Browsing by Author "Pinheiro, Carlos"
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- A survey on association rule mining for enterprise architecture model discoveryPublication . Pinheiro, Carlos; Guerreiro, Sérgio; São Mamede, HenriqueAssociation Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predic- tive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suit- able for designing organizational structures. It uses view- points derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applica- tions, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it dis- cusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.
- Towards an ontology to enforce enterprise architecture miningPublication . Pinheiro, Carlos; Guerreiro, Sérgio; São Mamede, HenriqueEnterprise Architecture (EA) is a coherent set of principles, methods, and models that express the structure of an enterprise and its IT landscape. Ontologies help to define concepts and the relationships among these concepts that describe a domain of interest. EA mining uses data mining techniques to automate the EA modelling. This work presents an extensible ontology for EA mining focused on extracting architectural models that use logs from an API gateway as the data source. The Unified Foundational Ontology (UFO) provided the foundations of ontology and OntoUML, the ontology language. A hypothesized scenario using data structures close to the real is used to simulate the ontology application and validate its theoretical feasibility. This research aims to contribute to the Enterprise Architecture Management acknowledgement base.