Abstract The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis. The system is an early warning/early action system for crime investigation that supports law enforcement with different processes to collect and process data as well as having knowledge extraction tools. It helps to extract information during the investigation of a criminal case or even to detect possible criminal hotspots that may lead to further investigation or analysis of a criminal case Abu Al‐Haija et al. (2022, Electronics, 11, 556). The functionality of the proposed system is illustrated through several examples using data collected from the dark web, which includes advertisements offering firearms‐related products.

Social networks have taken an irreplaceable role in our lives. They are used daily by millions of people to communicate and inform themselves. This success has also led to a lot of irrelevant content and even misinformation on social media. In this paper, we propose a user-centred framework to reduce the amount of irrelevant content in social networks to support further stages of data mining processes. The system also helps in the reduction of misinformation in social networks, since it selects credible and reputable users. The system is based on the belief that if a user is credible then their content will be credible. Our proposal uses word embeddings in a first stage, to create a set of interesting users according to their expertise. After that, in a later stage, it employs social network metrics to further narrow down the relevant users according to their credibility in the network. To validate the framework, it has been tested with two real Big Data problems on Twitter. One related to COVID-19 tweets and the other to last United States elections on 3rd November. Both are problems in which finding relevant content may be difficult due to the large amount of data published during the last years. The proposed framework, called NOFACE, reduces the number of irrelevant users posting about the topic, taking only those that have a higher credibility, and thus giving interesting information about the selected topic. This entails a reduction of irrelevant information, mitigating therefore the presence of misinformation on a posterior data mining method application, improving the obtained results, as it is illustrated in the mentioned two topics using clustering, association rules and LDA techniques. • A new framework for filtering irrelevant content in Twitter has been proposed. • First time that word embeddings and bios are used to obtain user expertise. • The framework filters content of social networks keeping only those relevant to the topic intend to study. • The proper performance has been tested on two real problems.

The enormous quantity of data handled by building management systems are key to develop more efficient energy operational systems. However, the inability of current systems to take benefit from the generated data may waste good opportunities of improving building performance. Big Data appears as a suitable framework to sustain the management system and conduct future prospective analysis. In this article, we present a Big Data-based architecture for the efficient management of buildings. The different Big Data components are involved not only in the data acquisition phase, but also in the implementation of algorithms capable of analyzing massive data collected from very heterogeneous sources. They also enable fast computations that can help the generation of optimal operational plan generations to improve the building functioning. The proposed architecture has been effectively introduced in four different-purpose buildings, demonstrating that Big Data can help during the energy cycle of the building.

The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10 equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.

The discovery and exploitation of hidden information in collected data have gained attention in many areas, particularly in the energy field due to their economic and environmental impact. Data mining techniques have then emerged as a suitable toolbox for analyzing the data collected in modern network management systems in order to obtain a meaningful insight into consumption patterns and equipment operation. However, the enormous amount of data generated by sensors, occupational, and meteorological data involve the use of new management systems and data processing. Big Data presents great opportunities for implementing new solutions to manage these massive data sets. In addition, these data present values whose nature complicates and hides the understanding and interpretation of the data and results. Therefore, the use of fuzzy methods to adequately transform the data can improve their interpretability. This article presents an automatic fuzzification method implemented using the Big Data paradigm, which enables, in a later step, the detection of interrelations and patterns among different sensors and weather data recovered from an office building.

Nowadays, searching for specific kind of knowledge that deviates from the usual standards is very useful in several domains such as network traffic anomalies, fraud detection, economic analysis, or medical diagnosis. Fuzzy association rules have been developed as a powerful tool for dealing with imprecision in databases (that may come from the source, i.e., imprecise measures taken by the machine, or from the human understanding of a concept) and offering a comprehensive representation of found knowledge. In this paper, we introduce the notion of fuzzy exception and fuzzy anomalous rule for the recognition of these types of deviations. The deviations are associated with the common patterns, which usually are hidden in data affected by some kind of fuzziness. We present a new approach for mining such rules based on a recently proposed model for representing and evaluating fuzzy rules. Important advantages are to obtain more understandable results and that the mining process can be parallelized. An algorithm following the proposed model is developed, and some experiments are performed in data where some numerical attributes have been fuzzified and also in some real fuzzy transactional datasets for testing the proposed algorithm. From experimentation, we have seen that the proposed fuzzy rules give some insights on the exception and anomaly detection in credit payments.

Mining association rules is a well known framework for extracting useful knowledge from databases. Despite their proven applicability there exist other approaches that also search for novel and useful information such us peculiarities, infrequent rules, exceptions or anomalous rules. The common feature of these proposals is the low support of such type of rules. So there is a necessity of finding efficient algorithms for extracting them. The principal objective of this paper is providing a unified framework for dealing with such kind of rules. In our case, we take advantage of an existing logic approach called GUHA. This model was first presented in the middle sixties by Hájek et al. and then has been developed by Rauch and others in the last decade. Following this line, this paper also offers some interesting issues. First, it provides a deep analysis of semantics and formulation of exception and anomalous rules. Second, we define the so called double rules as a new type of rules which in conjunction with exceptions and anomalies will describe in more detail the relationship between two sets of items. Third, we give new approaches for mining them and we propose an algorithm with reasonably good performance.

Many papers have addressed the task of proposing a set of convenient axioms that a good rule interestingness measure should fulfil. We provide a new study of the principles proposed until now by means of the logic model proposed by Hájek et al.14 In this model association rules can be viewed as general relations of two itemsets quantified by means of a convenient quantifier.28 Moreover, we propose and justify the addition of two new principles to the three proposed by Piatetsky-Shapiro.27 We also use the logic approach for studying the relation between the different classes of quantifiers and these axioms. We define new classes of quantifiers according to the notions of strong and very strong rules, and we present a quantifier based on the certainty factor measure,317 studying its most salient features.

Many databases in real life involve pairs of the form (item,quantity). This kind of data can be characterized using the theory of bags. We present a general framework for extracting useful knowledge from bag databases using different types of patterns. Here we present different approaches for the task of discovering fuzzy association rules and gradual dependencies in bag databases.