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This paper presents a case study focused on synthesizing relational datasets within Official Statistics for software and technology testing purposes. Specifically, the focus is on generating synthetic data for testing and validating software code. ...

Although data lake technology has received increasing research attention in recent years with the popularity of Big Data and heterogeneous data technologies, it still receives little attention in the enterprise space and relation to relational databases. ...

Knowledge graphs and other forms of relational data have become a widespread kind of data, and powerful methods to analyze and learn from them are needed. Formal Concept Analysis (FCA) is a mathematical framework for the analysis of symbolic ...

In this paper we present two novel families of semi-supervised multi-view clustering algorithms for relational data, providing relevance weights for views and cluster representatives. The families differ by their cluster representative ...

With the boom in the development of computer hardware and software, social media, IoT platforms, and communications, there has been exponential growth in the volume of data produced worldwide. Among these data, relational datasets are ...

• The first distributed RESCAL implementation to estimate latent features.
• ...

Data compression has been widely adopted in the industry to reduce storage or bandwidth consumption by removing redundant data or encoding information. Redundancy in semantics implies that some facts in a knowledge base can be inferred ...

To handle data variety in one project, some researchers proposed using multiple databases or one multi-model database to manage various data. However, considering that the predominated Relational Database Management Systems (RDBMSs) in the current ...

Robust relational database watermarking techniques aim to preserve the watermark in the protected assets despite daily updates and malicious attacks. Nevertheless, most techniques do not analyze the root of operations that can degrade ...

• A low critical number of detected marks can still help to overcome attacks.
• ...

Data integration technology can integrate data from different data sources, making it convenient and prompt to use heterogeneous data when processing big data. Therefore, data integration plays an important role in many industries. Recently, more ...

Reversible watermarking is an important method of information hiding, which has been widely used in copyright protection of relational data. Reversible watermarking is more advanced than its predecessors in many ways. It can be used not only to ...

With the recent progress of data acquisition technology, classification of data exhibiting relational dependence, from online social interactions to multi-omics studies to linkage of electronic health records, continues to gain an ever ...

Effective query optimization is a core feature of any database management system. While most query optimization techniques make use of simple metadata, such as cardinalities and other basic statistics, other optimization techniques are based on ...

The analysis and evaluation of simulation data, especially the selection of assessment indexes and the establishment of index system, need the knowledge base or knowledge map of simulation application field as the expert knowledge support. The ...

Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, little is known about their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g., the ...

• We address the problem of clustering relational data in a cautious way.
• The ...

We propose in this article a new relational clustering method that can return a partial answer (i.e., a set of clusterings) in some cases. Starting from relational or similarity data, we determine a partial equivalence relation defined ...

There is an increasing interest for multi-view clustering due to its ability to manage data from several sources. The majority of multi-view clustering algorithms are suitable to analyse vector data, but much less attention has been given for the ...

• We propose prototype-based clustering algorithms for relational data, i.e. data described by their similarities.

Among the variety of algorithms that have been developed for clustering, prototype-based approaches are very popular due to their low computational complexity, allowing real-life applications. In such algorithms, the data set is ...

• The paper provides multi-view clustering algorithms for relational data.
• The ...

Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic ...

We describe our winning solution to the 2017’s Soccer Prediction Challenge organized in conjunction with the MLJ’s special issue on Machine Learning for Soccer. The goal of the challenge was to predict outcomes of future matches within a selected ...

As data accumulate faster and bigger, building representational models has turned into an art form. Despite sharing common data types, each scientific discipline often takes a different approach. In this work, we propose representational models ...