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Efficient Algorithms for Dynamic Incomplete Decision Systems 动态不完全决策系统的高效算法
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2021-01-01 DOI: 10.4018/IJDWM.2021070103
N. Thang, Long Giang Nguyen, Hoang Viet Long, N. Tuan, T. Tuan, Ngo Duy Tan
{"title":"Efficient Algorithms for Dynamic Incomplete Decision Systems","authors":"N. Thang, Long Giang Nguyen, Hoang Viet Long, N. Tuan, T. Tuan, Ngo Duy Tan","doi":"10.4018/IJDWM.2021070103","DOIUrl":"https://doi.org/10.4018/IJDWM.2021070103","url":null,"abstract":"Attribute reduction is a crucial problem in the process of data mining and knowledge discovery in big data. In incomplete decision systems, the model using tolerance rough set is fundamental to solve the problem by computing the redact to reduce the execution time. However, these proposals used the traditional filter approach so that the reduct was not optimal in the number of attributes and the accuracy of classification. The problem is critical in the dynamic incomplete decision systems which are more appropriate for real-world applications. Therefore, this paper proposes two novel incremental algorithms using the combination of filter and wrapper approach, namely IFWA_ADO and IFWA_DEO, respectively, for the dynamic incomplete decision systems. The IFWA_ADO computes reduct incrementally in cases of adding multiple objects while IFWA_DEO updates reduct when removing multiple objects. These algorithms are also verified on six data sets. Experimental results show that the filter-wrapper algorithms get higher performance than the other filter incremental algorithms.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"85 1","pages":"44-67"},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76236229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 2
A Temporal Multidimensional Model and OLAP Operators 时间多维模型和OLAP操作符
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI: 10.4018/ijdwm.2020100107
Waqas Ahmed, E. Zimányi, A. Vaisman, R. Wrembel
{"title":"A Temporal Multidimensional Model and OLAP Operators","authors":"Waqas Ahmed, E. Zimányi, A. Vaisman, R. Wrembel","doi":"10.4018/ijdwm.2020100107","DOIUrl":"https://doi.org/10.4018/ijdwm.2020100107","url":null,"abstract":"Usually, data in data warehouses (DWs) are stored using the notion of the multidimensional (MD) model. Often, DWs change in content and structure due to several reasons, like, for instance, changes in a business scenario or technology. For accurate decision-making, a DW model must allow storing and analyzing time-varying data. This paper addresses the problem of keeping track of the history of the data in a DW. For this, first, a formalization of the traditional MD model is proposed and then extended as a generalized temporal MD model. The model comes equipped with a collection of typical online analytical processing (OLAP) operations with temporal semantics, which is formalized for the four classic operations, namely roll-up, dice, project, and drill-across. Finally, the mapping from the generalized temporal model into a relational schema is presented together with an implementation of the temporal OLAP operations in standard SQL.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"112 1","pages":"112-143"},"PeriodicalIF":1.2,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75657896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 3
Recommender Systems Based on Resonance Relationship of Criteria With Choquet Operation 基于标准共振关系和Choquet操作的推荐系统
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI: 10.4018/ijdwm.2020100103
H. Huynh, Le Hoang Son, Cu Nguyen Giap, T. Huynh, H. H. Luong
{"title":"Recommender Systems Based on Resonance Relationship of Criteria With Choquet Operation","authors":"H. Huynh, Le Hoang Son, Cu Nguyen Giap, T. Huynh, H. H. Luong","doi":"10.4018/ijdwm.2020100103","DOIUrl":"https://doi.org/10.4018/ijdwm.2020100103","url":null,"abstract":"Recommender systems are becoming increasingly important in every aspect of life for the diverse needs of users. One of the main goals of the recommender system is to make decisions based on criteria. It is thus important to have a reasonable solution that is consistent with user requirements and characteristics of the stored data. This paper proposes a novel recommendation method based on the resonance relationship of user criteria with Choquet Operation for building a decision-making model. It has been evaluated on the multirecsys tool based on R language. Outputs from the proposed model are effective and reliable through the experiments. It can be applied in appropriate contexts to improve efficiency and minimize the limitations of the current recommender systems.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"237 1 1","pages":"44-62"},"PeriodicalIF":1.2,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72946115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1
The Model-Driven Architecture for the Trajectory Data Warehouse Modeling 轨迹数据仓库建模的模型驱动体系结构
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI: 10.4018/ijdwm.2020100102
Noura Azaiez, J. Akaichi
{"title":"The Model-Driven Architecture for the Trajectory Data Warehouse Modeling","authors":"Noura Azaiez, J. Akaichi","doi":"10.4018/ijdwm.2020100102","DOIUrl":"https://doi.org/10.4018/ijdwm.2020100102","url":null,"abstract":"Business Intelligence includes the concept of data warehousing to support decision making. As the ETL process presents the core of the warehousing technology, it is responsible for pulling data out of the source systems and placing it into a data warehouse. Given the technology development in the field of geographical information systems, pervasive systems, and the positioning systems, the traditional warehouse features become unable to handle the mobility aspect integrated in the warehousing chain. Therefore, the trajectory or the mobility data gathered from the mobile object movements have to be managed through what is called the trajectory ELT. For this purpose, the authors emphasize the power of the model-driven architecture approach to achieve the whole transformation task, in this case transforming trajectory data source model that describes the resulting trajectories into trajectory data mart models. The authors illustrate the proposed approach with an epilepsy patient state case study.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"57 1","pages":"26-43"},"PeriodicalIF":1.2,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74381297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1
Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis 发现异质特征之间的相似性:临床基因组分析的案例研究
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI: 10.4018/ijdwm.2020100104
V. Janeja, J. Namayanja, Y. Yesha, A. Kench, V. Misal
{"title":"Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis","authors":"V. Janeja, J. Namayanja, Y. Yesha, A. Kench, V. Misal","doi":"10.4018/ijdwm.2020100104","DOIUrl":"https://doi.org/10.4018/ijdwm.2020100104","url":null,"abstract":"The analysis of both continuous and categorical attributes generating a heterogeneous mix of attributes poses challenges in data clustering. Traditional clustering techniques like k-means clustering work well when applied to small homogeneous datasets. However, as the data size becomes large, it becomes increasingly difficult to find meaningful and well-formed clusters. In this paper, the authors propose an approach that utilizes a combined similarity function, which looks at similarity across numeric and categorical features and employs this function in a clustering algorithm to identify similarity between data objects. The findings indicate that the proposed approach handles heterogeneous data better by forming well-separated clusters.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"420 1","pages":"63-83"},"PeriodicalIF":1.2,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84918911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 2
Enhancing the Diamond Document Warehouse Model 改进钻石文档仓库模型
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI: 10.4018/ijdwm.2020100101
M. Azabou, Ameen Banjar, J. Feki
{"title":"Enhancing the Diamond Document Warehouse Model","authors":"M. Azabou, Ameen Banjar, J. Feki","doi":"10.4018/ijdwm.2020100101","DOIUrl":"https://doi.org/10.4018/ijdwm.2020100101","url":null,"abstract":"The data warehouse community has paid particular attention to the document warehouse (DocW) paradigm during the last two decades. However, some important issues related to the semantics are still pending and therefore need a deep research investigation. Indeed, the semantic exploitation of the DocW is not yet mature despite it representing a main concern for decision-makers. This paper aims to enhancing the multidimensional model called Diamond Document Warehouse Model with semantics aspects; in particular, it suggests semantic OLAP (on-line analytical processing) operators for querying the DocW.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"61 1","pages":"1-25"},"PeriodicalIF":1.2,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85634852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Improvement of K-Medoids Clustering Algorithm Based on Fixed Point Iteration 基于不动点迭代的K-Medoids聚类算法改进
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI: 10.4018/ijdwm.2020100105
Xiaodi Huang, Minglun Ren, Zhongfeng Hu
{"title":"An Improvement of K-Medoids Clustering Algorithm Based on Fixed Point Iteration","authors":"Xiaodi Huang, Minglun Ren, Zhongfeng Hu","doi":"10.4018/ijdwm.2020100105","DOIUrl":"https://doi.org/10.4018/ijdwm.2020100105","url":null,"abstract":"The process of K-medoids algorithm is that it first selects data randomly as initial centers to form initial clusters. Then, based on PAM (partitioning around medoids) algorithm, centers will be sequential replaced by all the remaining data to find a result has the best inherent convergence. Since PAM algorithm is an iterative ergodic strategy, when the data size or the number of clusters are huge, its expensive computational overhead will hinder its feasibility. The authors use the fixed-point iteration to search the optimal clustering centers and build a FPK-medoids (fixed point-based K-medoids) algorithm. By constructing fixed point equations for each cluster, the problem of searching optimal centers is converted into the solving of equation set in parallel. The experiment is carried on six standard datasets, and the result shows that the clustering efficiency of proposed algorithm is significantly improved compared with the conventional algorithm. In addition, the clustering quality will be markedly enhanced in handling problems with large-scale datasets or a large number of clusters.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"195 1","pages":"84-94"},"PeriodicalIF":1.2,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85850552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 3
Data Discovery Over Time Series From Star Schemas Based on Association, Correlation, and Causality 基于关联、相关性和因果关系的星型模式的时间序列数据发现
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI: 10.4018/ijdwm.2020100106
Wallace A. Pinheiro, G. Xexéo, J. Souza, A. B. Pinheiro
{"title":"Data Discovery Over Time Series From Star Schemas Based on Association, Correlation, and Causality","authors":"Wallace A. Pinheiro, G. Xexéo, J. Souza, A. B. Pinheiro","doi":"10.4018/ijdwm.2020100106","DOIUrl":"https://doi.org/10.4018/ijdwm.2020100106","url":null,"abstract":"This work proposes a methodology applied to repositories modeled using star schemas, such as data marts, to discover relevant time series relations. This paper applies a set of measures related to association, correlation, and causality to create connections among data. In this context, the research proposes a new causality function based on peaks and values that relate coherently time series. To evaluate the approach, the authors use a set of experiments exploring time series about a particular neglected disease that affects several Brazilian cities called American Tegumentary Leishmaniasis and time series about the climate of some cities in Brazil. The authors populate data marts with these data, and the proposed methodology has generated a set of relations linking the notifications of this disease to the variation of temperature and pluviometry.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"63 1","pages":"95-111"},"PeriodicalIF":1.2,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83654658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Extending LINE for Network Embedding With Completely Imbalanced Labels 标签完全不平衡的网络嵌入扩展LINE
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070102
Zheng Wang, Qiao Wang, Tanjie Zhu, Xiaojun Ye
{"title":"Extending LINE for Network Embedding With Completely Imbalanced Labels","authors":"Zheng Wang, Qiao Wang, Tanjie Zhu, Xiaojun Ye","doi":"10.4018/ijdwm.2020070102","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070102","url":null,"abstract":"Network embedding is a fundamental problem in network research. Semi-supervised network embedding, which benefits from labeled data, has recently attracted considerable interest. However, existing semi-supervised methods would get biased results in the completely-imbalanced label setting where labeled data cannot cover all classes. This article proposes a novel network embedding method which could benefit from completely-imbalanced labels by approximately guaranteeing both intra-class similarity and inter-class dissimilarity. In addition, the authors prove and adopt the matrix factorization form of LINE (a famous network embedding method) as the network structure preserving model. Extensive experiments demonstrate the superiority and robustness of this method.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"81 1","pages":"20-36"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83912304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1
Discovering Specific Sales Patterns Among Different Market Segments 发现不同细分市场的特定销售模式
IF 1.2 4区 计算机科学
International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070103
Cheng-Hsiung Weng, Cheng-Kui Huang
{"title":"Discovering Specific Sales Patterns Among Different Market Segments","authors":"Cheng-Hsiung Weng, Cheng-Kui Huang","doi":"10.4018/ijdwm.2020070103","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070103","url":null,"abstract":"Formulating different marketing strategies to apply to various market segments is a noteworthy undertaking for marketing managers. Accordingly, marketing managers should identify sales patterns among different market segments. The study initially applies the concept of recency–frequency–monetary (RFM) scores to segment transaction datasets into several sub-datasets (market segments) and discovers RFM itemsets from these market segments. In addition, three sales features (unique, common, and particular sales patterns) are defined to identify various sales patterns in this study. In particular, a new criterion (contrast support) is also proposed to discover notable sales patterns among different market segments. This study develops an algorithm, called sales pattern mining (SPMING), for discovering RFM itemsets from several RFM-based market segments and then identifying unique, common, and particular sales patterns. The experimental results from two real datasets show that the SPMING algorithm can discover specific sales patterns in various market segments.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"28 1","pages":"37-59"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81898263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1
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