive features, and demographic data. Such features help provide a practical approach to represent the user’s interests and preferences. The focus of such an ad hoc dynamic involves assessing the interactions of the user with a system and do not deal with complex social networking like educational hypermedia or focus in serious games. Most of the computer-derived models are created to describe market behaviors, and they use personas or user models for representation. The model users provide a precise way to think and communicate about how the persona think, behave, what they wish to achieve and why (Fernandez-Llatas et al. 15434). The motivations and behaviors of the persona are observed and represented throughout the design process of a computer-derived model. The persona must be regarded with a considerable sophistication because using their stereotypes or generalization would not be enough to produce a clear representation. Besides, discretion and vigor have to be applied to identify the meaningful and significant patterns in the user’s behaviors and utilize the acquired information represent a broad cross-section of the persona. The dynamic information modeling focuses on personality and diverse computing experiences.

Further, utilizing the dynamic information of the user could be used to create a system that can adapt to the user dynamically. The ability of the system to adapt to the user is essential in identifying and highlighting potential users as well as predicting their behaviors. Thus, it is significant to understand that dynamics in modeling deals with lifestyles, ages, IT consumption, and space. The dynamic information is substantial, especially in the studies involving ceremonies interactions and social activities involving teenagers (Fernandez-Llatas et al. 15436). Ceremonies, especially the traditional ones provide typical social activities that enhance the sense of belonging to the members of the family, which are passed through generations. Thus, the custom is inheritable, meaning it will be predictable in the future as the young people grow up to become the target audience of such traditional ceremonies. Therefore, if the designers of the computer-derived ad-hoc dynamic can identify such potential from the users and derive data from the right source, they can support some observations like cultural heritage.

Another suitable example used in computer-derived models is the social interactions among teenagers, mainly through technology usage. Studies have established that although the youths are actively using technology to look for new friends on social media platforms, the economic status can limit their modes of communication. Generally, teenagers have limited finances, and most of the IT products or media platforms offer such services at a certain fee (Fernandez-Llatas et al. 15434). However, the older teenagers can find financial freedom later in life and develop heightened attentiveness and dependence on the Internet; thus, making them the primary target audience of the IT products and consumption, which is a concept that can be applied to predict the near future.

Benefits of technologies such as graph database in sociology

Technological advances have developed beneficial programs that do not require static patterns to process event data, particularly that of human behaviors and actions. For instance, the graph database has been used for data storage and representation. The key concept of this database is the graph, edge or a relationship of the observations and behaviors that relate the data items in storage directly. The relationship represented in the graph database allows the stored data to be linked or combined it to create a successful representation (Huang et al. 3). Besides, process mining technology enable the sociologists to facilitate workflow interpretation from certain event records and reports while conducting studies. This technology interprets graphs that are understandable by the experts studying human behavior patterns using the routine actions recorder by ambient intelligence environments. Thus, it is easier for the experts to comprehend the process of human action as well as deduce a comparison using the previous inferences to identify particular behavior patterns or changes.


The process of analyzing human behavior patterns is extensively used for several research fields, especially in sociology. Most sociologists consider the use of IT and age as dynamic attributes of the user’s profile while conducting a study to classify behaviors and pattern of life. The applied user’ profiles and applied behavioral modeling should reflect their changes in hypermedia experience as well as behavioral changes based on demographic settings and interests. Thus, the research design using computer derived ad-hoc dynamic persona-types to classify behaviors and pattern of life has to consider the anticipated changes in lifestyle, age, IT consumption, and economic status of the user.


Fernández-Llatas, Carlos, et al. “Process mining for individualized behavior modeling using wireless tracking in nursing homes.” Sensors 13.11 (2013): 15434-15451.

Huang, Ko-Hsun, Yi-Shin Deng, and Ming-Chuen Chuang. “Static and dynamic user portraits.” Advances in Human-Computer Interaction 2012 (2012): 2.