The problem of association rules mining has given rise to a rich literature, especially in classic binary bidimensional data. In particular, the representation of the set of rules without loss of information is well understood. This is not the case in multidimensional binary data. This paper shows that the knowledge of n-1 components of every closed n-sets of a multidimensional Boolean tensor, as well as the cardinality of the remaining dimension, is enough to allow for the derivation of the confidence of every multidimensional association rule. This generalises well-known results in the bidimensional case. This paper provides experimental comparisons between the numbers of closed n-sets and frequent associations.

Given the France's rich wine heritage as well as its pioneering position as the world's second wine producer, the production of high quality wines plays a role of primary importance. The recent development of IOT and efficient big data processing has been shown to provide purposeful issue to permanent monitoring during the entire wine making process. Standing within this trend, we introduce in this paper an intelligent system for vineyards monitoring in the Burgundy region. The main trust of the proposed system relies on the use of the Swrl rules in WineCloud ontology. The design of the ontology is mainly based on information gathered from interviews with wine growers. In addition, sensor data is also collected and used to feed the ontology after being processed. The system is used in the aim to have better grape quality with an improved vineyard management. To do so, association rules are extracted from the collected data aiming to provide useful knowledge to forecast vine diseases.

In order to address the issue of counterfeiting online, it is necessary to use automatic tools that analyze the large amount of information available over the Internet. Analysis methods that extract information about the content of the images are very promising for this purpose. In this paper, a method that automatically extract the brand of objects in images is proposed. The method does not explicitly search for text or logos. This information is implicitly included in the Bag-of-Words representation. In the Bag-of-Words paradigm, visual features are clustered to create the visual words. Despite its shortcomings, k-means is the most widely used algorithm. With k-means, the selection of the number of visual words is critical. In this paper, another clustering algorithm is proposed. Markov Cluster Algorithm (MCL) is very fast, does not require an arbitrary selection of the number of classes and does not rely on random initialization. First, we demonstrate in this paper that MCL is competitive to k-means with a number of cluster experimentally selected. Second, we show that it is possible to identify brand from objects in images without previous knowledge about visual identity of these brands.

Analyzing Big Data can help corporations to improve their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also propose a Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.

Increasing complexity in both the software and the underlying hardware, and ever tighter time-to-market pressures are some of the key challenges faced when designing multimedia embedded systems. Optimizing the debugging phase can help to reduce development time significantly. A powerful approach used extensively during this phase is the analysis of execution traces. However, huge trace volumes make manual trace analysis unmanageable. In such situations, Data Mining can help by automatically discovering interesting patterns in large amounts of data. In this paper, we are interested in discovering periodic behaviors in multimedia applications. Therefore, we propose a new pattern mining approach for automatically discovering all periodic patterns occurring in a multimedia application execution trace.