arXiv subjects

Wan Shen Lim

Publications and source records attributed to Wan Shen Lim.

3 recordsLinked to original sources

arXiv2025

This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!

Tuning database management systems (DBMSs) is challenging due to trillions of possible configurations and evolving workloads. Recent advances in tuning have led to breakthroughs in optimizing over the possible configurations. However, due to their design and inability to leverage query-level historical insights, existing automated tuners struggle to adapt and re-optimize the DBMS when the environment changes (e.g., workload drift, schema transfer). This paper presents the Booster framework that assists existing tuners in adapting to environment changes (e.g., drift, cross-schema transfer). Booster structures historical artifacts into query-configuration contexts, prompts large language models (LLMs) to suggest configurations for each query based on relevant contexts, and then composes the query-level suggestions into a holistic configuration with beam search. With multiple OLAP workloads, we evaluate Booster's ability to assist different state-of-the-art tuners (e.g., cost-/machine learning-/LLM-based) in adapting to environment changes. By composing recommendations derived from query-level insights, Booster assists tuners in discovering configurations that are up to 74% better and in up to 4.7x less time than the alternative approach of continuing to tune from historical configurations.

cs.DB↗

arXiv2025

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia

Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often results in artificial intelligence (AI) models that fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing high-quality, culturally relevant data for SEA languages. By involving contributors from SEA countries, SEA-VL aims to ensure better cultural relevance and diversity, fostering greater inclusivity of underrepresented languages in VL research. Beyond crowdsourcing, our initiative goes one step further in the exploration of the automatic collection of culturally relevant images through crawling and image generation. First, we find that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing. Second, despite the substantial progress in generative vision models, synthetic images remain unreliable in accurately reflecting SEA cultures. The generated images often fail to reflect the nuanced traditions and cultural contexts of the region. Collectively, we gather 1.28M SEA culturally-relevant images, more than 50 times larger than other existing datasets. Through SEA-VL, we aim to bridge the representation gap in SEA, fostering the development of more inclusive AI systems that authentically represent diverse cultures across SEA.

cs.CV↗

arXiv2020

Mainlining Databases: Supporting Fast Transactional Workloads on Universal Columnar Data File Formats

The proliferation of modern data processing tools has given rise to open-source columnar data formats. The advantage of these formats is that they help organizations avoid repeatedly converting data to a new format for each application. These formats, however, are read-only, and organizations must use a heavy-weight transformation process to load data from on-line transactional processing (OLTP) systems. We aim to reduce or even eliminate this process by developing a storage architecture for in-memory database management systems (DBMSs) that is aware of the eventual usage of its data and emits columnar storage blocks in a universal open-source format. We introduce relaxations to common analytical data formats to efficiently update records and rely on a lightweight transformation process to convert blocks to a read-optimized layout when they are cold. We also describe how to access data from third-party analytical tools with minimal serialization overhead. To evaluate our work, we implemented our storage engine based on the Apache Arrow format and integrated it into the DB-X DBMS. Our experiments show that our approach achieves comparable performance with dedicated OLTP DBMSs while enabling orders-of-magnitude faster data exports to external data science and machine learning tools than existing methods.

cs.DB↗