Directed acyclic graphs (DAGs) are used in many domains ranging from computer science to bioinformatics, including industry and geoscience. They enable to model complex evolutions where spatial objects (e.g., soil erosion) may move, (dis)appear, merge or split. We study a new graph-based representation, called attributed DAG (a-DAG). It enables to capture interactions between objects as well as information

Supervised machine learning methods depend highly on the quality of the training dataset and the underlying model. In particular, neural network models, that have shown great success in dealing with natural language problems, require a large dataset to learn a vast number of parameters. However, it is not always easy to build a large (labelled) dataset. For example, due to the complex nature of tweets

As data become an increasingly important asset for organizations, so does the access control policies that protect aforesaid data. Many subjects (public, researchers, etc.) are interested in accessing these data, leading to the desire for simple access control. However, some scenarios use vague concepts, such as the “researcher’s expertise”, when making access control decisions. Therefore, access control

Nowadays, many R&I institutions are presently implementing mechanisms to measure and rate their scientific production so as to comply with current legislation and to support research management and decision making. In many cases, they rely on the implementation of current research information systems (CRIS). This is a challenging task that often requires major human intervention and supervision to

Autonomy is a key factor in the quality of life of a person. With the aging of the population, an increasing number of people suffers from a reduced level of autonomy. That compromises their capacity of performing their daily activities and causes safety issues. The new concept of ambient assisted living (AAL), and more specifically its application in smart homes for supporting elderly people, constitutes

Time-to-event prediction has been an important practical task for longitudinal studies in many fields such as manufacturing, medicine, and healthcare. While most of the conventional survival analysis approaches suffer from the presence of censored failures and statistically circumscribed assumptions, few attempts have been made to develop survival learning machines that explore the underlying relationship

Consumer-generated reviews have become increasingly important in decision-making processes for customers. Meanwhile, the overwhelming quantity of review data makes it extremely difficult to find useful information from it. A considerable amount of studies have attempted to address this problem by selecting reviews that might be helpful for and preferred by users. However, the performance of existing

In traditional facility location recommendations, the objective is to select the best locations which maximize the coverage or convenience of users. However, since users’ behavioral habits are often influenced by time, the temporal impacts should not be neglected in recommendation. In this paper, we study the problem of time-aware facility location recommendation problem, taking the time factor into

Crowdsourcing services provide an efficient and relatively inexpensive approach to obtain substantial amounts of labeled data by employing crowd workers. It is obvious that the labeling qualities of crowd workers directly affect the quality of the labeled data. However, existing label aggregation strategies seldom consider the differences in the quality of workers labeling different instances. In this

The widespread use of position-tracking devices leads to vast volumes of spatial–temporal data aggregated in the form of the trajectory data streams. Extracting useful knowledge from moving object trajectories can benefit many applications, such as traffic monitoring, military surveillance, and weather forecasting. Most of the knowledge gleaned from the trajectory data illustrates different kinds of

Representing the knowledge learned by neural networks in the form of interpretable rules is a prudent technique to justify the decisions made by neural networks. Heretofore many algorithms exist to extract symbolic rules from neural networks, but among them, a few extract rules from deep neural networks trained using deep learning techniques. So, this paper proposes an algorithm to extract rules from

A big challenge in the knowledge discovery process is to perform data pre-processing, specifically feature selection, on a large amount of data and high dimensional attribute set. A variety of techniques have been proposed in the literature to deal with this challenge with different degrees of success as most of these techniques need further information about the given input data for thresholding,

Generally, users are reserved in describing their search intention when submitting queries into the search engine. Therefore, a large number of search queries are usually short, ambiguous and tend to have multiple interpretations. With the gigantic size of the web, ignoring the information needs underlying such queries can misguide the search engine. To mitigate these issues, an effective approach

Social media have a great potential to improve information dissemination in our society, yet they have been held accountable for a number of undesirable effects, such as polarization and filter bubbles. It is thus important to understand these negative phenomena and develop methods to combat them. In this paper, we propose a novel approach to address the problem of breaking filter bubbles in social

In the published article, Figure 9(a) and Figure 9(b) are the same figure.

In this paper, we propose an outlier detection method from an unlabeled target dataset by exploiting an unlabeled source dataset. Detecting outliers has attracted attention of data miners for over two decades, since such outliers can be crucial in decision making, knowledge discovery, and fraud detection, to name but a few. The fact that outliers are scarce and often tedious to label motivated researchers

Local community detection, which aims to find a target community containing a set of query nodes, has recently drawn intense research interest. The existing local community detection methods usually assume all query nodes are from the same community and only find a single target community. This is a strict requirement and does not allow much flexibility. In many real-world applications, however, we

Sequential pattern mining has been introduced by Agrawal and Srikant (in: Proceedings of ICDE’95, pp 3–14, 1995) 2 decades ago, and its usefulness has been widely proved for different mining tasks and application fields such as web usage mining, text mining, bioinformatics, fraud detection and so on. Since 1995, despite numerous optimization proposals, sequential pattern mining remains a costly task

Recently, learning-based partial differential equation (L-PDE) has achieved success in few-shot learning area, while its feature weighting mechanism and recognition stability require further improvement. To address these issues, we propose a novel model called “location-dependent PDE” (LD-PDE) based on Navier–Stokes equation and rotational invariants in this paper. To our best knowledge, LD-PDE is

Data-as-a-service (DaaS) is a cloud computing service that emerged as a viable option to businesses and individuals for outsourcing and sharing their collected data with other parties. Although the cloud computing paradigm provides great flexibility to consumers with respect to computation and storage capabilities, it imposes serious concerns about the confidentiality of the outsourced data as well

Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier

The widely discussed and applied Johnson–Lindenstrauss (JL) Lemma has an existential form saying that for each set of data points Q in n-dimensional space, there exists a transformation f into an \(n'\)-dimensional space (\(n'

Collaborative filtering (CF) is a widely adopted technique in recommender systems. Traditional CF models mainly focus on predicting the user preference to items in a single domain, such as the movie domain or the music domain. A major challenge for such models is the data sparsity, and especially, CF cannot make accurate predictions for the cold-start users who have no ratings at all. Although cross-domain

Author verification is a fundamental problem in authorship attribution, and it suits most relevant applications where it is not possible to predefine a closed set of suspects. So far, the most successful approaches attempt to sample the non-target class (all documents by all other authors) and transform author verification to a binary classification task. Moreover, they follow the instance-based paradigm

Popularity in social media is mostly interpreted by drawing a relationship between a social media account and its followers. Although understanding popularity from social media has been explored for about a decade, to our knowledge, the extent to which the account owners put efforts to enhance their popularity has not been evaluated in detail. In this paper, we focus on Twitter, a popular social media

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional

Clustering is a technique employed for data mining and analysis. k-means is one of the algorithms utilized for clustering. However, the answer derived using this algorithm is dependent on the initial solution and hence easily retrieves the optimal local answers. To overcome the disadvantages of this algorithm, in this paper a combination of pollination of flowers algorithm and genetic algorithm, named

In this paper, we study the problem of searching k-discriminative paths on road networks. Given a source node src and a destination node dest on a road network, we aim to search k paths between src and dest, where these k paths satisfy the multi-objective goal including the minimization of the path overlapping and the minimization of the path length. Specifically, the requirement of minimizing the

Since the introduction of the Decision model and notation (DMN), the standard has successfully been adopted in both industry and academia. However, no clear modelling guidelines can be found regarding the development of DMN decision models. For approaching this gap, this paper discusses modelling methodologies for the DMN standard, both at the decision requirements level as well as at the decision

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics

In the modern digital era, information systems must operate in increasingly interconnected and dynamic environments, which force them to be changeable yet consistent. Such modern information systems are usually decision- and knowledge-intensive. A recently introduced standard, the decision model and notation (DMN), has been adopted in both industry and academia as a suitable method for modelling decisions

Unsupervised domain adaptation (UDA) serves to transfer specific knowledge from massive labeled source domain data to unlabeled target domain data via mitigating domain shift. In this paper, we propose a discriminative relevance regularization term (DRR) to enhance the performance of UDA by reducing the domain shift from the aspect of semantic relevance across domains. In particular, DRR is formulated

Data processing is increasingly becoming the subject of various policies and regulations, such as the European General Data Protection Regulation (GDPR) that came into effect in May 2018. One important aspect of GDPR is informed consent, which captures one’s permission for using one’s personal information for specific data processing purposes. Organizations must demonstrate that they comply with these

Online social media has become an ideal place in spreading rumor events with its convenience in communication and information dissemination, which raises the difficulty in debunking rumor events automatically. To deal with such a challenge, traditional classification approaches relying on manually labeled features have to face a daunting number of human efforts. With the consideration of the realness

Abstract This paper revisits the problem of consistency maintenance when insertions or deletions are performed on a valid database containing marked nulls. This problem comes back to light in real-world linked data or RDF databases when blank nodes are associated with null values. This paper proposes solutions for the main problems one has to face when dealing with updates and constraints, namely update

Abstract With the rapid growth of georeferenced data, large-scale data processing and analysis methods are needed for spatial big data. Spatial co-location pattern mining is an interesting and important issue in spatial data mining area which discovers the subsets of features whose objects are frequently located together in geographic proximity. There are several works for efficiently processing co-location

Abstract Crowdsourcing is a useful and economic approach to annotate data. Various computational solutions have been developed to pursue a consensus of high quality. However, available solutions mainly target single-label tasks, and they neglect correlations among labels. In this paper, we introduce a multi-label crowd consensus (MLCC) model based on a joint matrix factorization. Specifically, MLCC

Abstract We present ProSecCo, an algorithm for the progressive mining of frequent sequences from large transactional datasets: It processes the dataset in blocks and it outputs, after having analyzed each block, a high-quality approximation of the collection of frequent sequences. ProSecCo can be used for interactive data exploration, as the intermediate results enable the user to make informed decisions

Abstract In analysing sensor data, it usually proves beneficial to use domain knowledge in the classification process in order to narrow down the search space of relevant features. However, it is often not effective when decision trees or the k-NN method is used. Therefore, the authors herein propose to build an appropriate concept ontology based on expert knowledge. The use of an ontology-based metric

Abstract Threats are used in persuasive negotiation dialogues when a proponent agent tries to persuade an opponent of him to accept a proposal. Depending on the information the proponent has modeled about his opponent(s), he may generate more than one threat, in which case he has to evaluate them in order to select the most adequate to be sent. One way to evaluate the generated threats is by calculating

Abstract With continued development of location-based systems, large amounts of trajectories become available which record moving objects’ locations across time. If the trajectories collected by different location-based systems come from the same moving object, they are spliceable trajectories, which contribute to representing holistic behaviors of the moving object. In this paper, we consider how

Abstract In this paper, we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlapping intervals, such that the intervals span subgraphs with maximum total

Abstract Semantic similarity and relatedness computation has attracted an increasing amount of attention among researchers. The majority of previous studies, including edge-based and information content-based methods, rely on a single semantic relationship in WordNet such as the “is-a” relation. However, a performance ceiling may have been created by semantic unicity and inadequate calculation in solely

Abstract As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenue. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, which usually involves churn at micro-level (between an app and a specific

Abstract The miniaturization of sensors and their integration in everyday appliances have opened the way for ecologically monitoring people’s behavior based on their interaction with smart objects. Thanks to behavior monitoring, mobile, and ubiquitous information systems in the areas of e-health, home automation, and smart cities are becoming more and more “smart,” being able to dynamically adapt themselves

Abstract Sentiment analysis is an important task in order to gain insights over the huge amounts of opinionated texts generated on a daily basis in social media like Twitter. Despite its huge amount, standard supervised learning methods won’t work upon such sort of data due to lack of labels and the impracticality of (human) labeling at this scale. In this work, we leverage distant supervision and

Abstract Due to the complexity of decision making under uncertainty and the existence of various and often conflicting criteria, several methods have been proposed to facilitate decision making, and fuzzy logic has been used successfully to address this issue. This paper presents a new framework for solving multi-attributes group decision-making problems under fuzzy environments. The proposed algorithm

Abstract Many real-world networks display hidden community structures with important potential implications in their dynamics. Many algorithms highly relevant to network analysis have been introduced to unveil community structures. Accurate assessment and comparison of alternative solutions are typically approached by benchmarking the target algorithm(s) on a set of diverse networks that exhibit a

Frequent itemset mining is considered a popular tool to discover knowledge from transactional datasets. It also serves as the basis for association rule mining. Several algorithms have been proposed to find frequent patterns in which the apriori algorithm is considered as the earliest proposed. Apriori has two significant bottlenecks associated with it: first, repeated scanning of input dataset and

In this paper, we propose PAIRS3: a possibilistic classification approach based on artificial immune recognition system (AIRS) and the possibility theory. PAIRS3 is applied to address shortcomings in student attainment rates of course learning outcomes by predicting effective remedial actions through learning from assessment rubrics instances. For most of assessment rubric instances, it is difficult

Bayesian network classifiers are, functionally, an interesting class of models, because they can be learnt out-of-core, i.e. without needing to hold the whole training data in main memory. The selective K-dependence Bayesian network classifier (SKDB) is state of the art in this class of models and has shown to rival random forest (RF) on problems with categorical data. In this paper, we introduce an

Given a social network with diffusion probabilities as edge weights and a positive integer k, which k nodes should be chosen for initial injection of information to maximize the influence in the network? This problem is popularly known as the Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain, since one and half decades

Abstract Recommender systems are a fundamental component of contemporary social-media platforms and require feedback submitted from users in order to fulfill their goal. On the other hand, the raise of advocacy about user-controlled data repositories supports the selective submission of data by user through intelligent software agents residing at the user end. These agents are endowed with the task

Identification of the influential users is one of the most practical analyses in social networks. The importance of this analysis stems from the fact that such users can affect their followers “/friends” viewpoints. This study aims at introducing two new indices to identify the most influential users in the Twitter social network. Four sets of features extracted from user activities, user profile,

Abstract Existing distance metric learning methods require optimisation to learn a feature space to transform data—this makes them computationally expensive in large datasets. In classification tasks, they make use of class information to learn an appropriate feature space. In this paper, we present a simple supervised dissimilarity measure which does not require learning or optimisation. It uses class

Extracting discriminative features and reducing the dimensionality of data are two main objectives of manifold learning. Among different techniques, nonlinear manifold learning methods have been proposed in order to extract features from data which are not linearly distributed. Kernel trick is one of the famous nonlinear techniques which helps to project the data without an explicit mapping which can

The increasing amount and complexity of Cyber security attacks in recent years have made text analysis and data mining techniques an important factor in discovering features of such attacks and detecting future security threats. In this paper, we report on the results of a recent case study that involved the analysis of a community data set collected from five small and medium companies in Korea. The

Abstract We present Lexifield, a fully automatic language-independent system for building domain-specific lexicons from a short list of terms defining the domain. Lexifield relies on a pre-trained word embedding model, a definition dictionary and a dictionary of synonyms. To evaluate this system, four lexicons have been generated: one lexicon in French for the topic “son” (“sound”) and three lexicons

Abstract Emotion recognition in text is an important natural language processing (NLP) task whose solution can benefit several applications in different fields, including data mining, e-learning, information filtering systems, human–computer interaction, and psychology. Explicit emotion recognition in text is the most addressed problem in the literature. The solution to this problem is mainly based

Abstract User-based collaborative filtering (UBCF) is widely used in recommender systems (RSs) as one of the most successful approaches, but traditional UBCF cannot provide recommendations with satisfactory accuracy and diversity simultaneously. Covering-based collaborative filtering (CBCF) is a useful approach that we have proposed in our previous work, which greatly improves the traditional UBCF