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

Business process management has been widely adopted by many organisations, 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

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

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

Conformance checking is an area of process mining that studies methods for measuring and characterizing commonalities and discrepancies between processes recorded in event logs of IT-systems and designed processes, either captured in explicit process models or implicitly induced by information systems. Applications of conformance checking range from measuring the quality of models automatically discovered

Energy awareness presents an immense challenge for cloud computing infrastructure and the development of next generation data centers. Virtual Machine (VM) consolidation is one technique that can be harnessed to reduce energy related costs and environmental sustainability issues of data centers. In recent times intelligent learning approaches have proven to be effective for managing resources in cloud

Multi-label document classification has a broad range of applicability to various practical problems, such as news article topic tagging, sentiment analysis, medical code classification, etc. A variety of approaches (e.g., tree-based methods, neural networks and deep learning systems that are specifically based on pre-trained language models) have been developed for multi-label document classification

Principal Component Analysis (PCA) is a widely popular technique for reducing the dimensionality of a dataset. Interestingly, when dimensions of the dataset grow too large, existing state-of-the-art methods for PCA face scalability issue due to the explosion of intermediate data. Moreover, in a geographically distributed environment where most of today’s data are originally generated, these methods

Processes are a crucial artifact in organizations, since they coordinate the execution of activities so that products and services are provided. The use of models to analyze the underlying processes is a well-known practice. However, due to the complexity and continuous evolution of their processes, organizations need an effective way of analyzing the relation between processes and models. Conformance

In the real world, data streams are ubiquitous — think of network traffic or sensor data. Mining patterns, e.g., outliers or clusters, from such data must take place in real time. This is challenging because (1) streams often have high dimensionality, and (2) the data characteristics may change over time. Existing approaches tend to focus on only one aspect, either high dimensionality or the specifics

The massive amounts of data processed in modern computational systems are becoming a problem of increasing importance. This data is commonly stored directly or indirectly through the use of data exchange languages, such as JSON (JavaScript Object Notation) and XML (eXtensible Markup Language), for human-readable platform-agnostic access. This paper focuses on exploring a set of succinct representations

E-mail tracking provides companies with fine-grained behavioral data about e-mail recipients, which can be a threat for individual privacy and enterprise security. This problem is especially severe since e-mail tracking techniques often gather data without the informed consent of the recipients. So far e-mail recipients lack a reliable protection mechanism. This article presents a novel protection

A fundamental problem in conformance checking is aligning event data with process models. Unfortunately, existing techniques for this task are either complex, or can only be applicable to restricted classes of models. This in practice means that for large inputs, current techniques often fail to produce a result. In this paper we propose a method to compute alignments for unconstrained process models

Digital transformation forces companies to rethink their processes to meet current customer needs. Business Process Management (BPM) can provide the means to structure and tackle this change. However, most approaches to BPM face restrictions on the number of processes they can optimize at a time due to complexity and resource restrictions. Investigating this shortcoming, the concept of the long tail

Massive amounts of data with spatio-temporal-textual information are being generated due to the proliferation of GPS-equipped mobile devices. Much of this data are social media posts, often used to share and spread personal updates and news. Exploring valuable information from a dynamic collection of social posts is of great interest and has attracted many studies. However, because the size of data

Along with the rapid development of socio-technical systems, people are playing an increasingly important role in information system and have actually become an essential system component. However, unlike technology-based attacks that have been investigated for decades, social engineering attacks have not been efficiently addressed. In particular, due to the interdisciplinary nature of social engineering

Cloud storage allows organizations to store data at remote sites of service providers. Although cloud storage services offer numerous benefits, they also involve new risks and challenges with respect to data security and privacy aspects. To preserve confidentiality, data must be encrypted before outsourcing to the cloud. Although this approach protects the security and privacy aspects of data, it also

Mixed-paradigm process models integrate strengths of procedural and declarative representations like Petri nets and Declare. They are specifically interesting for process mining because they allow capturing complex behaviour in a compact way. A key research challenge for the proliferation of mixed-paradigm models for process mining is the lack of corresponding conformance checking techniques. In this

Huge DBMSs storing genomic information are being created and engineerized for doing large-scale, comprehensive and in-depth analysis of human beings and their diseases. This paves the way for significant new approaches in medicine, but also poses major challenges for storing, processing and transmitting such big amounts of data in compliance with recent regulations concerning user privacy. We designed

The comparison of trace variants of business processes opens the door for a fine-grained analysis of the distinctive features inherent in the executions of a process in an organization. The current approaches for trace variant analysis do not consider the situation where a process model is present, and therefore, it can guide the derivation of the trace variants by considering high-level structures

Nowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards being the ultimate framework for streaming analytics. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP). The former enables stateful computation on infinite sequences of data items while the latter