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Y. C. Tay

Publications and source records attributed to Y. C. Tay.

9 recordsLinked to original sources
arXiv2025

IRG: Modular Synthetic Relational Database Generation with Complex Relational Schemas

Relational databases (RDBs) are widely used by corporations and governments to store multiple related tables. Their relational schemas pose unique challenges to synthetic data generation for privacy-preserving data sharing, e.g., for collaborative analytical and data mining tasks, as well as software testing at various scales. Relational schemas typically include a set of primary and foreign key constraints to specify the intra-and inter-table entity relations, which also imply crucial intra-and inter-table data correlations in the RDBs. Existing synthetic RDB generation approaches often focus on the relatively simple and basic parent-child relations, failing to address the ubiquitous real-world complexities in relational schemas in key constraints like composite keys, intra-table correlations like sequential correlation, and inter-table data correlations like indirectly connected tables. In this paper, we introduce incremental relational generator (IRG), a modular framework designed to handle these real-world challenges. In IRG, each table is generated by learning context from a depth-first traversal of relational connections to capture indirect inter-table relationships and constructs different parts of a table through several classical generative and predictive modules to preserve complex key constraints and data correlations. Compared to 3 prior art algorithms across 10 real-world RDB datasets, IRG successfully handles the relational schemas and captures critical data relationships for all datasets while prior works are incapable of. The generated synthetic data also demonstrates better fidelity and utility than prior works, implying its higher potential as a replacement for the basis of analytical tasks and data mining applications. Code is available at: https://github.com/li-jiayu-ljy/irg.

cs.DB↗
arXiv2024

A Performance Analysis of BFT Consensus for Blockchains

Distributed ledgers are common in the industry. Some of them can use blockchains as their underlying infrastructure. A blockchain requires participants to agree on its contents. This can be achieved via a consensus protocol, and several BFT (Byzantine Fault Tolerant) protocols have been proposed for this purpose. How do these protocols differ in performance? And how is this difference affected by the communication network? Moreover, such a protocol would need a timer to ensure progress, but how should the timer be set? This paper presents an analytical model to address these and related issues in the case of crash faults. Specifically, it focuses on two consensus protocols (Istanbul BFT and HotStuff) and two network topologies (Folded-Clos and Dragonfly). The model provides closed-form expressions for analyzing how the timer value and number of participants, faults and switches affect the consensus time. The formulas and analyses are validated with simulations. The conclusion offers some tips for analytical modeling of such protocols.

cs.PF↗
arXiv2024

The Role of Federated Learning in a Wireless World with Foundation Models

Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-generation wireless networks, where federated learning (FL) is a key enabler of distributed network intelligence. Currently, the exploration of the interplay between FMs and FL is still in its nascent stage. Naturally, FMs are capable of boosting the performance of FL, and FL could also leverage decentralized data and computing resources to assist in the training of FMs. However, the exceptionally high requirements that FMs have for computing resources, storage, and communication overhead would pose critical challenges to FL-enabled wireless networks. In this article, we explore the extent to which FMs are suitable for FL over wireless networks, including a broad overview of research challenges and opportunities. In particular, we discuss multiple new paradigms for realizing future intelligent networks that integrate FMs and FL. We also consolidate several broad research directions associated with these paradigms.

cs.NI↗
arXiv2023

Dominant Eigenvalue-Eigenvector Pair Estimation via Graph Infection

We present a novel method to estimate the dominant eigenvalue and eigenvector pair of any non-negative real matrix via graph infection. The key idea in our technique lies in approximating the solution to the first-order matrix ordinary differential equation (ODE) with the Euler method. Graphs, which can be weighted, directed, and with loops, are first converted to its adjacency matrix A. Then by a naive infection model for graphs, we establish the corresponding first-order matrix ODE, through which A's dominant eigenvalue is revealed by the fastest growing term. When there are multiple dominant eigenvalues of the same magnitude, the classical power iteration method can fail. In contrast, our method can converge to the dominant eigenvalue even when same-magnitude counterparts exist, be it complex or opposite in sign. We conduct several experiments comparing the convergence between our method and power iteration. Our results show clear advantages over power iteration for tree graphs, bipartite graphs, directed graphs with periods, and Markov chains with spider-traps. To our knowledge, this is the first work that estimates dominant eigenvalue and eigenvector pair from the perspective of a dynamical system and matrix ODE. We believe our method can be adopted as an alternative to power iteration, especially for graphs.

math.NA↗
arXiv2022

A simple model for citation curve

There is considerable interest in the citation count for an author's publications. This has led to many proposals for citation indices for characterizing citation distributions. However, there is so far no tractable model to facilitate the analysis of these distributions and the design of these indices. This paper presents a simple equation for such design and analysis. The equation has three parameters that are calibrated by three geometrical characteristics of a citation distribution. Its simple form makes it tractable. To demonstrate, the equation is used to derive closed-form expressions for various citation indices, analyze the effect of time and identify individual contribution to the Hirsch index for a group.

cs.DL↗
arXiv2018

A tool framework for tweaking features in synthetic datasets

Researchers and developers use benchmarks to compare their algorithms and products. A database benchmark must have a dataset D. To be application-specific, this dataset D should be empirical. However, D may be too small, or too large, for the benchmarking experiments. D must, therefore, be scaled to the desired size. To ensure the scaled D' is similar to D, previous work typically specifies or extracts a fixed set of features F = {F_1, F_2, . . . , F_n} from D, then uses F to generate synthetic data for D'. However, this approach (D -> F -> D') becomes increasingly intractable as F gets larger, so a new solution is necessary. Different from existing approaches, this paper proposes ASPECT to scale D to enforce similarity. ASPECT first uses a size-scaler (S0) to scale D to D'. Then the user selects a set of desired features F'_1, . . . , F'_n. For each desired feature F'_k, there is a tweaking tool T_k that tweaks D' to make sure D' has the required feature F'_k. ASPECT coordinates the tweaking of T_1,...,T_n to D', so T_n(...(T_1(D'))...) has the required features F'_1,...,F'_n. By shifting from D -> F -> D' to D -> D' -> F', data scaling becomes flexible. The user can customise the scaled dataset with their own interested features. Extensive experiments on real datasets show that ASPECT can enforce similarity in the dataset effectively and efficiently.

cs.DB↗
arXiv2016

A Utility Optimization Approach to Network Cache Design

In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.

cs.NI↗
arXiv2000

MOO: A Methodology for Online Optimization through Mining the Offline Optimum

Ports, warehouses and courier services have to decide online how an arriving task is to be served in order that cost is minimized (or profit maximized). These operators have a wealth of historical data on task assignments; can these data be mined for knowledge or rules that can help the decision-making? MOO is a novel application of data mining to online optimization. The idea is to mine (logged) expert decisions or the offline optimum for rules that can be used for online decisions. It requires little knowledge about the task distribution and cost structure, and is applicable to a wide range of problems. This paper presents a feasibility study of the methodology for the well-known k-server problem. Experiments with synthetic data show that optimization can be recast as classification of the optimum decisions; the resulting heuristic can achieve the optimum for strong request patterns, consistently outperforms other heuristics for weak patterns, and is robust despite changes in cost model.

cs.DS↗