Comprehensive data-driven systems require the integration of various access control and privacy patterns to address the diverse needs of subjects. However, existing approaches often struggle to simultaneously support precise access control, privacy preservation, and efficient policy maintenance. This paper presents G2D (Graph to Data), a novel technique that employs a Data Regulatory Graph (DRG) to dynamically generate data authorization statements tailored to specific subjects. G2D unifies access control and privacy by producing authorized SQL queries and specifying necessary data transformations for sensitive fields. Experimental results demonstrate that G2D incurs minimal execution overhead, simplifies policy updates, and effectively balances system performance with data protection, even under high concurrency. These findings highlight G2D’s potential to support scalable, privacy-aware data access in complex environments.

Dengue fever poses a significant global public health challenge, particularly in regions with tropical and sub-tropical climates. Over the past six decades, Thailand has witnessed a surge in dengue fever outbreaks, reporting over 50,000 cases annually. Addressing these outbreaks effectively requires contin-uous public awareness initiatives and active community engagement. Interestingly, today, public awareness of dengue fever is significantly driven by posts on social media platforms. However, the sheer volume and diversity of posts may lead to crucial dengue- related messages being overshadowed. This paper thus focuses on developing the data filter component, which employs a deep learning technique to automatically classify the each individual Thai post containing dengue-specific keywords into two categories: dengue-related and dengue-unrelated. Essentially, the data filter component serves as the core engine of the TanRabad-AWARE system, aiming to streamline and prioritize crucial information from social media platforms to enhance dengue public awareness and promote the prevention and control of dengue outbreaks.

Developing front-end applications involving large datasets with concurrent usage poses significant challenges. These challenges encompass efficient data retrieval and visualization. This paper addresses these issues within the domain of time-series data and explores methods such as progressive data retrieval and data aggregation strategies to effectively manage and present large datasets.

This paper explores the user experience (UX) de-sign challenges for individuals with varying levels of digital proficiency, focusing on the TanRabad SURVEY app as a case study. Through the analysis of data evidence and user interviews, common obstacles are identified, and potential solutions to address these challenges are proposed. These solutions have been evaluated by approximately 30 users and have shown effectiveness in resolving the identified issues.

Table has been recognized as a simply and widely used data representation scheme. Each table alone typically contains rich and useful information which is valuable for many applications such as information retrieval, question-answering and etc. While all table formats can simply be parsed by human, this parsing is difficult for computer, prohibiting such applications to be done in an automatic manner. In this paper, we thus propose the comprehensive and novel table interpretation technique, namely tInterpreter. Essentially, it transforms a table into its corresponding horizontal 1-dimensional tables. To achieve this, the underlying work is based on (i) the similarity of two given cells with respect to the data type and the semantic correspondence concerns; (ii) the discovery for the boundary of a primitive table residing in a composite table; (iii) the identification of the attribute-value relationship and the value association of cells; and (iv) the integration of two pieces of similar or dissimilar information. The experimental result showed that the overall effectiveness of tInterpreter was higher than Chen, Tengli and Kim.

It is common practice for software developers to use examples to guide development efforts. This largely unwritten, yet standard, practice of "develop by example" is often supported by examples bundled with library or framework packages, provided in textbooks, and made available for download on both official and unofficial web sites. However, the vast number of examples that are embedded in the billions of lines of already developed library and framework code are largely untapped. We have developed XSnippet, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand. In particular, our work makes three primary contributions. First, a range of queries is provided to allow developers to switch between a context-independent retrieval of code snippets to various degrees of context-sensitive retrieval for object instantiation queries. Second, a novel graph-based code mining algorithm is provided to support the range of queries and enable mining within and across method boundaries. Third, an innovative context-sensitive ranking heuristic is provided that has been experimentally proven to provide better ranking for best-fit code snippets than context-independent heuristics such as shortest path and frequency. Our experimental evaluation has shown that XSnippet has significant potential to assist developers, and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.