Taxis are commonly used by tourists to travel around unfamiliar cities they visit. These taxis today have GPS devices, which can then be used to collect a significant amount of data on the movement of tourists. One problem with this idea, however, is the question of how to extract that movement data from the raw GPS data, which includes a lot of other data, such as vehicle IDs, timestamps, and speeds, etc. The purpose of this research is to propose a data management platform to process heterogeneous data including taxi data, social media data, and place data for tourist behavior analysis. We propose a data pipeline that can be scaled in order to process a significant amount of data regarding taxi trajectory and social media, with two objectives. The first objective is to extract the tourist trajectory data from the raw GPS data and produce a data integration module enriched with a knowledge base of tourist trajectories. This knowledge base is constructed through the extension of semantic trajectory ontology (STO) and mobility behavior ontology (MBO). The second objective is to extract tourist activities/point of interests (POIs) from geo-tagged Twitter data. The results of the data pipeline can readily be used for tourist behavior analysis, such as tourist descriptive analysis, popular tourist destinations/zones, and tourist movement patterns identification. We leverage the study’s results to demonstrate the real-life case study in Bangkok during the Songkran Festival in 2019. Thus, we could precisely identify tourist movement during various periods, determine popular destinations/zones, discover high density density of taxi destination points for a given trajectory type, and display the top ten tourist destinations, as well as prominent tourism keywords or trends at the time. This can provide insight to governments and businesses related to tourism regarding the trajectories and activities of tourists, and it will help predict future tourism trends.

Massive open online course (MOOC) is an online course aimed at unlimited participation and open access via the web. Although there are many MOOC providers, they typically focus on the online course providing and typically do not link with traditional education and business sector requirements. This paper presents a MOOC service framework that focuses on adopting MOOC to provide additional services to support students in traditional education and to provide credit bank consisting of student academic credentials for business sector demand. Particularly, it extends typical MOOC to support academic / credential record and transcript issuance. The MOOC service framework consists of five layers: authentication, resources, learning, assessment and credential layers. We discuss the adoption of the framework in Thai MOOC, the national MOOC system for Thai universities. Several main issues related to the framework adoption are discussed, including the service strategy and model as well as infrastructure design for large-scale MOOC service.

Data integration is one of the most challenging tasks for digital collections whose data are stored across various repositories. Data integration across digital repositories has several challenges. First, data heterogeneity in terms of data schema and data values usually occurs across diverse data sources. Second, heterogeneity in data representation and semantic issues are among the problems. The same data may appear in different repositories with varied data representations, i.e., metadata schema. Recent research has focused on matching several related metadata schemas. In this paper, a metadata integration framework is proposed to support digital repositories in socio-cultural anthropology at the Princess Maha Chakri Sirindhorn Anthropology Centre (SAC), Thailand. The proposed framework is defined based on the Metadata Lifecycle Model (MLM). It utilizes non-procedural schema mappings to express data relationships in diverse schemas. A case study of metadata integration over the SAC digital repositories was conducted to validate the framework. The SAC common metadata schema was designed to support data mapping across 13 digital repositories. The SAC “One Search” system was developed to exemplify the system implementation of the framework. Evaluation results showed that the proposed metadata integration framework can support domain experts in socio-cultural anthropology in unified searching across the repositories.

In the current era of extensive data usage across industries, data collection, preservation, utilization, and organization has become more challenging and nuanced because it is necessary to consider critical concerns such as data security, privacy, and legal issues, apart from efficiency issues. As a result, Thai government initiated the idea and effort to implement data governance throughout the government agency. This paper showcases the implementation of data governance in a governmental research organization with highly diverse structured and unstructured data. The implementation follows international standards and the guidelines of the Digital Government Development Agency (DGA). The executives set up the working body, including the Data Governance Council and Data Stewards, responsible for setting up and deploying policies and regulations. Creating awareness and the necessary infrastructure are the main focuses in the first-year phase. The metadata was designed to extend DGA's version and match the organization's unique requirements. A data catalog platform was developed accordingly. We organized activities to boost employee awareness and participation, including advertising and data catalog platform training. By the end of the first year of implementation, every organization unit had registered at least one data record into the data catalog.

Tourism after the outbreak of the emerging epidemic of COVID-19 has drastically changed. Tourist attractions will be certified with Green National Park and New Normal Standards. Starting in the year 2021 onwards, Thailand's national parks are important tourist destinations, of which 155 nationwide will be subject to complying with such standards to ensure safety, hygiene and environmentally friendly service starting in the year 2021 onwards. This research aims to develop a support system for self-assessment and gap analysis based on Smart Self-Assessment for New Normal Tourism Standards to enable the national parks to assess themselves and be prepared for future actual assessments. The system development focuses on user data import design and report output, system performance test, self-assessment score percentage difference tests, and system performance evaluation by the experts. The percentage difference of self-assessment scores is found at 0.0 for all items after adding details in some of the work lists based on the experts’ opinions, whereas, the performance testing indicates that the system developed is applicable and highly efficient (= 4.40, S.D.= 0.54).