João Carlos de A. R. Gonçalves

Business process mining from group stories

Process Modeling has been adopted by organizations in various contexts, such as analysis for best practices, system design and Information Technology architecture. Despite its importance, modeling process is still costly and complex. The process mining technique extracts information from systems event log, however, a business process incorporates human activities, which will never be present in logs. This paper presents an approach that explores the narrative technique associated with text mining and natural language interpretation for automatic generation of process models.

A case study on designing business processes based on collaborative and mining approaches

Companies invest a significant amount of time and resources to discover and represent how they work into business processes models. However, traditional process mapping has been done in an ad hoc manner and tends to be resource-intensive and time-consuming due to the informal and ambiguous collection of process information. The Story Mining Method aims to address those problems by the union of free-form narratives about processes and the usage of Text Mining and Natural Language techniques for text translation into process models. This paper presents a case study of the method, detailing its implementation as well as major issues found on a practical scenario within an organization.

Analysis of Knowledge-Intensive Processes Focused on the Communication Perspective

Knowledge-intensive Processes (KiPs) are unstructured processes that demand an understanding beyond control flow and data. Being knowledge-centric and varying at each instance, KiPs demand new perspectives for proper process analysis. Most KiPs have strong collaboration characteristics, where interactions among participants are crucial to achieve process goals. Process participants perform activities and collaborate with each other, driven by their Beliefs, Desires and Intentions; therefore, the analysis of these elements is vital to the correct understanding, modeling and execution of a KiP. This research proposes a method based on Speech Act Theory and Process Mining to discover the flow of speech acts related to Beliefs, Desires and Intentions from event logs, and shows how this relation fosters process performance analysis. The approach was evaluated through a case study in a real life scenario, and results showed that relevant insights in forms of speech acts flow patterns were discovered and related to performance issues of the KiP.

Discovering Intentions and Desires Within Knowledge Intensive Processes

Traditional approaches for process modeling usually comprise the control flow of well-structured activities that an organization performs in order to achieve its objectives. However, many processes involving decision-making and creativity do not follow a well-structured flow of activities, having rather a more ad-hoc nature at each instance. Knowledge Intensive Processes (KIP) is an example of this kind of process. It is difficult to gather information about a KIP and create a representative model, since it might vary from instance to instance due to decisions made by its participants. The contextual information of each activity - as well as the desires and intentions of the participants - are vital to the complete understanding of the process itself. In this paper, we propose a method to extract intentions and desires from KIP participants using NLP Techniques and social media content, as well as exploring its possibilities on a real case study using Twitter.

Uncovering the Hidden Co-evolution in the Work History of Software Projects

The monitoring of project-oriented business processes is difficult because their state is fragmented and represented by the progress of different documents and artifacts being worked on. This observation holds in particular for software development projects in which various developers work on different parts of the software concurrently. Prior contributions in this area have proposed a plethora of techniques to analyze and visualize the current state of the software artifact as a product. It is surprising that these techniques are missing to provide insights into what types of work are conducted at different stages of the project and how they are dependent upon another. In this paper, we address this research gap and present a technique for mining the software process including dependencies between artifacts. Our evaluation of various open-source projects demonstrates the applicability of our technique.

K2Chat: Uma Ferramenta de Bate-Papo com Suporte ao Registro e Indexação das Sessões

Chat tools have been used by a wide range of companies as a communication channel with customers and for the promotion of products and services. Workgroups often use textual chats to communicate in the workplace. Such interactions are rich in knowledge transfer and have been under used in organizational memory. The K2Chat, a textual chat groupware, has been developed to structure the conversation by classifying and relating messages and highlighting keywords for indexation.

Speech Acts Featuring Decisions in Knowledge-Intensive Processes

A Knowledge-Intensive Process (KiP) is specified as a composition of a set of prospective activities (events) whose execution contributes to achieving a goal and whose control-flow, at the instance level, typically presents a high degree of variability among its several past executions. Variability is a consequence of a combination of decision points and informal interactions among participants on collaborative and innovative activities. These interactions may occur through message exchange, thus understanding the interplay of illocutionary acts within messages may bring insights on how participants make decisions. In this paper, we propose mechanisms that identify speech acts in the set of messages that mostly lead to decision points in a KiP providing an understanding of conversational patterns. We empirically evaluate our proposal considering data from a company that provides IT services to several customers.

The Role of Context Within the Interactions of Knowledge Intensive Processes

With the evolution of the Business Process Management (BPM) research field, many researchers started to perceive a specific type of process as being critical to organizations, the so-called Knowledge-intensive Processes (KiP), characterized by a dynamic and unstable control-flow and complex activities that change frequently at run-time. This research proposes an approach based on the characterization of a KIP in terms of its interactions and the critical role of context during its execution. We argue that a detailed description about their precise conceptualization and how they influence or determine the execution of a KIP is still missing in the literature of this area. The analysis of the interactions that occur during the executions of the activities of a KiP, as well as their associated elements (such as context and speech acts), is vital to its correct understanding, modeling and execution.

Collaborative narratives for business rule elicitation

Business rules are a form of knowledge that expresses policies being practiced throughout the organization. The set of business rules represents part of important collective knowledge, therefore enterprises are likely to have them explicited and documented. The Story Mining Method was proposed previously for the extraction of business process elements from collaborative narratives; however, process models and rules are complementary forms of knowledge that represent organizational knowledge as a whole. This paper presents an extension of the Story Mining method and the collaborative supporting application, expanding the scope of the method for business rules extraction.