Browse Proceedings

This paper contributes a reference architecture of a reusable infrastructure for scientific experiments on data processing and data integration. The architecture is based on containerization and is integrated with an external machine learning ...

The subgroup discovery problem aims to identify a subset of objects which exhibit interesting characteristics according to a quality measure defined on a target attribute. In this paper, we propose a new optimized approach, called SD-CEDI, which ...

SQL/Row Pattern Recognition (SQL/RPR), a row matching query processing for sequence data stored in a database, has been standardized in SQL:2016. So far, many studies have focused on developing technology to perform SQL/RPR for large-scale ...

Tabular metadata (i.e. attribute names) location and classification is a fundamental problem for large-scale structured corpora. Web tables [24], CORD-19 [35], have thousands to millions of tables, but often have missing or incorrect labels for ...

Multiple kernel clustering (MKC), as an important tool for handling multi-view non-linear data, has attracted notable attention among data mining and machine learning communities. The key issue of MKC is to obtain a more accurate and appropriate ...$\mathit{}$$\mathit{}\mathit{}$$\mathit{}$$\mathit{}\mathit{}$

Data lineage allows information to be traced to its origin in data analysis by showing how the results were derived. Although many methods have been proposed to identify the source data from which the analysis results are derived, analysis is ...

We are interested in the regression of data to a parameterised system of differential equations formalising a dynamical system. We study the case of the Green Solow model, a neoclassical economics model for sustainable growth. Faced with the ...

Nowadays, companies are more and more adopting cloud technologies in the management of their business processes rising, then, the Business Process as a Service (BPaaS) model. In order to guarantee the consistency of the provisioned BPaaS, cloud ...

Digital transformation enables a vast growth of health data. Because of that, scholars and professionals considered AI to enhance quality of care significantly. Machine learning (ML) algorithms for improvement have been studied extensively, but ...${}_{\mathrm{}}$${}_{\mathrm{}}$

Billboard advertisement is among the dominant modes of outdoor advertisements. The billboard operator has an opportunity to improve its revenue by satisfying the advertising demands of an increased number of clients by means of exploiting the user ...

In this paper, we propose methods to detect sarcasm from Japanese text on Twitter by using the BERT language model and analyzing emoji as well as text. After constructing a Japanese Twitter dataset, we extract feature vector for both text and ...

Legal information retrieval holds a significant importance to lawyers and legal professionals. Its significance has grown as a result of the vast and rapidly increasing amount of legal documents available via electronic means. Legal documents, ...

Sentiment analysis can provide a suitable lead for the tools used in software engineering along with the API recommendation systems and relevant libraries to be used. In this context, the existing tools like SentiCR, SentiStrength-SE, etc. ...

Finding outliers in networks is a central task in different application domains. Here, we exploit the stochastic block model framework to study the network from a generative point of view and design a score able to highlight those nodes whose ...

Text classification is a fundamental task that is often carried out upstream of natural language processing (NLP) techniques. Therefore, this task plays an essential role in information retrieval and extraction, and has a wide range of ...

Frequent pattern mining ($\mathsf{FPM}$) on large graph has been receiving increasing attention due to its wide applications. The $\mathsf{FPM}$ problem is defined as mining all the subgraphs (a.k.a. patterns), with frequency above a user-defined threshold in a large ...

High utility co-location pattern mining is still computationally expensive in terms of both runtime and memory consumption. In this paper, an efficient high utility co-location pattern mining algorithm, named EHUCM, is proposed to address this ...

Aspect-Based Opinion Mining (ABOM) mainly focuses on mining the aspect terms (product’s features) and related opinion polarities (e.g., Positive, Negative, and Neutral) from user’s reviews. The most prominent neural network-based methods to ...

In this paper, we propose a parallel 5-vertex orbit counting method using GPUs. Given a graph and a set of subgraph patterns, the vertex orbit counting problem is to output, for each vertex in the graph, the number of subgraph patterns that ...

Partial periodic-frequent pattern mining is an important knowledge discovery technique in data mining. It involves identifying all frequent patterns that have exhibited partial periodic behavior in a temporal database. The following two ...

Compliance with data retention laws and legislation is an important aspect of data management. As new laws governing personal data management are introduced (e.g., California Consumer Privacy Act enacted in 2020) and a greater emphasis is placed ...

Missing data occur commonly in data warehouses and may generate data usefulness problems. Thus, it is essential to address missing data to carry out a better analysis. There exists data imputation methods for missing data in fact tables, but not ...

Data retention laws establish rules intended to protect privacy. These define both retention durations (how long data must be kept) and purging deadlines (when the data must be destroyed in storage). To comply with the laws and to minimize ...