標題: Titlebook: Big Data Analytics and Knowledge Discovery; 25th International C Robert Wrembel,Johann Gamper,Ismail Khalil Conference proceedings 2023 The [打印本頁] 作者: 出租車 時間: 2025-3-21 17:20
書目名稱Big Data Analytics and Knowledge Discovery影響因子(影響力)
書目名稱Big Data Analytics and Knowledge Discovery影響因子(影響力)學科排名
書目名稱Big Data Analytics and Knowledge Discovery網(wǎng)絡(luò)公開度
書目名稱Big Data Analytics and Knowledge Discovery網(wǎng)絡(luò)公開度學科排名
書目名稱Big Data Analytics and Knowledge Discovery被引頻次
書目名稱Big Data Analytics and Knowledge Discovery被引頻次學科排名
書目名稱Big Data Analytics and Knowledge Discovery年度引用
書目名稱Big Data Analytics and Knowledge Discovery年度引用學科排名
書目名稱Big Data Analytics and Knowledge Discovery讀者反饋
書目名稱Big Data Analytics and Knowledge Discovery讀者反饋學科排名
作者: 漂白 時間: 2025-3-21 21:04
https://doi.org/10.1007/978-1-4842-6197-2luate the proposed method on synthetic and real-world datasets. While delivering comparable anomaly detection performance as the state-of-the-art approaches, STAD works more efficiently and provides extra interpretability.作者: obstruct 時間: 2025-3-22 00:26 作者: 正式通知 時間: 2025-3-22 08:12
Bapi Chakraborty,Yashajeet Chowdhury for the user. Two algorithms are proposed to mine these patterns efficiently called HUGI (High Utility Gradual Itemsets mining), and HUGI-Merging, which extracts these patterns from both a negative and positive quantitative data separately and merges the obtained results. Experimental results show 作者: 情愛 時間: 2025-3-22 09:44 作者: 廢墟 時間: 2025-3-22 16:22
https://doi.org/10.1007/978-1-4842-6998-5ndividual or group level. We conduct quantitative experiments and sensitivity studies on the real-world clinical PBC dataset. The results demonstrate that the proposed fairness notations and debiasing algorithm are capable of guaranteeing fairness in the presence of accurate prediction.作者: nitroglycerin 時間: 2025-3-22 18:21
Introducing Ethereum and SolidityWe conduct exploratory analyses to understand our dataset’s characteristics and analyze useful patterns. We also experiment various state-of-the-art rumor classification methods to illustrate DAT@Z21’s usefulness, especially its visual components. Eventually, DAT@Z21 is available online at ..作者: surrogate 時間: 2025-3-22 21:23
EXOS: Explaining Outliers in Data Streamsorrelation within a data stream and across data streams to estimate the local context. The experiments using three real and two synthetic datasets show that, on average, EXOS achieves up to 49% higher F1 score and 29.6 times lower explanation time than existing algorithms.作者: lymphedema 時間: 2025-3-23 05:27 作者: 獎牌 時間: 2025-3-23 09:25
Anomaly Detection in?Financial Transactions Via?Graph-Based Feature Aggregationsgate strategy to accurately preserve anomaly information, thereby alleviating the over-smoothing issue incurred by proximal feature aggregation. Our experiments comparing . against 10 baselines on real transaction datasets from PayPal demonstrate that . consistently outperforms all baselines in term作者: 贊美者 時間: 2025-3-23 10:11 作者: Externalize 時間: 2025-3-23 17:07 作者: DEFT 時間: 2025-3-23 18:36
Fair-DSP: Fair Dynamic Survival Prediction on?Longitudinal Electronic Health Recordndividual or group level. We conduct quantitative experiments and sensitivity studies on the real-world clinical PBC dataset. The results demonstrate that the proposed fairness notations and debiasing algorithm are capable of guaranteeing fairness in the presence of accurate prediction.作者: 是他笨 時間: 2025-3-24 01:55
DAT@Z21: A Comprehensive Multimodal Dataset for?Rumor Classification in?MicroblogsWe conduct exploratory analyses to understand our dataset’s characteristics and analyze useful patterns. We also experiment various state-of-the-art rumor classification methods to illustrate DAT@Z21’s usefulness, especially its visual components. Eventually, DAT@Z21 is available online at ..作者: Presbycusis 時間: 2025-3-24 04:15
0302-9743 took place in Penang, Malaysia, during August 29-30, 2023. ..The 18 full papers presented together with 19 short papers were carefully reviewed and selected from a total of 83 submissions..They were organized in topical sections as follows: Data quality; advanced analytics and pattern discovery; ma作者: Hallmark 時間: 2025-3-24 09:54 作者: mutineer 時間: 2025-3-24 14:00
https://doi.org/10.1007/978-1-4842-5013-6ns on type definitions to transform technical errors into type errors. We show how to use this framework to prevent errors related to schema or model transformations in analytical workflows. We provide an open-source implementation in Scala which can be used to detect errors at compile time.作者: bizarre 時間: 2025-3-24 16:50 作者: 亞麻制品 時間: 2025-3-24 21:45
Using Ontologies as?Context for?Data Warehouse Quality Assessmentality in Data Warehouse considering data context. In addition to presenting our general approach, in this paper we propose two particular data quality rules for accuracy dimension, using OWL domain ontologies as context. With our approach, we obtain data quality metrics that can be adapted to any application domain.作者: Excise 時間: 2025-3-24 23:50
Preventing Technical Errors in?Data Lake Analyses with?Type Theoryns on type definitions to transform technical errors into type errors. We show how to use this framework to prevent errors related to schema or model transformations in analytical workflows. We provide an open-source implementation in Scala which can be used to detect errors at compile time.作者: 晚來的提名 時間: 2025-3-25 04:39 作者: Vulnerable 時間: 2025-3-25 07:32
https://doi.org/10.1007/978-1-4842-5917-7s on the existence of differences between two datasets as contrast ItemSB. We further report the results of evaluation experiments conducted on the properties of ItemSB from the perspective of reproducibility and reliability using contrast ItemSB.作者: 涂掉 時間: 2025-3-25 13:57
Discovery of?Contrast Itemset with?Statistical Background Between Two Continuous Variabless on the existence of differences between two datasets as contrast ItemSB. We further report the results of evaluation experiments conducted on the properties of ItemSB from the perspective of reproducibility and reliability using contrast ItemSB.作者: commune 時間: 2025-3-25 18:34 作者: insincerity 時間: 2025-3-25 21:28
Decision Diagram-Based Simulationriately. Experimental results show that by tuning the parameters of the proposed method appropriately, highly accurate results can be obtained even for large hypergraphs for machine learning tasks such as node label classification.作者: Aviary 時間: 2025-3-26 02:20 作者: fatuity 時間: 2025-3-26 04:34 作者: demote 時間: 2025-3-26 08:52 作者: 桉樹 時間: 2025-3-26 16:14
Contextual Shift Method (CSM)icial data point. The problem of not generating contextual shifts is true for the quantile shift method. We propose the Contextual Shift Method (CSM), which improves the quantile shift method by generating contextual shifts. We show that the CSM reduces the amount of data points created in low data density areas.作者: staging 時間: 2025-3-26 20:27
Hypergraph Embedding Based on?Random Walk with?Adjusted Transition Probabilitiesriately. Experimental results show that by tuning the parameters of the proposed method appropriately, highly accurate results can be obtained even for large hypergraphs for machine learning tasks such as node label classification.作者: 泥沼 時間: 2025-3-26 21:55 作者: 咽下 時間: 2025-3-27 04:20
Using Ontologies as?Context for?Data Warehouse Quality Assessmentty is context-dependent and this fact should be considered in its management. This work is a step forward to a general mechanism for assessing Data Quality in Data Warehouse considering data context. In addition to presenting our general approach, in this paper we propose two particular data quality作者: 不妥協(xié) 時間: 2025-3-27 05:21
Preventing Technical Errors in?Data Lake Analyses with?Type Theoryicle, we present a formal framework based on type theory to prevent technical errors in such compositions of operators. This framework uses restrictions on type definitions to transform technical errors into type errors. We show how to use this framework to prevent errors related to schema or model 作者: 最后一個 時間: 2025-3-27 09:37 作者: demote 時間: 2025-3-27 15:51 作者: 共棲 時間: 2025-3-27 19:35 作者: groggy 時間: 2025-3-28 01:09
Anomaly Detection in?Financial Transactions Via?Graph-Based Feature Aggregationsnancial institutions. Existing solutions utilize solely transaction attributes as feature representations without the consideration of direct/indirect interactions between users and transactions, leading to limited accuracy. We formulate anomaly detection in financial transactions as the problem of 作者: 博識 時間: 2025-3-28 03:31 作者: SENT 時間: 2025-3-28 07:28
Hypergraph Embedding Based on?Random Walk with?Adjusted Transition Probabilitiesnce into a skip-gram used in natural language processing, a vector representation that captures the graph structure can be obtained. We propose a random walk method with adjustable transition probabilities for hypergraphs. As a result, we argue that it is possible to embed graph features more approp作者: PALL 時間: 2025-3-28 14:15 作者: arcane 時間: 2025-3-28 17:24 作者: endure 時間: 2025-3-28 20:15 作者: 否認 時間: 2025-3-28 23:56 作者: mediocrity 時間: 2025-3-29 04:41 作者: Extort 時間: 2025-3-29 09:29 作者: 撤退 時間: 2025-3-29 13:01 作者: MOT 時間: 2025-3-29 19:04 作者: elastic 時間: 2025-3-29 20:28
https://doi.org/10.1007/978-3-031-39831-5big data; knowledge discovery; query languages; artificial intelligence; machine learning; warehousing; di作者: circumvent 時間: 2025-3-30 00:42 作者: 增強 時間: 2025-3-30 05:58 作者: 使虛弱 時間: 2025-3-30 08:25
Calculating Values Using Operations,ty is context-dependent and this fact should be considered in its management. This work is a step forward to a general mechanism for assessing Data Quality in Data Warehouse considering data context. In addition to presenting our general approach, in this paper we propose two particular data quality作者: Alcove 時間: 2025-3-30 13:45
https://doi.org/10.1007/978-1-4842-5013-6icle, we present a formal framework based on type theory to prevent technical errors in such compositions of operators. This framework uses restrictions on type definitions to transform technical errors into type errors. We show how to use this framework to prevent errors related to schema or model 作者: cunning 時間: 2025-3-30 18:07 作者: Aerophagia 時間: 2025-3-30 21:21
https://doi.org/10.1007/978-1-4842-6197-2he literature. MotifAug leverages the warping path constructed by MotifDTW, a novel alignment method that uses the Matrix Profile (MP) motif discovery mechanism and Dynamic Time Warping (DTW) to align two time series data instances.作者: Charlatan 時間: 2025-3-31 02:40 作者: exclusice 時間: 2025-3-31 08:38
https://doi.org/10.1007/978-3-030-41753-6nancial institutions. Existing solutions utilize solely transaction attributes as feature representations without the consideration of direct/indirect interactions between users and transactions, leading to limited accuracy. We formulate anomaly detection in financial transactions as the problem of 作者: PANIC 時間: 2025-3-31 11:40 作者: CHYME 時間: 2025-3-31 13:37 作者: 輕快來事 時間: 2025-3-31 21:25
