Automatic Process Model Discovery from Textual Methodologies: An Archaeology Case Study

International audience— Process mining has been successfully used in automatic knowledge discovery and in providing guidance or support. The known process mining approaches rely on processes being executed with the help of information systems thus enabling the automatic capture of process traces as event logs. However, there are many other fields such as Humanities, Social Sciences and Medicine where workers follow processes and log their execution manually in textual forms instead. The problem we tackle in this paper is mining process instance models from unstructured, text-based process traces. Using natural language processing with a focus on the verb semantics, we created a novel unsupervised technique TextProcessMiner that discovers process instance models in two steps: 1.ActivityMiner mines the process activities; 2.ActivityRelationshipMiner mines the sequence, parallelism and mutual exclusion relationships between activities. We employed technical action research through which we validated and preliminarily evaluated our proposed technique in an Archaeology case. The results are very satisfactory with 88% correctly discovered activities in the log and a process instance model that adequately reflected the original process. Moreover, the technique we created emerged as domain independent

Évaluation du concept de famille de méthodes et de son application aux méthodes agiles

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Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning

International audienceUnderstanding people's goals is a challenging issue that is met in many different areas such as security, sales, information retrieval, etc. Intention Mining aims at uncovering intentions from observations of actual activities. While most Intention Mining techniques proposed so far focus on mining individual intentions to analyze web engine queries, this paper proposes a generic technique to mine intentions from activity traces. The proposed technique relies on supervised learning and generates intentional models specified with the Map formalism. The originality of the contribution lies in the demonstration that it is actually possible to reverse engineer the underlying intentional plans built by people when in action, and specify them in models e.g. with intentions at different levels, dependencies, links with other concepts, etc. After an introduction on intention mining, the paper presents the Supervised Map Miner Method and reports two controlled experiments that were undertaken to evaluate precision, recall and F-Score. The results are promising since the authors were able to find the intentions underlying the activities as well as the corresponding map process model with satisfying accuracy, efficiency and performance

Selected Topics on Research Challenges in Information Science: Editorial Introduction to Issue 17 of CSIMQ

This thematic issue of the Complex Systems Informatics and Modeling Quarterly journal is dedicated to Information Science. It is a selection of the extended best papers presented at the 12th edition of the International IEEE Conference on Research Challenges in Information Science (RCIS’2018). The articles presented in this thematic issue contain at least 30% new material compared to the initial papers.The RCIS conference covers a wide spectrum of topics in the information science field: information search and discovery, requirement engineering, product lines, smart cities and Internet of Things, business processes, recommendation and prediction, security, social computing and Social Network Analysis, Human-Computer Interaction and systems engineering.After an additional reviewing process, the articles which have been finally selected present contributions of different types to the field of information science: models, architectures, frameworks, and surveys

Intentional Process Modeling of Statistical Analysis Methods

International audienceEach Humanities researcher has its own way to deal with data (collection, coding, analysis and interpretation). All these specific ways of working are not shared-each researcher is reinventing his/her own method while analyzing data without any previous experience. Nevertheless, developing and sharing these methods should be useful to the research community and students in Humanities. Moreover, a lot of data analysis is done with statistical analysis methods, to find correlations between events, to make predictions or assumptions on facts or artifacts; and the use of one method or another requires good statistical knowledge. We conducted interviews among archaeologists and historians to understand their ways of working and collected information on the methods they used. We then built a method to guide researchers in using statistical analysis methods