Includes bibliographical references.

Enhancing the Diamond Document Warehouse Model -- The Model-Driven Architecture for the Trajectory Data Warehouse Modeling -- Recommender Systems Based on Resonance Relationship of Criteria With Choquet Operation -- Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis -- An Improvement of K-Medoids Clustering Algorithm Based on Fixed Point Iteration -- Data Discovery Over Time Series From Star Schemas Based on Association, Correlation, and Causality -- A Temporal Multidimensional Model and OLAP Operators.

[Article Title: Enhancing the Diamond Document Warehouse Model/ Maha Azabou, Ameen Banjar and Jamel Omar Feki, p. 1-25]

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.

DOI: 10.4018/IJDWM.2020100101

[Article Title: The Model-Driven Architecture for the Trajectory Data Warehouse Modeling/ Noura Azaiez and Jalel Akaichi, p. 26-43]

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.

DOI: 10.4018/IJDWM.2020100102

[Article Title: Recommender Systems Based on Resonance Relationship of Criteria With Choquet Operation/ Hiep Xuan Huynh, Le Hoang Son, Giap Nguyen Cu, Tri Minh Huynh and Huong Hoang Luong, p. 44-62]

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.

DOI: 10.4018/IJDWM.2020100103

[Article Title: Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis/ Vandana P. Janeja, Josephine M. Namayanja, Yelena Yesha, Anuja Kench and Vasundhara Misal, p. 63-83]

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.

DOI: 10.4018/IJDWM.2020100104

[Article Title: An Improvement of K-Medoids Clustering Algorithm Based on Fixed Point Iteration/ Xiaodi Huang, Minglun Ren and Zhongfeng Hu, p. 84-94]

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.

DOI: 10.4018/IJDWM.2020100105

[Article Title: Data Discovery Over Time Series From Star Schemas Based on Association, Correlation, and Causality/ Wallace Anacleto Pinheiro, Geraldo Xexéo, Jano Moreira de Souza and Ana Bárbara Sapienza Pinheiro, p. 95-111]

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.

DOI: 10.4018/IJDWM.2020100106

[Article Title: A Temporal Multidimensional Model and OLAP Operators/ Waqas Ahmed, Esteban Zimányi, Alejandro Ariel Vaisman and Robert Wrembel, p. 112-143]

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.

DOI: 10.4018/IJDWM.2020100107