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This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching‐based Instances (COMI) and Pattern mining for Ontology Matching‐based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart‐city data into similar clusters using the generic k‐means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state‐of‐the‐art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal with heterogeneous large‐scale data in smart‐city environments. [ABSTRACT FROM AUTHOR]

Graph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities. This article is categorized under:Algorithmic Development > Spatial and Temporal Data MiningAlgorithmic Development > Association Rules [ABSTRACT FROM AUTHOR]

As an essential data mining task, high-utility itemset mining (HUIM) has attracted a lot of research. With the increase of dataset size, traditional exact HUIM algorithms are faced with exponential growth search space, which is unacceptable for some algorithms. As HUIM can be treated as a combinatorial optimization problem, Evolutionary Computation (EC) based HUIM approaches have been proposed and shown promise performance in mining HUIs. However, the existing EC-based HUIM approaches usually only find part of the HUIs in a limitation time or discovering all the HUIs is usually time-consuming. In this study, an improved binary particle swarm optimization for HUIM (HUIM-IBPSO) is proposed with multiple adjustment strategies to address these problems. In HUIM-IBPSO, a particle movement direction adjustment strategy is presented to keep the same HUIs during the evolution process. In order to utilize the repeated HUIs more efficiently and enhance the search ability, the strategy of local exploration is proposed in HUIM-IBPSO. A restart strategy for the population is developed in HUIM-IBPSO with the purpose to avoid the premature convergence before discovering any HUIs. To mine HUIs more efficiently, particle modify strategy and fitness value hash strategy are introduced in HUIM-IBPSO. A comprehensive comparison with five state-of-the-art EC-based HUIM algorithms and three precise HUIM algorithms on real datasets shows that the designed model outperforms them in terms of the number of HUIs found, the speed at which they converge, and the duration of execution (runtime). • An improved binary PSO with specific strategies is presented for mining HUIs. • A particle movement direction adjustment strategy during the evolution process is proposed. • A neighborhood exploration strategy and fitness value hash strategy are designed. • A restart strategy and a repair strategy are proposed to improve the effectiveness. [ABSTRACT FROM AUTHOR]

High Utility Pattern Mining is a popular task for analyzing data. It consists of discovering patterns having a high importance in databases. A popular application of high utility pattern mining is to identify high utility (profitable) patterns in customer transaction data. Though such analysis can be useful to understand data, it does not consider the cost (e.g. effort, resources, money or time) required for obtaining the utility (benefits). In this paper, we argue that to discover interesting patterns in event sequences, it is useful to consider both a utility model and a cost model. For example, to identify cost-effective ways of treating patients from medical pathways data, it is desirable to consider not only the ability of treatments to inhibit symptoms or cure a disease (utility) but also the resources consumed and the time spent (cost) to provide these treatments. Based on this perspective, this paper defines a novel task of discovering Cost-Effective Event Sequences in event logs. In this task, cost is modeled as numeric values, while utility is represented either as binary or numeric values. Measures are proposed to evaluate the trade-off and correlation between cost and utility of patterns to identify cost-effective patterns (patterns having a low cost but providing a high utility). Three efficient algorithms called CEPB, corCEPB and CEPN are designed to extract these patterns. They rely on a tight lower-bound on the cost and a memory buffering technique to find patterns efficiently. Experiments show that the proposed algorithms achieve high efficiency, that proposed optimizations improve efficiency, and that insightful cost-effective patterns are found in real-life e-learning data. [ABSTRACT FROM AUTHOR]

High utility pattern mining has been actively researched and applied to diverse applications because it can process the database by considering the quantity and importance of items. However, traditional high utility pattern mining methods aim to handle static databases, so they cannot meet the requirements of users who want to process the dynamic environments. Although methods to process incremental databases have been proposed, they have limitations that they perform the mining process on the entire database, including already processed data, whenever data are newly inserted. The pre-large concept is one of the techniques to process the dynamic database. Utilizing the pre-large technique, we can efficiently handle the transaction insertion using the extracted patterns of the previous mining process. In this paper, we propose a novel pre-large-based approach to discover high utility patterns from incremental databases. A list structure is proposed to store the utility information of patterns, so candidate patterns are not generated, and an additional database scan is not required. Performance evaluation performed on various real and synthetic datasets shows that the proposed algorithm is more efficient and effective than the latest approaches in a dynamic environment. [ABSTRACT FROM AUTHOR]