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Estimating the compressibility of raster data 估计栅格数据的可压缩性

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-13 DOI: 10.1016/j.is.2025.102624

Martita Muñoz , José Fuentes-Sepúlveda , Cecilia Hernández , Diego Seco

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

z

v

, and

g

, compressors (bzip, gzip) and a CDS called

k^{2}

-raster. The second approach introduces

δ_{Δ}

, a 2D compressibility measure sensitive to differences within the alphabet values. Its purpose is to refine the estimation of raster compressibility. Results indicate that

δ_{Δ}

is affected by the actual values and their frequencies, aligning with the outcomes of some specific compressors. This alignment underscores the suitability of

δ_{Δ}

for compressibility estimation tasks closely related to those performed by such compressors.

栅格数据模型在地理信息系统和图像处理中有着广泛的应用。栅格数据量的不断增长对存储和管理提出了重大挑战。光栅的紧凑表示已经成为解决这个问题的关键解决方案，利用数据局部性来实现有效的压缩。在此背景下，研究界提出了旨在估计数据可压缩性的可压缩性度量。一些最初针对序列提出的测度，已经推广到二维和三维矩阵。本文对栅格数据压缩性估计方法进行了实验分析。第一种方法对矩阵表示的栅格数据应用线性化函数，然后使用现有的一维压缩性度量。该方法的评估比较了一维可压缩性度量与二维度量、数据压缩器、紧凑数据结构（cds）和空间局域估计技术。结果表明，空间局域性、字母大小和噪声直接影响栅格的可压缩性，对z、v和g、压缩器（bzip、gzip）和称为k2-栅格的CDS等措施的影响更大。第二种方法引入δΔ，这是一种2D可压缩性度量，对字母值之间的差异非常敏感。其目的是改进栅格可压缩性的估计。结果表明，δΔ受实际值及其频率的影响，与某些特定压缩机的结果一致。这种一致性强调了δΔ对压缩性估计任务的适用性，这些任务与这些压缩器执行的任务密切相关。

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引用次数: 0

Extended parameterized Burrows–Wheeler transform 扩展参数化Burrows-Wheeler变换

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-11 DOI: 10.1016/j.is.2025.102611

Eric M. Osterkamp , Dominik Köppl

The Burrows–Wheeler transform (BWT) lies at the heart of succinct and compressed full-text indexes for pattern matching queries. Notable variants are (a) the extended BWT (eBWT) capable to index multiple circular texts for pattern matching, or (b) the parameterized BWT (pBWT) for parameterized pattern matching. A natural extension is the combination of the virtues of both variants into a new data structure, whose name we coin with extended parameterized BWT (epBWT). We show that the epBWT supports pattern matching in context of parameterized pattern matching on multiple circular texts, within the same complexities as known solutions presented for the pBWT [Kim and Cho, IPL’21] for patterns not longer than the shortest indexed text. Additionally, we show how to compute the epBWT within the same complexities as [Iseri et al., ICALP’24], i.e., in compact space and quasilinear time. As an application, we extend the matching statistics problem to the parameterized pattern matching setting on circular texts.

Burrows-Wheeler变换（BWT）是用于模式匹配查询的简洁压缩全文索引的核心。值得注意的变体是(a)能够索引多个循环文本以进行模式匹配的扩展BWT (eBWT)，或(b)用于参数化模式匹配的参数化BWT （pBWT）。自然扩展是将这两种变体的优点结合到一个新的数据结构中，我们将其名称与扩展参数化BWT （epBWT）一起命名。我们证明了epBWT在多个圆形文本的参数化模式匹配背景下支持模式匹配，其复杂性与pBWT提出的解决方案相同[Kim和Cho， IPL ' 21]，适用于不超过最短索引文本的模式。此外，我们展示了如何在与[Iseri等人，ICALP ' 24]相同的复杂性下计算epBWT，即在紧空间和拟线性时间内。作为应用，我们将匹配统计问题扩展到圆形文本的参数化模式匹配设置。

引用次数: 0

Special issue on verification, control, and repair in business process management 关于业务流程管理中的验证、控制和修复的专刊

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-11 DOI: 10.1016/j.is.2025.102619

Matteo Zavatteri, Massimiliano de Leoni, Johann Eder, Manfred Reichert

引用次数: 0

Validating temporal compliance patterns: A unified approach with MTLf over various data models 验证时间遵从性模式：在各种数据模型上使用MTLf的统一方法

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-11 DOI: 10.1016/j.is.2025.102623

Nesma M. Zaki , Iman M.A. Helal , Ehab E. Hassanein , Ahmed Awad

Process mining extracts valuable insights from event data to help organizations improve their business processes, which is essential for their growth and success. By leveraging process mining techniques, organizations gain a comprehensive understanding of their processes’ execution, enabling the discovery of process models, detection of deviations, i.e., conformance checking, identification of bottlenecks, and assessment of performance. Compliance checking, a specific area within conformance checking, ensures that the organizational activities adhere to prescribed process models and regulations. Linear Temporal Logic over finite traces (

L T L_{f}

) is commonly used for conformance checking, but it may not capture all temporal aspects accurately. This paper proposes Metric Temporal Logic over finite traces (

M T L_{f}

) to define explicit time-related constraints effectively in addition to the implicit time-ordering covered by

L T L_{f}

. Therefore, it provides a universal formal approach to capture compliance rules. Moreover, we define a minimal set of generic

M T L_{f}

formulas and show that they are capable of capturing all the common patterns for compliance rules.

As compliance validation is largely driven by the data model used to represent the event logs, we provide a mapping from

M T L_{f}

to the common data models we found in the literature to encode event logs, namely, the relational and the graph models. A comprehensive study comparing various data models and an empirical evaluation across real-life event logs demonstrate the effectiveness of the proposed approach.

流程挖掘从事件数据中提取有价值的见解，以帮助组织改进其业务流程，这对其成长和成功至关重要。通过利用过程挖掘技术，组织获得了对其过程执行的全面理解，支持过程模型的发现、偏差的检测，即一致性检查、瓶颈的识别以及性能的评估。符合性检查是符合性检查中的一个特定领域，它确保组织活动遵守规定的过程模型和法规。有限轨迹上的线性时间逻辑（LTLf）通常用于一致性检查，但它可能无法准确捕获所有时间方面。本文提出了有限轨迹上的度量时间逻辑（MTLf）来有效地定义显式时间相关约束，以及ltf所涵盖的隐式时间排序。因此，它提供了一种通用的形式化方法来捕获遵从性规则。此外，我们定义了一组最小的通用MTLf公式，并展示了它们能够捕获遵从性规则的所有通用模式。由于遵从性验证在很大程度上是由用于表示事件日志的数据模型驱动的，因此我们提供了从MTLf到我们在文献中发现的用于编码事件日志的公共数据模型的映射，即关系模型和图模型。一项综合研究比较了各种数据模型，并对现实生活中的事件日志进行了实证评估，证明了所提出方法的有效性。

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引用次数: 0

Dynamic group recommender methodology: Leveraging temporal trust and confidence graphs 动态小组推荐方法：利用时间信任和信心图

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-03 DOI: 10.1016/j.is.2025.102612

Khadijeh Rahimkhani, Kamran Zamanifar

Group recommender systems aim to recommend items to groups with shared interests, aiming to satisfy each member. Managing trust and mutual influence within the group is a key challenge that influences the choice of items by users. These systems generate suggestions for the group based on inter-member trust. A less explored but critical aspect of this trust is its evolution, which can affect the group's item selections. This paper aims to assess the impact of time on trust in group recommendations. We begin by constructing a time-based confidence graph derived from the items selected by the group members. This graph allows us to measure the confidence levels between members and plays a crucial role in identifying their risk tolerance towards new items. Recognizing that members' risk-taking behavior can influence the group, we identify members who significantly affect group decisions. The confidence graph is periodically updated to reflect new user choices and the influence of key members. Ultimately, we introduce a novel method for calculating implicit trust based on similarity and confidence metrics, providing a recommendation list that maximizes group satisfaction based on the computed trust levels. Finally, the proposed method is evaluated using MovieLens100k, MovieLens10M, Epinions and Yelp datasets. The results demonstrate significant improvements in Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Precision, and group satisfaction measures compared to current state-of-the-art techniques.

群体推荐系统的目标是向有共同兴趣的群体推荐项目，以满足每个成员的需求。管理群组内的信任和相互影响是影响用户选择项目的关键挑战。这些系统基于成员间的信任为组生成建议。这种信任的一个较少探索但很重要的方面是它的演变，它会影响群体的项目选择。本文旨在评估时间对团队推荐信任的影响。我们首先从组成员选择的项目中构造一个基于时间的置信度图。这个图表使我们能够衡量成员之间的信心水平，并在确定他们对新项目的风险承受能力方面发挥关键作用。认识到成员的冒险行为可以影响群体，我们确定了显著影响群体决策的成员。置信度图定期更新，以反映新的用户选择和关键成员的影响。最后，我们引入了一种基于相似性和置信度度量来计算隐式信任的新方法，提供了一个基于计算的信任水平最大化群体满意度的推荐列表。最后，使用MovieLens100k、MovieLens10M、Epinions和Yelp数据集对所提方法进行了评估。结果表明，与当前最先进的技术相比，在平均绝对误差（MAE），均方根误差（RMSE），精度和群体满意度措施方面有显着改善。

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引用次数: 0

Compressed consecutive pattern matching 压缩连续模式匹配

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-08-28 DOI: 10.1016/j.is.2025.102607

Paweł Gawrychowski , Garance Gourdel , Tatiana Starikovskaya , Teresa Anna Steiner

Originating from the work of Navarro and Thankachan [TCS 2016], the problem of consecutive pattern matching is a variant of the fundamental pattern matching problem. In this problem, one is given a text and a pair of patterns

p_{1}, p_{2}

, and must compute consecutive occurrences of

p_{1}, p_{2}

in the text. Assuming that the text is given as a straight-line program of size

g

, we develop an algorithm that computes all consecutive occurrences of

p_{1}, p_{2}

in optimal

O (g + | p_{1} | + | p_{2} | + output)

time, where

output

is the size of the output. As a corollary, we also derive an algorithm that reports all co-occurrences separated by a distance

d \in [a, b]

O (g + | p_{1} | + | p_{2} | + output)

time and an algorithm that reports the top-

k

closest co-occurrences in

O (g + | p_{1} | + | p_{2} | + k)

time.

连续模式匹配问题起源于Navarro和Thankachan [TCS 2016]的工作，是基本模式匹配问题的一种变体。在这个问题中，给定一个文本和一对模式p1，p2，并且必须计算文本中p1，p2的连续出现次数。假设文本作为大小为g的直线程序给出，我们开发了一种算法，该算法计算最优O（g+|p1|+|p2|+输出）时间内所有连续出现的p1，p2，其中输出是输出的大小。作为推论，我们还推导出一种算法，该算法在O（g+|p1|+|p2|+输出）时间内报告距离d∈[a，b]的所有共现，以及一种算法，该算法在O（g+|p1|+|p2|+k）时间内报告最接近的前k个共现。

引用次数: 0

DaMoOp: A global approach for optimizing denormalized schemas through a multidimensional cost model DaMoOp：一种通过多维成本模型优化非规范化模式的全局方法

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-08-26 DOI: 10.1016/j.is.2025.102598

Jihane Mali , Shohreh Ahvar , Faten Atigui , Ahmed Azough , Nicolas Travers

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 DaMoOp, an automated approach for leading data model selection process. DaMoOp 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.

数据库系统的复杂性随着数据的指数级增长而增加，这就要求信息系统（IS）架构师不断改进数据模型，并精心选择符合需求的存储和管理选项。虽然现有的解决方案侧重于数据模型转换，但没有一个解决方案提供为给定用例选择最合适的数据模型的指导。在这种情况下，我们提出了DaMoOp，这是一种用于领先数据模型选择过程的自动化方法。DaMoOp从概念模型和包含查询、设置和基础设施约束的相关用例开始，以生成相关的逻辑数据模型。考虑环境、财务和时间因素的成本模型有助于比较和选择最合适的数据模型。我们的成本模型包含数据模型和查询成本。此外，我们建议采用一个数据模型选择流程，该流程可以增强为特定用例选择最佳数据模型的能力，同时还可以适应快速发展的用例。我们提供了一种战略优化方法，旨在随着用例场景的发展确定最具成本效益和最稳定的数据模型。此外，我们为整个过程提供了一个模拟工具，它可以可视化用例变化对数据模型成本的影响，从而使IS架构师能够做出明智的决策。

{"title":"DaMoOp: A global approach for optimizing denormalized schemas through a multidimensional cost model","authors":"Jihane Mali , Shohreh Ahvar , Faten Atigui , Ahmed Azough , 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":"2025-08-26","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}

引用次数: 0

A task taxonomy for conformance checking 用于一致性检查的任务分类法

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-08-23 DOI: 10.1016/j.is.2025.102605

Jana-Rebecca Rehse , Michael Grohs , Finn Klessascheck , Lisa-Marie Klein , Tatiana von Landesberger , Luise Pufahl

Conformance checking is a sub-discipline of process mining, which compares observed process traces with a process model to analyze whether the process execution conforms with or deviates from the process design. Organizations can leverage this analysis, for example to check whether their processes comply with internal or external regulations or to identify potential improvements. Gaining these insights requires suitable visualizations, which make complex results accessible and actionable. So far, however, the development of conformance checking visualizations has largely been left to tool vendors. As a result, current tools offer a wide variety of visual representations for conformance checking, but the analytical purposes they serve often remain unclear. However, without a systematic understanding of these purposes, it is difficult to evaluate the visualizations’ usefulness. Such an evaluation hence requires a deeper understanding of conformance checking as an analysis domain. To this end, we propose a task taxonomy, which categorizes the tasks that can occur when conducting conformance checking analyses. This taxonomy supports researchers in determining the purpose of visualizations, specifying relevant conformance checking tasks in terms of their goal, means, constraint type, data characteristics, data target, and data cardinality. Combining concepts from process mining and visual analytics, we address researchers from both disciplines to enable and support closer collaborations.

一致性检查是流程挖掘的一个子学科，它将观察到的流程跟踪与流程模型进行比较，以分析流程执行是否符合或偏离了流程设计。例如，组织可以利用这种分析来检查他们的过程是否符合内部或外部法规，或者识别潜在的改进。获得这些见解需要适当的可视化，这使得复杂的结果易于访问和操作。然而，到目前为止，一致性检查可视化的开发在很大程度上还是留给了工具供应商。因此，当前的工具为一致性检查提供了各种各样的可视化表示，但是它们所服务的分析目的往往仍然不清楚。然而，如果没有对这些目的的系统理解，就很难评估可视化的有用性。因此，这样的评估需要对作为分析领域的一致性检查有更深的理解。为此，我们提出了一个任务分类法，它对执行一致性检查分析时可能出现的任务进行分类。该分类法支持研究人员确定可视化的目的，根据目标、方法、约束类型、数据特征、数据目标和数据基数指定相关的一致性检查任务。结合过程挖掘和可视化分析的概念，我们解决了两个学科的研究人员，以实现和支持更紧密的合作。

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引用次数: 0

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-08-21 DOI: 10.1016/j.is.2025.102608

Francesca Zampino , Laura Genga , Antonella Longo

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.

尽管广泛的研究已经探索了业务流程模型的相似性，但这些研究的一致性和结构仍然没有得到充分的探索。本文系统地回顾了相关文献，特别关注行为相似性。我们对过程模型相似度的文献进行了系统的回顾，重点关注两种主要的测量方法：基于跟踪和基于模型的相似度。基于99项综述研究，我们建立了一个三维框架，并对选择的相似性度量进行了定量比较，以进行更深入的分析。我们的研究结果提供了结构化的见解，以加强过程模型相似性的评估，特别是从行为的角度。审查过程遵循六个阶段的系统方法，从确定相关关键词到制作目视表示调查结果的书目图。这些见解为该领域未来的研究和实际应用奠定了基础。

引用次数: 0

Adaptive Personalized Recommendation Systems: A systematic Review 自适应个性化推荐系统：系统综述

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-08-21 DOI: 10.1016/j.is.2025.102594

Bachir Asri, Sara Qassimi, Said Rakrak

Recommender systems assist users in navigating the vast selection of choices by offering personalized suggestions based on preferences. Originally used in e-commerce and streaming services, these systems are now applied in various sectors such as healthcare, education, and more, making them increasingly important. Despite their growth, recommender systems still face challenges, especially when addressing users whose preferences change over time.

This paper presents a review of recent research on recommender systems that deliver personalized and adaptive recommendations for users with evolving preferences. Analyzing 97 studies published between 2020 and 2024, the review categorizes them across multiple dimensions to address key research questions.

The findings reveal a diverse landscape of evaluation metrics, datasets, adaptation mechanisms, and application domains within adaptive personalized recommender systems (AdPRSs), with MovieLens as the most widely used dataset and the attention mechanism as the predominant adaptation approach. Furthermore, the review introduces a novel categorization of AdPRSs based on adaptation mechanism. By synthesizing current research, this review highlights key challenges faced in the field and identifies future directions for enhancing the efficiency and effectiveness of AdPRSs. These insights are of significant value to both practitioners and academic researchers, providing a foundation for advancing the development and optimization of AdPRSs.

推荐系统根据用户的喜好提供个性化的建议，帮助用户在大量的选择中导航。这些系统最初用于电子商务和流媒体服务，现在应用于医疗保健、教育等各个领域，使它们变得越来越重要。尽管不断增长，但推荐系统仍然面临挑战，尤其是在解决用户偏好随时间变化的问题时。本文介绍了最近关于推荐系统的研究综述，该系统为用户提供个性化和自适应的推荐。该综述分析了2020年至2024年间发表的97项研究，对它们进行了多维度分类，以解决关键的研究问题。研究结果揭示了自适应个性化推荐系统（adprs）中评估指标、数据集、适应机制和应用领域的多样化格局，其中MovieLens是使用最广泛的数据集，而注意力机制是主要的适应方法。此外，本文还介绍了一种基于适应机制的adprs分类方法。通过综合目前的研究，本文强调了该领域面临的主要挑战，并确定了提高adprs效率和有效性的未来方向。这些见解对实践者和学术研究者都具有重要的价值，为推进adprs的发展和优化提供了基础。

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