Process Variability: concepts, approaches and its application on a model of Cloud BPM

Business Process Management as a discipline has suffered several changes during the implementation of the execution and monitoring phases in the cloud model. Different strategies have been seen in terms of the implementation needed in order to gather information from the different nodes during process execution, and finally show the results seamlessly without the notion of a partitioned business process. Another aspect to introduce in this context is Process Variability, in terms of the changes suffered by a process model during its lifecycle, and how these changes affect the actual instances in execution. In terms of a cloud BPM implementation, Process Variability adds even more complexity during execution considering the different process portions, as well as during the gathering and monitoring phases. The main purpose of this work is to establish how the different aspects of a cloud BPM implementation with decomposed processes are affected by introducing concepts of Process Variability, both in execution as well as in the monitoring phase. To achieve this goal an analysis of some current bibliography and the main aspects of process variability management is accomplished.XIV Workshop de Ingeniería de Software (WIS).Red de Universidades con Carreras en Informática (RedUNCI

A Type-Based Blocking Technique for Efficient Entity Resolution over Large-Scale Data

In data integration, entity resolution is an important technique to improve data quality. Existing researches typically assume that the target dataset only contain string-type data and use single similarity metric. For larger high-dimensional dataset, redundant information needs to be verified using traditional blocking or windowing techniques. In this work, we propose a novel ER-resolving method using a hybrid approach, including type-based multiblocks, varying window size, and more flexible similarity metrics. In our new ER workflow, we reduce the searching space for entity pairs by the constraint of redundant attributes and matching likelihood. We develop a reference implementation of our proposed approach and validate its performance using real-life dataset from one Internet of Things project. We evaluate the data processing system using five standard metrics including effectiveness, efficiency, accuracy, recall, and precision. Experimental results indicate that the proposed approach could be a promising alternative for entity resolution and could be feasibly applied in real-world data cleaning for large datasets

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI