Extracting domain knowledge is important for different purposes, including development of new systems and maintenance of existing systems in the domain. Automatically supporting this task is challenging; most existing methods assume high similarity of variants which limits reuse of the generated domain artifacts, or provide very low-level features which hinder domain structure and behavior. In this

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm. We first formally define the types of clusters DP and DBSCAN are designed to detect; and then identify

Electronic Health Records (EHRs) have become one of the main sources of evidence to evaluate clinical actions, improve medical quality, predict disease-risk, and optimize treatment effects. However, EHRs present several modeling challenges, including heterogeneous data types and dynamic characteristics. Reinforcement learning provides an efficient way for sequential decision-making. Powered by model-based

The design and improvement of business processes is of central importance for realizing benefits of information systems. A broad spectrum of methods has been proposed since the 1990s, which ranges into several dozen. It is unclear whether this large number trivially stems from copying and relabeling or whether there are substantial differences in these methods that can be tied to their applicability

The efficiency and effectiveness of business processes are usually evaluated by Process Performance Indicators (PPIs), which are computed using process event logs. PPIs can be insightful only when they are measurable, i.e., reliable. This paper proposes to define PPI measurability on the basis of the quality of the data in the process logs. Then, based on this definition, a framework for PPI measurability

Modern data-intensive applications have to manage huge quantities of streaming/relational data and need advanced query capabilities involving combinations of continuous queries (CQs) and one-time queries (OTQs) also requiring the verification of complex temporal conditions. In this paper, we go beyond the disjointed panorama of current approaches and adopt a new holistic approach to the integration

The General Data Protection Regulation (GDPR) came into force in 2018. After this enforcement, many fines have already been imposed by national data protection authorities in Europe. This paper examines the individual GDPR articles referenced in the enforcement decisions, as well as predicts the amount of enforcement fines with available meta-data and text mining features extracted from the enforcement

In many instances, misinformation among the population manifests itself in the form of conspiracy theories. Services such as YouTube, which allow the publication of audiovisual material in juxtaposition with peer responses (e.g., comments), function as ideal forums to disseminate such conspiracy theories and reach a massive audience. While previous research provided initial evidence about the prevalence

Spam reviews misguide decision makings of consumers and may seriously affect fair trading in the online markets. Existing methods for detecting spam reviews mainly focus on feature designs from linguistic and psychological clues, but they hardly reveal the potential semantics. Recent research works apply deep learning to capture semantics features, while these models fail to extract multi-granularity

The emergence of the blockchain phenomenon affects various aspects of human life. Public sector reforms are also influenced by this trend. E-government leaders around the world are tentatively beginning to grasp the potential of blockchain and other distributed ledger technologies, especially with regard to their promise to provide more decentralized information management solutions in government and

Digitalization of knowledge work in communication-intensive domains such as intellectual property protection poses great challenges but also opportunities to improve today’s working environments. The legal domain is strongly characterized by knowledge work, whereby, despite a common legal framework, creativity of individual experts is decisive. This knowledge-intensive work deals with a great amount

Software engineering includes the runtime evaluation of a prototype by experiments with carefully selected sample inputs. For the development of software intended to operate on relation instances for a given relational schema with a single functional dependency, we are then challenged to generate appropriate sample instances of increasing size. Moreover, studying the impact of varying the sizes of

Internet of Things (IoT) paradigm raises challenges for devising efficient strategies that offload applications to the fog or the cloud layer while ensuring the optimal response time for a service. Traditional computation offloading policies assume the response time is only dominated by the execution time. However, the response time is a function of many factors including contextual parameters and

Business process management has been widely adopted by many organizations,​ resulting in the accumulation of large collections of process models. The majority of these models are rather large and complex. Even though such models constitute a great source of knowledge, they cannot be easily understood by all process stakeholders. Process model abstraction techniques have been proven effective in generating

In order to manage evolving organisational practice and maintain compliance with changes in policies and regulations, businesses must be capable of dynamically reconfiguring their business processes. However, such dynamic reconfiguration is a complex, human-intensive and error prone task. Not only must new business process rules be devised but also, crucially, the transition between the old and new

Automated legal knowledge extraction systems are strongly demanded, to support annotation of legal documents as well as knowledge extraction from them, to provide useful and relevant suggestions to legal actors (e.g., judges, lawyers) for managing incoming new cases. In this paper, we propose CRIKE (CRIme Knowledge Extraction), a knowledge-based framework conceived to support legal knowledge extraction

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to

The Internet of Things (IoT) tsunami, public embracement, and the ubiquitous adoption of smart devices are affecting virtually every industry, directly or indirectly. The success of the current and future landscape of IoT and connected devices requires service provision characterized by scalability, ubiquity, reliability, and high-performance, among others. In order to achieve this attribution, the

Influence Maximization Problem (IMP) is selecting a seed set of nodes in the social network to spread the influence as widely as possible. It has many applications in multiple domains, e.g., viral marketing is frequently used for new products or activities advertisement. While it is a classic and well-studied problem in computer science, unfortunately, all those proposed techniques are compromising

Privacy-Preserving Record Linkage (PPRL) intends to integrate private data from several data sources held by different parties. Due to recent laws and regulations (e.g, General Data Protection Regulation), PPRL approaches are increasingly demanded in real-world application areas such as health-care, credit analysis, public policy evaluation, and national security. However, the majority of the PPRL

Anomaly detection in process mining aims to recognize outlying or unexpected behavior in event logs for purposes such as the removal of noise and identification of conformance violations. Existing techniques for this task are primarily frequency-based, arguing that behavior is anomalous because it is uncommon. However, such techniques ignore the semantics of recorded events and, therefore, do not take

In Papadakis et al. (2020), we presented the latest release of JedAI, an open-source Entity Resolution (ER) system that allows for building a large variety of end-to-end ER pipelines. Through a thorough experimental evaluation, we compared a schema-agnostic ER pipeline based on blocks with another schema-based ER pipeline based on similarity joins. We applied them to 10 established, real-world datasets

The industry-wide adoption of graph databases has been hindered due to the fragmentation in syntax and semantics of available graph query languages. As a result, several projects have been proposed by industry and academia to develop a standard query language by integrating features from existing practical graph query languages. A significant factor that can impact query language integration is the

This article introduces an algorithm to automatically translate a user-specified keyword-based query K to a SPARQL query Q so that the answers Q returns are also answers for K. The algorithm does not rely on an RDF schema, but it synthesizes SPARQL queries by exploring the similarity between the property domains and ranges, and the class instance sets observed in the RDF dataset. It estimates set similarity

The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of process data in information systems. These are made available in the form of event logs. Process mining techniques enable the process-centric analysis of data, including automatically discovering process models and checking if

This paper presents an exhaustive and unified repository of judgments documents, called ECHR-DB, based on the European Court of Human Rights. The need of such a repository is explained through the prism of the researcher, the data scientist, the citizen, and the legal practitioner. Contrarily to many open data repositories, the full creation process of ECHR-DB, from the collection of raw data to the

The interpretation of any legal norm typically requires consideration of relationships between parts within the same piece of legislation. This work describes a general framework for the development of a system to identify and classify implicit inter-relationships between parts of a legal text. In particular, our approach demonstrates the usefulness of co-occurrence networks of terms, in a practical

This paper reconsiders common benchmarking approaches to nearest neighbor search. It is shown that the concepts of local intrinsic dimensionality (LID), local relative contrast (RC), and query expansion allow to choose query sets of a wide range of difficulty for real-world datasets. Moreover, the effect of the distribution of these dimensionality measures on the running time performance of implementations

Clustering non-Euclidean data is difficult, and one of the most used algorithms besides hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also simply referred to as k-medoids clustering. In Euclidean geometry the mean – as used in k-means – is a good estimator for the cluster center, but this does not exist for arbitrary dissimilarities. PAM uses the medoid instead

Document repositories often provide a keyword-based query interfaces to allow users to search for documents. These interfaces typically have rate limits or monetary cost per access operation. Constrained search interfaces include legal or medical data sources, social networks and the Web. We study the problem where a user has a set of input documents, and wants to discover other similar documents using

Efficient knn computation for high-dimensional data is an important, yet challenging task. Today, most information systems use a column-store back-end for relational data. For such systems, multi-dimensional indexes accelerating selections are known. However, they cannot be used to accelerate knn queries. Consequently, one relies on sequential scans, specialized knn indexes, or trades result quality

In high dimensional datasets, exact indexes are ineffective for proximity queries, and a sequential scan over the entire dataset is unavoidable. Accepting this, here we present a new approach employing two-dimensional embeddings. Each database element is mapped to the XY plane using the four-point property. The caveat is that the mapping is local: in other words, each object is mapped using a different

Nowadays, topic detection from Twitter attracts the attention of several researchers around the world. Different topic classification approaches have been proposed as a result of these research efforts. However, four of the major challenges faced in this context are the use of handcrafted features, the use of Deep Learning algorithms with so many parameters, the fact that their performance is still

The emergence of Location-based social networks (LBSNs) in recent years has boosted improvements in Recommender Systems for a new and specific task: the recommendation of points-of-interest (POI). Despite all the blunt advances recently observed, the area still lacks an updated and consolidated view about the main limitations, common assumptions, and directions we are following as a community. Thus

K-means configuration is to find a configuration of k-means (e.g., the number of clusters, feature sets) that maximize some objectives. It is a time-consuming process due to the iterative nature of k-means. This paper proposes reuse-centric k-means configuration to accelerate k-means configuration. It is based on the observation that the explorations of different configurations share lots of common

Online Social Networks (OSNs) are the most popular interactive media for communicating, posting, and sharing indefinite amounts of personal information. However, along with interesting and attractive topics and contents, some users neither like the fact that certain topics that are not among their interests can fill their personal pages nor do they wish to see disappointing negative posts that may

In this paper, we present GMG-BCU  — a local search algorithm based on block coordinate update for estimating a generalized median graph for a given collection of labeled or unlabeled input graphs. Unlike all competitors, GMG-BCU is designed for both discrete and continuous label spaces and can be configured to run in linear time w. r. t. the size of the graph collection whenever median node and edge

Recently, due to the rapid growth of online social networks (OSNs) such as Facebook, Twitter, Weibo, etc. the number of machine accounts/social bots that mimic human users has increased. Along with the development of artificial intelligence (AI), social bots are designed to become smarter and more sophisticated in their efforts at replicating the normal behaviors of human accounts. Constructing reliable

Fully automated rumor defeating is meaningful for reducing hazards of misinformation in social networks. As one of the automated approaches, content-based rumor defeating is a pipeline that could be divided into four sequential sub-tasks: detection, tracking, sentence classification, and veracity. Specifically, rumor tracking gathers relevant posts and filters unrelated posts for a potential rumor

Online financial content is widespread on social media, especially on Twitter. The possibility to access open, real-time data about stock market information and firms’ public reputation can bring competitive advantages to industry insiders. However, as many studies extensively demonstrated before, manipulative campaigns by social bots do not spare the financial sector either. In this work, we show

The BPMN standard notation allows business process designers to model both intra-organizational processes and inter-organizational collaborations. A great effort has been devoted in proposing formal semantics for BPMN, and, fewer, in providing dedicated verification tools. Still, some advanced features of BPMN, namely communication or time-related constructs, are often set aside. This becomes an issue

Despite the number and variety of business process modelling languages and notations available in the Business Process Management field, all of them mainly focus on a single type of relationship holding between business process activities, namely the activity execution order within the control flow. However, other types of relationships may hold between activities (e.g., co-occurrence or causal constraints)

Temperature deviations are critical in a pharmaceutical supply chain (SC) due to quality deterioration concerns and resulting health risks. The current solutions ensuring temperature maintenance are either labor-intensive or prone to triggering alarms that require no corrective measures, which, in turn, increase the alarm investigation costs. Machine learning (ML) methods have fared well both in the

Feature selection refers to selecting optimal feature subset with effective data preprocessing policy in making high dimensional data for diverse pattern recognition problems. The aims of feature selection are enhancing accuracy, improving the evaluation performance, and finding the smallest effective feature subset. In this study, ensemble feature selection method is adopted based on an assumption

The problem of answering rank/select queries over a bitmap is of utmost importance for many succinct data structures. When the bitmap does not change, many solutions exist in the theoretical and practical side. In this work we consider the case where one is allowed to modify the bitmap via a flip (i) operation that toggles its ith bit. By adapting and properly extending some results concerning prefix-sum

The disasters often lead to panic among people resulting into life threatening crowd movements. An efficient and systematic evacuation can mitigate the impact of devastation on human life. The evacuation, on the wake of extreme events, requires delay-sensitive computation and energy conserving mechanisms for power constrained data collection devices. In this paper, an IoT based Fog-Cloud assisted layered

The sports industry utilizes science to improve short to long-term team and player management regarding budget, health, tactics, training, and most importantly performance. Data Science (DS) and Sports Analytics play key roles in supporting teams, players and experts to improve performance. This paper reviews the literature to identify important attributes correlated with injuries and attempts to quantify

Characteristic sets (CS) organize RDF triples based on the set of properties associated with their subject nodes. This concept was recently used in indexing techniques, as it can capture the implicit schema of RDF data. While most CS-based approaches yield significant improvements in space and query performance, they fail to perform well when answering complex query workloads in the presence of schema

The popularity of the Resource Description Framework (RDF) and SPARQL has thrust the development of high-performance systems to manage data represented with this model. Former approaches adapted the well-established relational model applying its storage, query processing, and optimization strategies. However, the borrowed techniques from the relational model are not universally applicable in the RDF

Conformance checking unleashes the full power of process mining: techniques from this discipline enable the analysis of the quality of a process model through the discovery of event data, the identification of potential deviations, and the projection of real traces onto process models. In this way, the insights gained from the available event data can be transferred to a richer conceptual level, amenable

Review websites allow users to share their reviews of products or businesses, give ratings to products or businesses, and interact with other users. Due to the rapid growth of online review data, users face the problem of information overload. To resolve this problem, many researches have proposed various recommendation methods based on the analysis of users’ ratings. Besides user ratings, the review

The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. In this paper, we introduce COOL, a framework devised for COnversational OLap applications. COOL interprets and translates a natural language dialogue into an OLAP session that starts with a GPSJ (Generalized Projection, Selection, and Join) query

Social networks have generated immense amounts of data that have been successfully utilized for research and business purposes. The approachability and immediacy of social media have also allowed ill-intentioned users to perform several harmful activities that include spamming, promoting, and phishing. These activities generate massive amounts of low-quality content that often exhibits duplicate, automated

Regression Models (RMs) and Machine Learning models (ML) in general, aim to offer high prediction accuracy, even for unforeseen queries/datasets. This depends on their fundamental ability to generalize. However, overfitting a model, with respect to the current DB state, may be best suited to offer excellent accuracy. This overfit-generalize divide bears many practical implications faced by a data analyst

The advancements in positioning technologies have led to the emergence of various location-based services, resulting in a drastic increase in location-based data generation, producing big-data. Location data are often linked with user privacy, as they can reveal sensitive information such as the places visited by a person. Moreover, most location-based services involve resource-constrained devices

Industry 4.0 will only become a reality through the convergence of Operational and Information Technologies (OT & IT), which use different computation and communication technologies. Cloud Computing cannot be used for OT involving industrial applications, since it cannot guarantee stringent non-functional requirements, e.g., dependability, trustworthiness and timeliness. Instead, a new computing paradigm

Initially, process mining focused on discovering process models from event data, but in recent years the use and importance of conformance checking has increased. Conformance checking aims to uncover differences between a process model and an event log. Many conformance checking techniques and measures have been proposed. Typically, these take into account the frequencies of traces in the event log

Multi-model DBMSs (MMDBMSs) have been recently introduced to store and seamlessly query heterogeneous data (structured, semi-structured, graph-based, etc.) in their native form, aimed at effectively preserving their variety. Unfortunately, when it comes to analyzing these data, traditional data warehouses (DWs) and OLAP systems fall short because they rely on relational DBMSs for storage and querying