Information Systems最新文献

{"title":"Behavioral similarity in business process models: A perspective that needs more attention","authors":"Francesca Zampino ,&nbsp;Laura Genga ,&nbsp;Antonella Longo","doi":"10.1016/j.is.2025.102608","DOIUrl":"10.1016/j.is.2025.102608","url":null,"abstract":"<div><div>Although extensive research has explored business process model similarity, the coherence and structure of these studies remain underexplored. This paper systematically reviews the literature, with a particular focus on behavioral similarity. We conduct a systematic review of the literature on process model similarity, with a focus on two primary measurement approaches: trace-based and model-based similarity. Based on 99 reviewed studies, we developed a three-dimensional framework and conducted a quantitative comparison of selected similarity measures for deeper analysis. Our findings provide structured insights to strengthen the assessment of process model similarity, particularly from a behavioral perspective. The review process follows a six-phase systematic methodology, from the identification of relevant keywords to the creation of bibliographic maps that visually represent the findings. These insights offer a foundation for future research and practical applications within the field.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102608"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the compressibility of raster data","authors":"Martita Muñoz ,&nbsp;José Fuentes-Sepúlveda ,&nbsp;Cecilia Hernández ,&nbsp;Diego Seco","doi":"10.1016/j.is.2025.102624","DOIUrl":"10.1016/j.is.2025.102624","url":null,"abstract":"<div><div>The raster data model is widely used in Geographic Information Systems and image processing. The continuous growth of raster data volume poses significant challenges for storage and management. Compact representations of rasters have emerged as a critical solution to address this issue, leveraging data locality to achieve efficient compression. In this context, the research community has proposed compressibility measures aiming to estimate the compressibility of data. Some measures, initially proposed for sequences, have been extended to two- and three-dimensional matrices. This work conducts an experimental analysis of measures applied to raster data compressibility estimation. The first approach applies a linearization function on raster data with matrix representation and then uses existing one-dimensional compressibility measures. The evaluation of the approach compares 1D compressibility measures with 2D measures, data compressors, Compact Data Structures (CDSs), and spatial locality estimation techniques. The results show that spatial locality, alphabet size, and noise directly influence raster compressibility, having more impact over measures like <span><math><mi>z</mi></math></span>, <span><math><mi>v</mi></math></span>, and <span><math><mi>g</mi></math></span>, compressors (bzip, gzip) and a CDS called <span><math><msup><mrow><mi>k</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>-raster. The second approach introduces <span><math><msub><mrow><mi>δ</mi></mrow><mrow><mi>Δ</mi></mrow></msub></math></span>, a 2D compressibility measure sensitive to differences within the alphabet values. Its purpose is to refine the estimation of raster compressibility. Results indicate that <span><math><msub><mrow><mi>δ</mi></mrow><mrow><mi>Δ</mi></mrow></msub></math></span> is affected by the actual values and their frequencies, aligning with the outcomes of some specific compressors. This alignment underscores the suitability of <span><math><msub><mrow><mi>δ</mi></mrow><mrow><mi>Δ</mi></mrow></msub></math></span> for compressibility estimation tasks closely related to those performed by such compressors.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102624"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SaaMS: The synopses-as-a-microservice paradigm for scalable adaptive streaming analytics across the cloud to edge continuum","authors":"Georgios Panagiotis Kalfakis,&nbsp;Nikos Giatrakos","doi":"10.1016/j.is.2025.102629","DOIUrl":"10.1016/j.is.2025.102629","url":null,"abstract":"<div><div>The use of data synopses in Big streaming Data analytics can offer 3 types of scalability: (i) horizontal scalability, for scaling with the volume and velocity of Big streaming Data, (ii) vertical scalability, for scaling with the number of processed streams, and (iii) federated scalability, i.e. reducing the communication cost for performing global analytics across a number of geo-distributed data centers or devices in IoT settings. Despite the aforementioned virtues of synopses, no state-of-the-art Big Data framework or IoT platform provides a native API for stream synopses supporting all three types of required scalability. In this work, we fill this gap by introducing a novel system and architectural paradigm, namely Synopses-as-a-MicroService (SaaMS), for both parallel and geo-distributed stream summarization at scale. SaaMS is developed on Apache Kafka and Kafka Streams and can provide all the required types of scalability together with (i) the ability to seamlessly perform adaptive resource allocation with zero downtime for the running analytics and (ii) the ability to run both across powerful computer clusters and Java-enabled IoT devices. Therefore, SaaMS is directly deployable from applications that either operate on powerful clouds or across the cloud to edge continuum.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102629"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph functional dependencies: Analysis and translation to PG-schema","authors":"Maude Manouvrier,&nbsp;Khalid Belhajjame","doi":"10.1016/j.is.2025.102633","DOIUrl":"10.1016/j.is.2025.102633","url":null,"abstract":"<div><div>Officially published as an ISO/IEC standard in April 2024, the Graph Query Language (GQL) aims to establish itself as the standard language for querying graph data, much like SQL is for relational data. The graph database community has also recently introduced additional specifications, such as PG-Key and, later, PG-Schema, to define graph schemas and dependencies. At the same time, several proposals have emerged in the literature to express Functional Dependency constraints in graph data. Given the wide range of Graph Dependencies presented in the literature, the first contribution of this article is a survey of Graph Functional Dependencies in existing proposals, highlighting the most significant ones, their differences, and their relative expressiveness. In a second contribution, we align with the goals of the graph database community by proposing mappings to translate different kinds of Graph Functional Dependencies from the literature into PG-Schema-compliant dependencies. These mappings are implemented within a publicly available tool <strong>PG-FD</strong>, which to our knowledge, is the first solution capable of transforming Graph Dependencies into the PG-Schema standard while fully preserving their semantics.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102633"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the lifecycle economics of AI: The levelized cost of artificial intelligence (LCOAI)","authors":"Eliseo Curcio","doi":"10.1016/j.is.2025.102634","DOIUrl":"10.1016/j.is.2025.102634","url":null,"abstract":"<div><div>As artificial intelligence (AI) becomes foundational to enterprise infrastructure, organizations face growing challenges in accurately assessing the full economic implications of AI deployment. Existing metrics such as API token costs, GPU-hour billing, or Total Cost of Ownership (TCO) fail to capture the complete lifecycle costs of AI systems and provide limited comparability across deployment models. This paper introduces the Levelized Cost of Artificial Intelligence (LCOAI), a standardized economic metric designed to quantify the total capital (CAPEX) and operational (OPEX) expenditures per unit of productive AI output, normalized by valid inference volume. Analogous to established metrics like the Levelized Cost of Electricity (LCOE) and the Levelized Cost of Hydrogen (LCOH) in the energy sector, LCOAI provides a rigorous, transparent framework for evaluating and comparing AI deployment strategies. We define the LCOAI methodology in detail and apply it to four representative scenarios OpenAI GPT-4.1 API, Anthropic Claude Haiku API, a self-hosted LLaMA-2–13B deployment, and a cloud-hosted LLaMA-2–13B deployment demonstrating how LCOAI captures critical trade-offs in scalability, investment planning, and cost optimization. Extensive sensitivity analyses further explore the impact of inference volume, CAPEX, and OPEX variability on lifecycle economics. The results illustrate the practical utility of LCOAI in procurement, infrastructure planning, and automation strategy, and establish it as a foundational benchmark for AI economic analysis. Policy implications and directions for future refinement, including integration of environmental and performance-adjusted cost metrics, are also discussed.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102634"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BBQ-Tree: A unified classifier and regressor combining Boolean and quantum logic decisions","authors":"Alexander Stahl,&nbsp;Ingo Schmitt","doi":"10.1016/j.is.2025.102632","DOIUrl":"10.1016/j.is.2025.102632","url":null,"abstract":"<div><div>This article provides a detailed explanation of the BBQ-Tree, a unified logic-based model that integrates both classical Decision Trees and Quantum-Logic Decision Trees into a generalized framework for classification and regression. As it combines these paradigms, the BBQ-Tree effectively addresses problems with both linear and curved decision boundaries while prioritizing interpretability. We provide a detailed description of the underlying concepts, a possible training algorithm, experimental evaluations and the incorporation of regression functionality, broadening its applicability beyond classification tasks. Strategies for efficient training and model optimization are also presented. Experimental results demonstrate that the BBQ-Tree produces compact, interpretable models capable of revealing data trends, while achieving accuracy comparable to Decision Trees. Furthermore, its new regression capabilities highlight its versatility and performance across a wider range of tasks.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102632"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145332251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DaMoOp: A global approach for optimizing denormalized schemas through a multidimensional cost model","authors":"Jihane Mali ,&nbsp;Shohreh Ahvar ,&nbsp;Faten Atigui ,&nbsp;Ahmed Azough ,&nbsp;Nicolas Travers","doi":"10.1016/j.is.2025.102598","DOIUrl":"10.1016/j.is.2025.102598","url":null,"abstract":"<div><div>The complexity of database systems has increased alongside the exponential growth of data, necessitating Information Systems (IS) architects to continuously refine data models and meticulously select storage and management options that align with requirements. While existing solutions focus on data model transformation, none offer guidance in selecting the most suitable data model for a given use case. In this context, we propose <span>DaMoOp</span>, an automated approach for leading data model selection process. <span>DaMoOp</span> starts from a conceptual model and associated use case comprising queries, settings and infrastructure constraints, to generate relevant logical data models. A cost model, considering environmental, financial, and temporal factors, facilitates comparison and selection of the most suitable data model. Our cost model incorporates both data model and queries costs. Additionally, we suggest a data model selection process that enhances the ability to choose the optimal data model(s) for a specific use case, while also adapting to rapidly evolving use cases. We provide a strategic optimization approach designed to identify the most cost-efficient and stable data model as use case scenarios evolve. Moreover, we offer a simulation tool for the entire process, which enables visualizing the impact of use case variations on data model costs, thus empowering IS architects to make informed decisions.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102598"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In system alignments we trust! Explainable alignments via projections","authors":"Dominique Sommers,&nbsp;Natalia Sidorova,&nbsp;Boudewijn van Dongen","doi":"10.1016/j.is.2025.102631","DOIUrl":"10.1016/j.is.2025.102631","url":null,"abstract":"<div><div>Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation – either a log or a model – but not in the other. Since processes involve multiple entities, such as objects and resources performing different tasks with objects, the interaction of these entities must be taken into account in the alignments. Additionally, both logged and modeled representations of reality may be imprecise and only partially represent some of these entities, but not all. In this paper, we introduce the concept of “relaxations” through projections for alignments to deal with partially correct models and logs. Relaxed alignments help to distinguish between trustworthy and untrustworthy content of the two representations (the log and the model) to achieve a better understanding of the underlying process and expose quality issues.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102631"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effects of database normalization on decision support system performance","authors":"Marin Fotache ,&nbsp;Marius-Iulian Cluci ,&nbsp;Toni Taipalus ,&nbsp;George Talaba","doi":"10.1016/j.is.2025.102636","DOIUrl":"10.1016/j.is.2025.102636","url":null,"abstract":"<div><div>Relational database normalization strives to minimize update anomalies and data redundancy, often at the cost of performance. Transactional systems typically require a higher degree of normalization since data are updated more frequently than in read-intensive decision support systems. While these reasons for and effects of normalization can be considered common knowledge, there are hardly any empirical studies on the query performance implications of various degrees of normalization in decision support systems. That is, it seems that the magnitude of the effects of normalization is not widely understood, even though performance implications are of importance to managers and analysts utilizing decision analytics, and for end-user information needs to be timely satisfied. In this study, the effects of normalization on a decision support database were tested for three popular SQL/relational database servers. The results raise serious concerns about the conventional consensus on the performance gains incurred by the reduced number of table joins. Even for small-sized databases, the penalties due to the extra volume caused by redundancy associated with lower normal forms seem larger than the performance gains due to the reduced number of joins. These results have practical implications on which design principles should be followed for efficient decision support system databases.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102636"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-source data outlier detection based on secure multi-party computation","authors":"Lin Yao ,&nbsp;Zhaolong Zheng ,&nbsp;Tian Wei ,&nbsp;Guowei Wu","doi":"10.1016/j.is.2025.102597","DOIUrl":"10.1016/j.is.2025.102597","url":null,"abstract":"<div><div>Outlier detection has been applied to many fields such as financial fraud, fault detection, and health diagnosis as an important technology to discover abnormal data. Data sharing is required to perform outlier detection on multi-source data. However, data sharing between multi-source generally discloses privacy embedded within the data such as sensitive patient information. With the increasing emphasis on personal privacy, it is necessary to study how to achieve outlier detection for multi-source data while preserving privacy. Secure Multi-Party Computation (SMPC) is a privacy-preserving technology to achieve secure calculation between multi-source in the absence of a trusted third party. But due to frequent data interaction, high complexity and low practicability comes with complex calculations. In this paper, we propose a secure multi-source data outlier detection scheme based on SMPC. Our scheme uses homomorphic encryption and perturbation to preserve the critical process of calculating the global distance matrix, which greatly reduces the complexity of the secure calculation process. Besides, we design an outlier determination strategy to reduce the steps of searching reverse neighbors and calculating the final local outlier factor. By comparison, our scheme outperforms the existing schemes in terms of accuracy ratio, running time and efficiency.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102597"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}