Data Mining

Recently, combinatorial interaction strategies have a large spectrum as black box strategies for testing software and hardware. This paper discusses a novel adoption of a combinatorial interaction strategy to generate a sparse combinatorial data table (SCDT) for machine learning. Unlike test data generation strategies, in which the t-way tuples synthesize into a test case, the proposed SCDT requires analyzing instances against their corresponding tuples to generate a systematic learning dataset.

The alignment between the implemented database application systems and their data specification standard description files significantly affects the accuracy of enterprises' estimation of data assets based on data standard files. In this study, we proposed an automated approach for discovering and aligning these consistent fields, greatly reducing the cost of manual evaluation. We frame the field's alignment problem as an entity matching computation on two distinct graphs, respectively constructed from the database of application systems and its data specification standard.

The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction.

This dataset was extracted from Twitter using keywords related to Dilma Roussef and Aécio Neves, that were the candidates of the second round of the 2014 presidential election in Brazil. This dataset contains texts in Portuguese and the respective classification of sentiments resulting from the techniques described in the article published in the 2018 IEEE International Conference on Data Mining Workshops - ICDMW (https://ieeexplore.ieee.org/abstract/document/8637504).