Includes bibliographical references.

Article 1. Mining integrated sequential patterns from multiple databases -- Article 2. Mining partners in trajectories -- Article 3. Soft set theory based decision support system for mining electronic government dataset -- Article 4. Patient oriented readability assessment for heart disease healthcare documents.

[Article Title: Mining Integrated Sequential Patterns From Multiple Databases / Christie I. Ezeife, Vignesh Aravindan, and Ritu Chaturvedi, p. 1-21]

Abstract: Existing work on multiple databases (MDBs) sequential pattern mining cannot mine frequent sequences to answer exact and historical queries from MDBs having different table structures. This article proposes the transaction id frequent sequence pattern (TidFSeq) algorithm to handle the difficult problem of mining frequent sequences from diverse MDBs. The TidFSeq algorithm transforms candidate 1-sequences to get transaction subsequences where candidate 1-sequences occurred as (1-sequence, itssubsequenceidlist) tuple or (1-sequence, position id list). Subsequent frequent i-sequences are computed using the counts of the sequence ids in each candidate i-sequence position id list tuples. An extended version of the general sequential pattern (GSP)-like candidate generates and a frequency count approach is used for computing supports of itemset (I-step) and separate (S-step) sequences without repeated database scans but with transaction ids. Generated patterns answer complex queries from MDBs. The TidFSeq algorithm has a faster processing time than existing algorithms.

https://doi.org/10.4018/IJDWM.2020010101

[Article Title: Mining Partners in Trajectories / Diego Vilela Monteiro, Rafael Duarte Coelho dos Santos, and Karine Reis Ferreira, p. 22-38]

Abstract: Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.

https://doi.org/10.4018/IJDWM.2020010102

[Article Title: Soft Set Theory Based Decision Support System for Mining Electronic Government Dataset / Deden Witarsyah, Mohd Farhan Md Fudzee, Mohamad Aizi Salamat, Iwan Tri Riyadi Yanto, and Jemal Abawajy, p. 39-62]

Abstract: Electronic government (e-gov) is applied to support performance and create more efficient and effective public services. Grouping data in soft-set theory can be considered as a decision-making technique for determining the maturity level of e-government use. So far, the uncertainty of the data obtained through the questionnaire has not been maximally used as an appropriate reference for the government in determining the direction of future e-gov development policy. This study presents the maximum attribute relative (MAR) based on soft set theory to classify attribute options. The results show that facilitation conditions (FC) are the highest variable in influencing people to use e-government, followed by performance expectancy (PE) and system quality (SQ). The results provide useful information for decision makers to make policies about their citizens and potentially provide recommendations on how to design and develop e-government systems in improving public services.

https://doi.org/10.4018/IJDWM.2020010103

[Article Title: Patient Oriented Readability Assessment for Heart Disease Healthcare Documents / Hui-Huang Hsu, Yu-Sheng Chen, Chuan-Jie Lin, and Tun-Wen Pai, p. 63-72]

Abstract: Personal health literacy is an important indicator for a national health status. Providing citizens with sufficient medical knowledge can help them understand their own health conditions. To achieve this goal, an integrated system is developed for evaluating the readability of healthcare documents by taking heart disease as a specific topic. The mechanism can be extended to other target diseases and languages by changing the corresponding word databank. The assessment system for examining document readability is based on patient-oriented aspects rather than professional aspects. Commonly used terms and professional medical terms extracted from a query document were utilized as fundamental elements for readability analysis, and the derived features included term frequency of professional medical terms, proportion of professional medical terms, and diversity indicator of medical terms. A five-fold cross validation is applied to measure the robustness of the proposed approach. The experimental results achieved a recall rate of 0.93, a precision rate of 0.97, and an accuracy rate of 0.95.

https://doi.org/10.4018/IJDWM.2020010104