Information Systems最新文献_第9页

Big data analytics deep learning techniques and applications: A survey 大数据分析深度学习技术与应用:调查

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-11-21 DOI: 10.1016/j.is.2023.102318

Hend A. Selmy , Hoda K. Mohamed , Walaa Medhat

{"title":"Big data analytics deep learning techniques and applications: A survey","authors":"Hend A. Selmy , Hoda K. Mohamed , Walaa Medhat","doi":"10.1016/j.is.2023.102318","DOIUrl":"https://doi.org/10.1016/j.is.2023.102318","url":null,"abstract":"<div><p>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 analyze and learn from enormous amounts of unstructured data. This survey paper tackles a comprehensive overview of state-of-the-art DL techniques applied in BDA. The main target of this survey is intended to illustrate the significance of DL and its taxonomy and detail the basic techniques used in BDA. It also explains the DL techniques used in big IoT data applications as well as their various complexities and challenges. The survey presents various real-world data-intensive applications where DL techniques can be applied. In particular, it concentrates on the DL techniques in accordance with the BDA type for each application domain. Additionally, the survey examines DL benchmarked frameworks used in BDA and reviews the available benchmarked datasets, besides analyzing the strengths and limitations of each DL technique and their suitable applications. Further, a comparative analysis is also presented by comparing existing approaches to the DL methods used in BDA. Finally, the challenges of DL modeling and future directions are discussed.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138436402","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

Document structure-driven investigative information retrieval 文档结构驱动的调查信息检索

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-11-19 DOI: 10.1016/j.is.2023.102315

Tuomas Ketola, Thomas Roelleke

{"title":"Document structure-driven investigative information retrieval","authors":"Tuomas Ketola, Thomas Roelleke","doi":"10.1016/j.is.2023.102315","DOIUrl":"https://doi.org/10.1016/j.is.2023.102315","url":null,"abstract":"<div><p>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 a methodology for document structure-driven investigative information retrieval (InvIR) InvIR is defined as a subtask of exploratory IR, where transparency and reasoning take centre stage The aim of InvIR is to facilitate the verification and discovery of facts from data and the communication of those facts to others From a technical perspective, the methodology applies recent work from structured document retrieval (SDR) concerned with formal retrieval constraints and information content-based field weighting (ICFW) Using ICFW, the paper establishes the concept of relevance structures to describe the document structure-based relevance of documents These contexts are then used to help the user navigate during their discovery process and to rank entities of interest The proposed methodology is evaluated using a prototype search system called Relevance Structure-based Entity Ranker (RSER) in order to demonstrate its the feasibility This methodology represents an interesting and important research direction in a world where transparency is becoming more vital than ever.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0306437923001515/pdfft?md5=934dc470062407433a9cf64fc9053b41&pid=1-s2.0-S0306437923001515-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138454298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CoCo-trie: Data-aware compression and indexing of strings CoCo-trie:字符串的数据感知压缩和索引

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-11-17 DOI: 10.1016/j.is.2023.102316

Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra

{"title":"CoCo-trie: Data-aware compression and indexing of strings","authors":"Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra","doi":"10.1016/j.is.2023.102316","DOIUrl":"https://doi.org/10.1016/j.is.2023.102316","url":null,"abstract":"<div><p>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.</p><p>We introduce a new data structure, called the COmpressed COllapsed Trie (CoCo-trie), that hinges on a pool of techniques to compress subtries (of arbitrary depth) into succinctly-encoded and efficiently-searchable trie macro-nodes with a possibly large fan-out. Then, we observe that the choice of the subtries to compress depends on the trie structure and its edge labels. Hence, we develop a data-aware optimisation approach that selects the best subtries to compress via the above pool of succinct encodings, with the overall goal of minimising the total space occupancy and still achieving efficient query time. We also investigate some variants of this approach that induce interesting space–time trade-offs in the CoCo-trie design.</p><p>Our experimental evaluation on six diverse and large datasets (representing URLs, XML data, DNA and protein sequences, database records, and search-engine dictionaries) shows that the space–time performance of well-established and highly-engineered data structures solving this problem is very input-sensitive. Conversely, our CoCo-trie provides a robust and uniform improvement over all competitors for half of the datasets, and it results on the Pareto space–time frontier for the others, thus offering new competitive trade-offs.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0306437923001527/pdfft?md5=ed299eb2c8fd012181bae65f8d22c88e&pid=1-s2.0-S0306437923001527-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138413518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel diversified echo state network for improved accuracy and explainability of EEG-based stroke prediction 新颖的多元回波状态网络提高了基于脑电图的脑卒中预测的准确性和可解释性

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-11-15 DOI: 10.1016/j.is.2023.102317

Samar Bouazizi , Hela Ltifi

{"title":"Novel diversified echo state network for improved accuracy and explainability of EEG-based stroke prediction","authors":"Samar Bouazizi , Hela Ltifi","doi":"10.1016/j.is.2023.102317","DOIUrl":"https://doi.org/10.1016/j.is.2023.102317","url":null,"abstract":"<div><p><span><span>Echo State Networks (ESNs) are a powerful </span>machine learning technique<span> 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, ensemble learning, and output refinement for improved </span></span>interpretability. The optimized feature extraction component uses a novel algorithm to extract features from EEG data that are more relevant to stroke prediction. The ensemble learning component uses a diversified Echo State Networks (D-ESN) to combine the predictions of multiple ESNs, which improves the accuracy of the predictions. The output improvement component uses two Explainability techniques, LIME and ELI5, to gain insight into the decision-making of the D-ESN model. These techniques allow users to see how each feature in the dataset contributed to the model's prediction. The framework was evaluated on a well-known EEG dataset from stroke patients. The experimental results showed that the framework significantly outperformed baseline approaches in terms of both accuracy with 95 % and interpretability. These results suggest that the proposed framework has the potential to advance the field of stroke prediction and enable informed decision-making in clinical settings.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138430498","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

Reproducible experiments for generating pre-processing pipelines for AutoETL 生成AutoETL预处理管道的可重复实验

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-11-02 DOI: 10.1016/j.is.2023.102314

Joseph Giovanelli , Besim Bilalli , Alberto Abelló , Fernando Silva-Coira , Guillermo de Bernardo

{"title":"Reproducible experiments for generating pre-processing pipelines for AutoETL","authors":"Joseph Giovanelli , Besim Bilalli , Alberto Abelló , Fernando Silva-Coira , Guillermo de Bernardo","doi":"10.1016/j.is.2023.102314","DOIUrl":"https://doi.org/10.1016/j.is.2023.102314","url":null,"abstract":"<div><p>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 model performance, data pre-processing is receiving a lot of attention. Yet, its impact over the final analysis is not widely recognized, primarily due to the lack of publicly available experiments that quantify it. To bridge this gap, this work introduces a set of reproducible experiments on the impact of data pre-processing by providing a detailed reproducibility protocol together with a software tool and a set of extensible datasets, which allow for all the experiments and results of our aforementioned work to be reproduced. We introduce a set of strongly reproducible experiments based on a collection of intermediate results, and a set of weakly reproducible experiments (Lastra-Dıaz, 0000) that allows reproducing our end-to-end optimization process and evaluation of all the methods reported in our primary paper. The reproducibility protocol is created in Docker and tested in Windows and Linux. In brief, our primary work (i) develops a method for generating effective prototypes, as templates or logical sequences of pre-processing transformations, and (ii) instantiates the prototypes into pipelines, in the form of executable or physical sequences of actual operators that implement the respective transformations. For the first, a set of heuristic rules learned from extensive experiments are used, and for the second techniques from Automated Machine Learning (AutoML) are applied.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92046422","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

Measuring rule-based LTLf process specifications: A probabilistic data-driven approach 度量基于规则的LTLf流程规范:一种概率数据驱动的方法

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-11-02 DOI: 10.1016/j.is.2023.102312

Alessio Cecconi , Luca Barbaro , Claudio Di Ciccio , Arik Senderovich

引用次数: 0

A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study 基于价值共创视角的混合智能系统数据标注:设计研究

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-10-30 DOI: 10.1016/j.is.2023.102311

Mahei Manhai Li , Philipp Reinhard , Christoph Peters , Sarah Oeste-Reiss , Jan Marco Leimeister

{"title":"A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study","authors":"Mahei Manhai Li , Philipp Reinhard , Christoph Peters , Sarah Oeste-Reiss , Jan Marco Leimeister","doi":"10.1016/j.is.2023.102311","DOIUrl":"10.1016/j.is.2023.102311","url":null,"abstract":"<div><p>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 labeled data leads to diminishing returns in AI performance. Following a design science research approach, we provide a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures. We develop a functional prototype based on 17,120 support tickets provided by a pilot partner as an instantiation and evaluate the design through accuracy metrics and user evaluations. Our evaluation revealed that recommendations after label improvement showed increased user ratings, and users are willing to contribute their domain knowledge. We demonstrate that our design benefits for both human agent and AI systems in the form of hybrid intelligence service systems. Overall, our results emphasize agents' need for value-in-use by providing better results if they improve the labeling of support tickets pre-labeled by AI. Thus, we provide prescriptive knowledge of a novel HIL design that enables efficient and interactive labeling in the context of diverse applications of reinforcement learning systems.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0306437923001473/pdfft?md5=0f8a4c5d8fbd5b5164495b8b67e52fe6&pid=1-s2.0-S0306437923001473-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136127713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adoption of IT solutions: A data-driven analysis approach 采用IT解决方案:数据驱动的分析方法

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-10-24 DOI: 10.1016/j.is.2023.102313

Iris Reinhartz-Berger , Alan Hartman , Doron Kliger

{"title":"Adoption of IT solutions: A data-driven analysis approach","authors":"Iris Reinhartz-Berger , Alan Hartman , Doron Kliger","doi":"10.1016/j.is.2023.102313","DOIUrl":"https://doi.org/10.1016/j.is.2023.102313","url":null,"abstract":"<div><p>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 are high. The method is data-driven and considers the preferences of the business units, as service providers, customers or owners. Under the assumption that the generation of value to all stakeholders will increase adoption, the approach encompasses the variation in business unit characteristics, as expressed by feature preferences and performance indicators, as well as the value gained from adopting the solutions by the business units in their different roles with respect to a given service. Besides providing a systematic tool for analyzing IT alternatives, the method supports the identification of design gaps and obstacles for adoption. These gaps and obstacles can be addressed by service design techniques and behavioral economics practices depending on the organizational leadership style (autocracy or democracy). To shed light on the feasibility of the approach, we report on the design and results of a case study of a vehicle fleet management service in a real setting.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92115808","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

An analysis of ensemble pruning methods under the explanation of Random Forest 随机森林解释下的集合剪枝方法分析

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-10-19 DOI: 10.1016/j.is.2023.102310

Faten A. Khalifa, Hatem M. Abdelkader, Asmaa H. Elsaid

{"title":"An analysis of ensemble pruning methods under the explanation of Random Forest","authors":"Faten A. Khalifa, Hatem M. Abdelkader, Asmaa H. Elsaid","doi":"10.1016/j.is.2023.102310","DOIUrl":"https://doi.org/10.1016/j.is.2023.102310","url":null,"abstract":"<div><p><span><span><span><span>“Black box” models created by modern machine learning techniques are typically hard to interpret. Thus, the necessity of </span>explainable artificial intelligence (XAI) has grown for understanding the rationale behind those models and converting them into white boxes. </span>Random Forest is a black box model essential in various domains due to its flexibility, ease of use, and remarkable </span>predictive performance<span>. One method for explaining a Random Forest is transforming it into a self-explainable Decision Tree using Forest-Based Tree (FBT) algorithm. It basically consists of three main phases, pruning, conjunction set generation, and Decision Tree construction. In this paper, we examine six state-of-the-art pruning approaches and analyze their effect on FBT performance through pruned FBT (PFBT) in order to minimize its </span></span>computational complexity. This would make it appropriate for forests and datasets of any size. They are assessed on 30 datasets, and the results show that UMEP and Hybrid pruning methods can be effectively used in the pruning stage of the PFBT algorithm in terms of pruning time and predictive performance. However, the AUC-Greedy method achieves good performance with small-size datasets.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92033629","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

ECG classification with learning ensemble based on symbolic discretization 基于符号离散化的学习集成心电分类

IF 3.7 2区计算机科学

Information Systems Pub Date : 2023-10-18 DOI: 10.1016/j.is.2023.102294

Mariem Taktak, Hela Ltifi, Mounir Ben Ayed

引用次数: 0