IDEAS

Hypergraphs are trivial mathematical structures that can embed other data models. For instance, relations in relational models, and edges in graphs and tree data models can all be naturally represented by hyperedges. Hypergraphs into other data models ...

GraphQL, a query language, has gained industry adoption (including GitHub, Coursera, and Neo4j) due to its ability to specify structures for input data as objects at the application level and get query results for the required parts of the matched ...

In the domain of research, development and innovation, every new discovery usually relies on previous evidence as the basis of any novel argument. Research papers and patents are often used to support such results. However, their discovery—especially, ...

Federated learning is a machine learning technique where multiple clients with local data collaborate in training a machine learning model. In FedAvg, the main federated learning algorithm, clients train machine learning models locally and share the ...

Propositionalisation tampers the running time of state-of-the-art algorithms in declarative temporal model mining, as they exhaustively generate the clauses instantiated with the results of frequent itemset mining algorithms. Existing algorithms also ...

Reducing the size of the training set, that is, replacing it with a condensing set, while maintaining the classification accuracy as much as possible is a very common practice to speed up instance-based classifiers. Data reduction techniques, also ...

The fast development of wireless location acquisition technologies has led to a significant increase in the availability of mobility data, specifically spatio-temporal trajectory data, which includes information about the movements (locations) of ...

There is a need to persuade public and private entities to share their currently unexposed bio-data banks by preserving ownership and secrecy. The reason is to make available results that can be obtained by massively exploiting the content of such data ...

Reduction through Homogeneous Clustering (RHC) and its editing variant (ERHC) are effective data reduction techniques for the k-NN classifier. They are based on an iterative k-means clustering task that discovers homogeneous clusters. The centers of ...

Cryptocurrencies are digital currencies that operate on the blockchain, which is the technology that offers security and decentralization. The principal characteristic of cryptocurrencies is that they are not generally issued by a central authority. ...

User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users’ feedback or place phony bids in order to increase their own ...

Service-oriented computing (SOC) is a new computing paradigm that creates new semantic Web services composition. Moreover, some current interactive (WS) composition approaches have produced multiple aspects of interactivity and collaborative processes ...

The COVID-19 pandemic has dramatically transformed healthcare systems globally, therefore improving health information technology sector. From the moment that pandemic has broken out, the use of information and communication technologies (ICT) has ...

In the context of the IoT, energy is a scarce resource. Since the transmission of data is one of the largest energy consumers, the amount of data must be reduced as much as possible. Modern database systems optimize the queries to reduce execution ...

In the context of process monitoring and predictive maintenance, an adapted visualisation of sensor data is essential in order to help the domain experts to make the right maintenance decision. The large volume and diversity of data leads us to ...

Drug traceability is currently a very challenging area given the complexity of several issues, including drug quality and counterfeit medications. The counterfeited drugs have a major impact on human life, treatment outcomes and economic burden. To deal ...

This study aims to find out the level of concern of Internet users in Spain for personalised advertising. The privacy and value of users' data is closely related to the risk and trust they feel in the network. Users are concerned about personalised ...

This paper offers a brief history of, ConfSys, a conference management system, that has been used for over 15 years to support a number of international academic conferences. It is a complete system that has all functions automated with the possibility ...

Recommender Systems (RS) often face challenges with new user and data sparsity problems. To address these issues, this paper proposes a new REcommendation Model based sOcio-enVironmEntal factors called "RE-MOVE", which takes into account socio-...

Entity Matching (EM) involves identifying different data representations referring to the same entity from multiple data sources and is typically formulated as a binary classification problem. It is a challenging problem in data integration due to the ...

Controversy refers to content attracting different point-of-views, as well as positive and negative feedback on a specific event, gathering users into different communities. Research on controversy led to two main categories of works: controversy ...

Speech recognition is difficult when the speech signal is weak or occurs in a noisy environment. This paper presents an efficient and robust method that can reconstruct the standard pronunciation of English phonemes and words given a weak or noisy ...

[]The Internet has been widely used in various industries, so the anomaly detection of network traffic is of great significance for the security of network applications. Currently, network traffic anomaly detection has a high detection accuracy, but it ...

Diabetic Retinopathy (DR) is a complication of diabetes, caused by a damage to the blood vessels in the light-sensitive tissue of the retina. Since it affects the eyes, it can determine visual impairment or even blindness. Considering the number of ...

Indexing multimedia content with rich and accurate metadata allows for improving the quality of the search engines’ results and boosting the recommender systems performances, which can benefit from this information to yield more effective ...

Given a federated learning model and a record, a membership inference attack can determine whether this record is part of the model’s training dataset. Federated learning is a machine learning technique that enables different parties to train a model ...