# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 2 Related Work
### Frameworks and Methodologies  
[PERSON] et al. [6] defined knowledge discovery in databases (KDD) as a process that identifies useful, valuable, and understandable patterns in data. They addressed how algorithms can be scaled to work well on massive datasets and how they can be visualized and interpreted. In addition, they also included how human-machine interactions can be modeled and supported. In the KDD framework, data mining is a singular step within the process, where properly preprocessed data are transformed into patterns that yield valuable knowledge. However, it is important to note that several methodologies developed after KDD, such as CRISP-DM [7] and SEMMA [8], use data mining as a synonym for KDD. CRISP-DM is widely recognized as the methodology for most proposals for data mining process models as data science has evolved. This methodology restates the steps of the original KDD proposal into business understanding, data understanding, data preparation, modeling, evaluation, and implementation. On the other hand, SEMMA methodology was proposed by SAS [8] and stands for Sample, Explore, Modify, Model, and Assess. This methodology focuses primarily on the technical aspects of data mining product development and is strongly tied to the SAS data miner. A significant difference between SEMMA and previous methodologies is the prerequisite of having and understanding the business requirements and databases. CRISP-DM, SEMMA, and other related methodologies such as ASUM-DM [10], CASP-DM [11], FMDS [12], and TDSP [13] are based on a goal-oriented perspective, dealing with processes, tasks, and roles. These methodologies were developed mainly focused on data mining processes with relatively clear business objectives. Furthermore, they primarily deal with structured data, which may originate from flat files or be obtained through querying relational databases. However, currently in the context of big data and data science, the great diversity of information sources, the amount of data, the heterogeneity of the data, and the complexity of the problems represent a challenge for classical methodologies. In recent years, new proposals have emerged to bridge the gap between classical methodologies and the complexity of current problems. [PERSON] et al. [14] propose a data trajectory model (DST) and categorize data science projects into goal-directed, exploratory, and data management. Their model extends CRISP-DM, including six exploratory activities focused on the goal, data source, data value, results, narrative, and product. Although DST is a proposal that may reduce the gap between data mining methodologies and data science projects, the proposal is limited to proposing exploratory activities for tabular data, which it describes in its use cases. [PERSON] et al. [15] proposed a process model for developing machine learning applications. Regarding frameworks, various proposals have emerged focused on different phases of knowledge discovery in graphs, for example, business understanding [16], data modeling [17], graph mining [18; 19; 20], graph visualization [21; 22], and evaluation [23; 24]. Some proposals are focused on how to model realistic graphs that match the patterns found in real-world graphs. [PERSON] et al. [25] proposed a graph mining framework that captures entities and relations between entities from different data sources. Their framework offers a comprehensive approach with five modules covering graph preprocessing, graph database, dense substructure extraction, frequent substructure discovery, and graph visualization. However, it lacks adequate addressing of the business goals and the evaluation process within the scope. [PERSON] et al. [26] presented a framework with two modeling components: context modeling and analytics design alternatives modeling. The first is to justify the need for predictive analytics within the organizational context.