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Cube query interestingness: Novelty, relevance, peculiarity and surprise Inform. Syst. (IF 3.7) Pub Date : 2024-03-21 Dimos Gkitsakis, Spyridon Kaloudis, Eirini Mouselli, Veronika Peralta, Patrick Marcel, Panos Vassiliadis
In this paper, we discuss methods to assess the interestingness of a query in an environment of data cubes. We assume a hierarchical multidimensional database, storing data cubes and level hierarchies. We start with a comprehensive review of related work in the fields of human behavior studies and computer science. We define the interestingness of a query as a vector of scores along different aspects
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The rise of nonnegative matrix factorization: Algorithms and applications Inform. Syst. (IF 3.7) Pub Date : 2024-03-21 Yi-Ting Guo, Qin-Qin Li, Chun-Sheng Liang
Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization methods result in misleading results and waste of computing resources due to lack of timely optimization and case-by-case consideration. Therefore, an up-to-date and comprehensive review on its algorithms and applications is needed to promote improvement and applications for NMF. Here, we start with introducing
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A graph neural network with topic relation heterogeneous multi-level cross-item information for session-based recommendation Inform. Syst. (IF 3.7) Pub Date : 2024-03-20 Fan Yang, Dunlu Peng
The aim of session-based recommendation (SBR) mainly analyzes the anonymous user’s historical behavior records to predict the next possible interaction item and recommend the result to the user. However, due to the anonymity of users and the sparsity of behavior records, recommendation results are often inaccurate. The existing SBR models mainly consider the order of items within a session and rarely
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An inter-modal attention-based deep learning framework using unified modality for multimodal fake news, hate speech and offensive language detection Inform. Syst. (IF 3.7) Pub Date : 2024-03-16 Eniafe Festus Ayetiran, Özlem Özgöbek
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SuperGuardian: Superspreader removal for cardinality estimation in data streaming Inform. Syst. (IF 3.7) Pub Date : 2024-02-17 Jie Lu, Hongchang Chen, Penghao Sun, Tao Hu, Zhen Zhang, Quan Ren
Measuring flow cardinality is one of the fundamental problems in data stream mining, where a data stream is modeled as a sequence of items from different flows and the cardinality of a flow is the number of distinct items in the flow. Many existing sketches based on estimator sharing have been proposed to deal with huge flows in data streams. However, these sketches suffer from inefficient memory usage
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A survey for managing temporal data in RDF Inform. Syst. (IF 3.7) Pub Date : 2024-02-17 Di Wu, Hsien-Tseng Wang, Abdullah Uz Tansel
The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description
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ArZiGo: A recommendation system for scientific articles Inform. Syst. (IF 3.7) Pub Date : 2024-02-06 Iratxe Pinedo, Mikel Larrañaga, Ana Arruarte
The large number of scientific publications around the world is increasing at a rate of approximately 4%–5% per year. This fact has resulted in the need for tools that deal with relevant and high-quality publications. To address this necessity, search and reference management tools that include some recommendation algorithms have been developed. However, many of these solutions are proprietary tools
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Temporal representation and reasoning in data-intensive systems Inform. Syst. (IF 3.7) Pub Date : 2024-02-06 Alexander Artikis, Roberto Posenato, Stefano Tonetta
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An improved context-aware weighted matrix factorization algorithm for point of interest recommendation in LBSN Inform. Syst. (IF 3.7) Pub Date : 2024-02-05 Xu Zhou, Zhuoran Wang, Xuejie Liu, Yanheng Liu, Geng Sun
The point of interest (POI) recommendation algorithm in location based social network (LBSN) can assist people to find more appealing locations and satisfy their specific demands. However, it is challengeable to infer user’s preference due to the sparsity of the user’s check-in data. To address the problem and improve recommendation performance, this paper proposes an improved context-aware weighted
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Model repair supported by frequent anomalous local instance graphs Inform. Syst. (IF 3.7) Pub Date : 2024-01-29 Laura Genga, Fabio Rossi, Claudia Diamantini, Emanuele Storti, Domenico Potena
Model repair techniques aim at automatically updating a process model to incorporate behaviors that are observed in reality but are not compliant with the original model. Most state-of-the-art techniques focus on the fitness of the repaired models, with the goal of including single anomalous behaviors observed in a log in the form of the events. This often hampers the precision of the obtained models
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Corrigendum to “BPMN 2.0 OR-Join Semantics: Global and local characterisation” [Information Systems 105 (2022), 101934] Inform. Syst. (IF 3.7) Pub Date : 2024-01-26 Asvin Goel
Abstract not available
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Paraconsistent reasoning for inconsistency measurement in declarative process specifications Inform. Syst. (IF 3.7) Pub Date : 2024-01-24 Carl Corea, Isabelle Kuhlmann, Matthias Thimm, John Grant
Inconsistency is a core problem in fields such as AI and data-intensive systems. In this work, we address the problem of inconsistency in declarative process specifications, with an emphasis on linear temporal logic (LTL). As we will show, existing inconsistency measures for classical logic cannot provide a meaningful assessment of inconsistency in LTL in general, as they cannot adequately handle the
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Enterprise sellers’ satisfaction with business-to-business cross-border e-commerce platforms: Alibaba.com as an example Inform. Syst. (IF 3.7) Pub Date : 2024-01-24 Jhong-Min Yang, Yu-Xin Xiang, Chi-Wen Liu
Seller satisfaction of using a cross-border e-commerce (CBEC) platform is crucial for the platform's continual use by sellers. Furthermore, seller satisfaction is an evaluation reference for companies developing a CBEC business strategy. In this study, factors that affect enterprise sellers’ satisfaction of using a platform were investigated using Alibaba.com as a business-to-business CBEC platform
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Secure multi-dimensional data retrieval with access control and range query in the cloud Inform. Syst. (IF 3.7) Pub Date : 2024-01-23 Zhuolin Mei, Jin Yu, Caicai Zhang, Bin Wu, Shimao Yao, Jiaoli Shi, Zongda Wu
Outsourcing data to the cloud offers various advantages, such as improved reliability, enhanced flexibility, accelerated deployment, and so on. However, data security concerns arise due to potential threats such as malicious attacks and internal misuse of privileges, resulting in data leakage. Data encryption is a recognized solution to address these issues and ensure data confidentiality even in the
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Enhancing business process simulation models with extraneous activity delays Inform. Syst. (IF 3.7) Pub Date : 2024-01-22 David Chapela-Campa, Marlon Dumas
Business Process Simulation (BPS) is a common approach to estimate the impact of changes to a business process on its performance measures. For example, it allows us to estimate what would be the cycle time of a process if we automated one of its activities, or if some resources become unavailable. The starting point of BPS is a business process model annotated with simulation parameters (a BPS model)
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Repairing raw metadata for metadata management Inform. Syst. (IF 3.7) Pub Date : 2024-01-20 Hiba Khalid, Esteban Zimányi
With the exponential growth of data production, the generation of metadata has become an integral part of the process. Metadata plays a crucial role in facilitating enhanced data analytics, data integration, and resource management by offering valuable insights. However, inconsistencies arise due to deviations from standards in metadata recording, including missing attribute information, publishing
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Filtering with relational similarity Inform. Syst. (IF 3.7) Pub Date : 2024-01-20 Vladimir Mic, Pavel Zezula
For decades, the success of the similarity search has been based on detailed quantifications of pairwise similarities of objects. Currently, the search features have become much more precise but also bulkier, and the similarity computations are more time-consuming. We show that nearly no precise similarity quantifications are needed to evaluate the nearest neighbours (NN) queries that dominate real-life
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Process Query Language: Design, Implementation, and Evaluation Inform. Syst. (IF 3.7) Pub Date : 2024-01-05 Artem Polyvyanyy, Arthur H.M. ter Hofstede, Marcello La Rosa, Chun Ouyang, Anastasiia Pika
Organizations can benefit from the use of practices, techniques, and tools from the area of business process management. Through the focus on processes, they create process models that require management, including support for versioning, refactoring and querying. Querying thus far has primarily focused on structural properties of models rather than on exploiting behavioral properties capturing aspects
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Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers Inform. Syst. (IF 3.7) Pub Date : 2023-12-23 Marco Siino, Ilenia Tinnirello, Marco La Cascia
With the advent of the modern pre-trained Transformers, the text preprocessing has started to be neglected and not specifically addressed in recent NLP literature. However, both from a linguistic and from a computer science point of view, we believe that even when using modern Transformers, text preprocessing can significantly impact on the performance of a classification model. We want to investigate
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Explaining cube measures through Intentional Analytics Inform. Syst. (IF 3.7) Pub Date : 2023-12-18 Matteo Francia, Stefano Rizzi, Patrick Marcel
The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been
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HubHSP graph: Capturing local geometrical and statistical data properties via spanning graphs Inform. Syst. (IF 3.7) Pub Date : 2023-12-22 Stephane Marchand-Maillet, Edgar Chávez
The computation of a continuous generative model to describe a finite sample of an infinite metric space can prove challenging and lead to erroneous hypothesis, particularly in high-dimensional spaces. In this paper, we follow a different route and define the Hubness Half Space Partitioning graph (HubHSP graph). By constructing this spanning graph over the dataset, we can capture both the geometrical
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A screenshot-based task mining framework for disclosing the drivers behind variable human actions Inform. Syst. (IF 3.7) Pub Date : 2023-12-21 A. Martínez-Rojas, A. Jiménez-Ramírez, J.G. Enríquez, H.A. Reijers
Robotic Process Automation (RPA) enables subject matter experts to use the graphical user interface as a means to automate and integrate systems. This is a fast method to automate repetitive, mundane tasks. To avoid constructing a software robot from scratch, Task Mining approaches can be used to monitor human behavior through a series of timestamped events, such as mouse clicks and keystrokes. From
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Foundations and practice of binary process discovery Inform. Syst. (IF 3.7) Pub Date : 2023-12-20 Tijs Slaats, Søren Debois, Christoffer Olling Back, Axel Kjeld Fjelrad Christfort
Most contemporary process discovery methods take as inputs only positive examples of process executions, and so they are one-class classification algorithms. However, we have found negative examples to also be available in industry, hence we build on earlier work that treats process discovery as a binary classification problem. This approach opens the door to many well-established methods and metrics
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Heterogeneous graph neural networks for fraud detection and explanation in supply chain finance Inform. Syst. (IF 3.7) Pub Date : 2023-12-11 Bin Wu, Kuo-Ming Chao, Yinsheng Li
It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers’ transactions in an ongoing business are inspected to support the providers’ decision on whether to lend the money. Considering multiple participants in a supply chain business, the borrowers may use sophisticated tricks to cheat, making fraud detection challenging. In this work
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LSPC: Exploring contrastive clustering based on local semantic information and prototype Inform. Syst. (IF 3.7) Pub Date : 2023-12-13 Jun-Fen Chen, Lang Sun, Bo-Jun Xie
Recently years, several prominent contrastive learning algorithms, a kind of self-supervised learning methods, have been extensively studied that can efficiently extract useful feature representations from input images by means of data augmentation techniques. How to further partition the representations into meaningful clusters is the issue that deep clustering is addressing. In this work, a deep
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Attention-based multi attribute matrix factorization for enhanced recommendation performance Inform. Syst. (IF 3.7) Pub Date : 2023-12-09 Dongsoo Jang, Qinglong Li, Chaeyoung Lee, Jaekyeong Kim
In E-commerce platforms, auxiliary information containing several attributes (e.g., price, quality, and brand) can improve recommendation performance. However, previous studies used a simple combined embedding approach that did not consider the importance of each attribute embedded in the auxiliary information or only used some attributes of the auxiliary information. However, user purchasing behavior
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An efficient visual exploration approach of geospatial vector big data on the web map Inform. Syst. (IF 3.7) Pub Date : 2023-12-09 Zebang Liu, Luo Chen, Mengyu Ma, Anran Yang, Zhinong Zhong, Ning Jing
The visual exploration of geospatial vector data has become an increasingly important part of the management and analysis of geospatial vector big data (GVBD). With the rapid growth of data scale, it is difficult to realize efficient visual exploration of GVBD by current visualization technologies even if parallel distributed computing technology is adopted. To fill the gap, this paper proposes a visual
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Validation set sampling strategies for predictive process monitoring Inform. Syst. (IF 3.7) Pub Date : 2023-12-07 Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Previous studies investigating the efficacy of long short-term memory (LSTM) recurrent neural networks in predictive process monitoring and their ability to capture the underlying process structure have raised concerns about their limited ability to generalize to unseen behavior. Event logs often fail to capture the full spectrum of behavior permitted by the underlying processes. To overcome these
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On tuning parameters guiding similarity computations in a data deduplication pipeline for customers records: Experience from a R&D project Inform. Syst. (IF 3.7) Pub Date : 2023-12-04 Witold Andrzejewski, Bartosz Bębel, Paweł Boiński, Robert Wrembel
Data stored in information systems are often erroneous. Duplicate data are one of the typical error type. To discover and handle duplicates, the so-called deduplication methods are applied. They are complex and time costly algorithms. In data deduplication, pairs of records are compared and their similarities are computed. For a given deduplication problem, challenging tasks are: (1) to decide which
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Worker similarity-based noise correction for crowdsourcing Inform. Syst. (IF 3.7) Pub Date : 2023-11-30 Yufei Hu, Liangxiao Jiang, Wenjun Zhang
Crowdsourcing offers a cost-effective way to obtain multiple noisy labels for each instance by employing multiple crowd workers. Then label integration is used to infer its integrated label. Despite the effectiveness of label integration algorithms, there always remains a certain degree of noise in the integrated labels. Thus noise correction algorithms have been proposed to reduce the impact of noise
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An empirical evaluation of unsupervised event log abstraction techniques in process mining Inform. Syst. (IF 3.7) Pub Date : 2023-11-25 Greg Van Houdt, Massimiliano de Leoni, Niels Martin, Benoît Depaire
These days, businesses keep track of more and more data in their information systems. Moreover, this data becomes more fine-grained than ever, tracking clicks and mutations in databases at the lowest level possible. Faced with such data, process discovery often struggles with producing comprehensible models, as they instead return spaghetti-like models. Such finely granulated models do not fit the
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A novel self-supervised graph model based on counterfactual learning for diversified recommendation Inform. Syst. (IF 3.7) Pub Date : 2023-11-29 Pu Ji, Minghui Yang, Rui Sun
Consumers’ needs present a trend of diversification, which causes the emergence of diversified recommendation systems. However, existing diversified recommendation research mostly focuses on objective function construction rather than on the root cause that limits diversity—namely, imbalanced data distribution. This study considers how to balance data distribution to improve recommendation diversity
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CoCo-trie: Data-aware compression and indexing of strings Inform. Syst. (IF 3.7) Pub Date : 2023-11-17 Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra
We address the problem of compressing and indexing a sorted dictionary of strings to support efficient lookups and more sophisticated operations, such as prefix, predecessor, and range searches. This problem occurs as a key task in a plethora of applications, and thus it has been deeply investigated in the literature since the introduction of tries in the ’60s. We introduce a new data structure, called
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Big data analytics deep learning techniques and applications: A survey Inform. Syst. (IF 3.7) Pub Date : 2023-11-21 Hend A. Selmy, Hoda K. Mohamed, Walaa Medhat
Deep learning (DL), as one of the most active machine learning research fields, has achieved great success in numerous scientific and technological disciplines, including speech recognition, image classification, language processing, big data analytics, and many more. Big data analytics (BDA), where raw data is often unlabeled or uncategorized, can greatly benefit from DL because of its ability to
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Document structure-driven investigative information retrieval Inform. Syst. (IF 3.7) Pub Date : 2023-11-19 Tuomas Ketola, Thomas Roelleke
Data-driven investigations are increasingly dealing with non-moderated, non-standard and even manipulated information Whether the field in question is journalism, law enforcement, or insurance fraud it is becoming more and more difficult for investigators to verify the outcomes of various black-box systems To contribute to this need of discovery methods that can be used for verification, we introduce
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Novel diversified echo state network for improved accuracy and explainability of EEG-based stroke prediction Inform. Syst. (IF 3.7) Pub Date : 2023-11-15 Samar Bouazizi, Hela Ltifi
Echo State Networks (ESNs) are a powerful machine learning technique that can be used for EEG-based stroke prediction. However, conventional ESNs suffer from two main limitations: they are not always accurate, and they are not always interpretable. This paper presents a novel multi-level framework that addresses these limitations. The framework consists of three main components: optimized feature extraction
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Reproducible experiments for generating pre-processing pipelines for AutoETL Inform. Syst. (IF 3.7) Pub Date : 2023-11-02 Joseph Giovanelli, Besim Bilalli, Alberto Abelló, Fernando Silva-Coira, Guillermo de Bernardo
This work is a companion reproducibility paper of the experiments and results reported in Giovanelli et al. (2022), where data pre-processing pipelines are evaluated in order to find pipeline prototypes that reduce the classification error of supervised learning algorithms. With the recent shift towards data-centric approaches, where instead of the model, the dataset is systematically changed for better
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Measuring rule-based LTLf process specifications: A probabilistic data-driven approach Inform. Syst. (IF 3.7) Pub Date : 2023-11-02 Alessio Cecconi, Luca Barbaro, Claudio Di Ciccio, Arik Senderovich
Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces LTLf. In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However,
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A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study Inform. Syst. (IF 3.7) Pub Date : 2023-10-30 Mahei Manhai Li, Philipp Reinhard, Christoph Peters, Sarah Oeste-Reiss, Jan Marco Leimeister
The adoption of innovative technologies confronts IT-Service-Management (ITSM) with an increasing volume and variety of requests. Artificial intelligence (AI) possesses the potential to augment customer service employees. However, the training data for AI systems are annotated by domain experts with little interest in labeling correctly due to their limited perceived value. Ultimately, insufficient
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Adoption of IT solutions: A data-driven analysis approach Inform. Syst. (IF 3.7) Pub Date : 2023-10-24 Iris Reinhartz-Berger, Alan Hartman, Doron Kliger
Many IT departments provide solutions that satisfy a variety of needs to deliver services and reach business goals. These IT solutions may fall short of addressing all the requirements of the relevant business units and hence are only partially adopted by some of them. The objective of this research is to develop an analysis method that supports the selection of solutions whose prospects of adoption
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An analysis of ensemble pruning methods under the explanation of Random Forest Inform. Syst. (IF 3.7) Pub Date : 2023-10-19 Faten A. Khalifa, Hatem M. Abdelkader, Asmaa H. Elsaid
“Black box” models created by modern machine learning techniques are typically hard to interpret. Thus, the necessity of explainable artificial intelligence (XAI) has grown for understanding the rationale behind those models and converting them into white boxes. Random Forest is a black box model essential in various domains due to its flexibility, ease of use, and remarkable predictive performance
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ECG classification with learning ensemble based on symbolic discretization Inform. Syst. (IF 3.7) Pub Date : 2023-10-18 Mariem Taktak, Hela Ltifi, Mounir Ben Ayed
This paper introduces a novel learning ensemble algorithm designed for the classification of Electro-Cardio Graphic (ECG) signals. In real-time monitoring of cardiovascular patients, addressing the scalability challenge requires an adapted representation that enhances dimensionality reduction before the classification process. Our approach focuses on a discretization technique that transforms Time
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GSM: A generalized approach to Supervised Meta-blocking for scalable entity resolution Inform. Syst. (IF 3.7) Pub Date : 2023-10-17 Luca Gagliardelli, George Papadakis, Giovanni Simonini, Sonia Bergamaschi, Themis Palpanas
Entity Resolution (ER) constitutes a core data integration task that relies on Blocking in order to tame its quadratic time complexity. Schema-agnostic blocking achieves very high recall, requires no domain knowledge and applies to data of any structuredness and schema heterogeneity. This comes at the cost of many irrelevant candidate pairs (i.e., comparisons), which can be significantly reduced through
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A taxonomy and ontology for digital platforms Inform. Syst. (IF 3.7) Pub Date : 2023-10-10 Thomas Derave, Frederik Gailly, Tiago Prince Sales, Geert Poels
In academic literature and in business communication digital platforms are categorized into different types including, but not limited to, multi-sided platform, digital marketplace, on-demand platform and sharing economy platform. Observing the substantial literature on these platform types, both in academia and professional contexts, there is lack of consensus on the definition of these digital platform
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Discovery, simulation, and optimization of business processes with differentiated resources Inform. Syst. (IF 3.7) Pub Date : 2023-10-09 Orlenys López-Pintado, Marlon Dumas, Jonas Berx
Business process simulation is a versatile technique to predict the impact of one or more changes on the performance of a process. Process simulation is often used to identify sets of changes that optimize one or more performance measures. Mainstream approaches to process simulation suffer from various limitations, some stemming from the fact that they treat resources as undifferentiated entities grouped
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Data augmentation via context similarity: An application to biomedical Named Entity Recognition Inform. Syst. (IF 3.7) Pub Date : 2023-10-05 Ilaria Bartolini, Vincenzo Moscato, Marco Postiglione, Giancarlo Sperlì, Andrea Vignali
In this paper, we present COntext SImilarity-based data augmentation for NER (COSINER), a new method for improving Named Entity Recognition (NER) tasks using data augmentation. Unlike current techniques, which may generate noisy and mislabeled samples through text manipulation, COSINER uses context similarity to replace entity mentions with more plausible ones on the basis of available training data
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Reinforcement learning with time intervals for temporal knowledge graph reasoning Inform. Syst. (IF 3.7) Pub Date : 2023-10-07 Ruinan Liu, Guisheng Yin, Zechao Liu, Ye Tian
Temporal reasoning methods have been successful in temporal knowledge graphs (TKGs) and are widely employed in many downstream application areas. Most existing TKG reasoning models transform time intervals into continuous time snapshots, with each snapshot representing a subgraph of the TKG. Although such processing can produce satisfactory outcomes, it ignores the integrity of a time interval and
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Medoid Silhouette clustering with automatic cluster number selection Inform. Syst. (IF 3.7) Pub Date : 2023-10-06 Lars Lenssen, Erich Schubert
The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its
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Supervisory control of business processes with resources, parallel and mutually exclusive branches, loops, and uncertainty Inform. Syst. (IF 3.7) Pub Date : 2023-10-05 Davide Bresolin, Matteo Zavatteri
A recent direction in Business Process Management studied methodologies to control the execution of Business Processes under several sources of uncertainty in order to always get to the end by satisfying all constraints. Current approaches encode business processes into temporal constraint networks or timed game automata in order to exploit their related strategy synthesis algorithms. However, the
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DB+-tree: A new variant of B+-tree for main-memory database systems Inform. Syst. (IF 3.7) Pub Date : 2023-09-21 Yongsik Kwon, Seonho Lee, Yehyun Nam, Joong Chae Na, Kunsoo Park, Sang K. Cha, Bongki Moon
The B-tree and its variants are an indispensable tool for database systems and applications. Hence the efficiency of the B-tree is one of the few critical factors that determine the performance of a database system. In main-memory database systems, the computational overhead intrinsic in the B-tree algorithms for branching becomes the dominant factor in performance. In this paper, we propose yet another
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Induced permutations for approximate metric search Inform. Syst. (IF 3.7) Pub Date : 2023-09-20 Lucia Vadicamo, Giuseppe Amato, Claudio Gennaro
Permutation-based Indexing (PBI) approaches have been proven to be particularly effective for conducting large-scale approximate metric searching. These methods rely on the idea of transforming the original metric objects into permutation representations, which can be efficiently indexed using data structures such as inverted files. The standard conceptualization of permutation associated with a metric
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AIMED: An automatic and incremental approach for business process model repair under concept drift Inform. Syst. (IF 3.7) Pub Date : 2023-09-20 Wei Guan, Jian Cao, Yang Gu, Shiyou Qian
Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However,
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AI-based decision support system for public procurement Inform. Syst. (IF 3.7) Pub Date : 2023-09-14 Lucia Siciliani, Vincenzo Taccardi, Pierpaolo Basile, Marco Di Ciano, Pasquale Lops
Tenders are powerful means of investment of public funds and represent a strategic development resource. Thus, improving the efficiency of procuring entities and developing evaluation models turn out to be essential to facilitate e-procurement procedures. With this contribution, we introduce our research to create a supporting system for the decision-making and monitoring process during the entire
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A survey of approaches for event sequence analysis and visualization Inform. Syst. (IF 3.7) Pub Date : 2023-09-09 Anton Yeshchenko, Jan Mendling
Event sequence data is increasingly available. Many business operations are supported by information systems that record transactions, events, state changes, message exchanges, and similar elements. This observation also applies to various industries, including production, logistics, healthcare, financial services, and education. The variety of application areas explains that techniques for event sequence
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KSGAN: Knowledge-aware subgraph attention network for scholarly community recommendation Inform. Syst. (IF 3.7) Pub Date : 2023-09-03 Qi Lu, Wei Du, Wei Xu, Jian Ma
On online scholarly platforms, recommending suitable communities to researchers matters for researchers’ communication and collaboration. Previous studies on community recommendation either treat a community as a single item or simply aggregate its member features while ignoring rich user interactions and side information in scholarly communities. Existing knowledge-aware recommenders fail to capture
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A framework for modeling, executing, and monitoring hybrid multi-process specifications with bounded global–local memory Inform. Syst. (IF 3.7) Pub Date : 2023-09-01 Anti Alman, Fabrizio Maria Maggi, Marco Montali, Fabio Patrizi, Andrey Rivkin
So far, approaches for business process modeling, enactment and monitoring have mainly been based on process specifications consisting of a single process model. This setting aptly captures monolithic scenarios from domains in which all possible behaviors can be folded into a single model. However, the same strategy cannot be applied to domains where multiple interacting (procedural) processes simultaneously
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Behavioral consistency measurement between extended WFD-nets Inform. Syst. (IF 3.7) Pub Date : 2023-08-31 Fang Zhao, Dongming Xiang, Guanjun Liu, Changjun Jiang
How to obtain the behavioral consistency degree of different process models in workflow systems is a key point in process matching, process fusion, and difference detection. Workflow net with data (WFD-net) and its extension (EWFD-net) are effective languages to describe and analyze the process model. In practice, checking the consistency of two EWFD-nets is a challenging task since many existing methods
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ClayRS: An end-to-end framework for reproducible knowledge-aware recommender systems Inform. Syst. (IF 3.7) Pub Date : 2023-08-26 Pasquale Lops, Marco Polignano, Cataldo Musto, Antonio Silletti, Giovanni Semeraro
Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of the information conveyed by textual content. Though rich and constantly evolving, the literature on knowledge-aware recommender systems is particularly scattered
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Local intrinsic dimensionality measures for graphs, with applications to graph embeddings Inform. Syst. (IF 3.7) Pub Date : 2023-08-19 Miloš Savić, Vladimir Kurbalija, Miloš Radovanović
The notion of local intrinsic dimensionality (LID) is an important advancement in data dimensionality analysis, with applications in data mining, machine learning and similarity search problems. Existing distance-based LID estimators were designed for tabular datasets encompassing data points represented as vectors in a Euclidean space. After discussing their limitations for graph-structured data considering