XML is one of the most important standards for manipulating data on the Internet. However, querying large volumes of XML data represents a bottleneck for several computationally intensive applications. A solution is to pre-process the document in streaming mode with resources approximately proportional to document depth and query selectivity. Limited processing space can then accommodate much larger documents. But the actual savings vary so much as to make them unpredictable. To overcome this limitation of stream-processing we propose a new application of the path tree synopsis data structure. Such a synopsis provides a succinct description of the original document with low computational overhead and high accuracy for processing tasks like selectivity estimation and query answer approximation. In this paper, we formally define the path tree synopsis, informally introduced by and used by, and propose a new streaming algorithm to construct it. We also present an online stream-querying system able to estimate the cost for a given query before answering it accurately. The core algorithm is adapted from LQ, we apply it to path tree traversal, cost estimation, query processing and even optimizations.

The WTFast Gamers Private Network (GPN ®) is a client/server solution designed to improve network conditions with online games that utilize centralized game servers. Using a GPN ®, connections between the clients personal computer and game network are improved in terms of reduced network latency, spikes, and jitter.A common concern among players participating in online games is the game’s connection stability. A key aspect of WTFast’s GPN ® service is its greatly improved connection consistency. This is accomplished by routing data from the client to the game server in a more controlled manner.To determine what kind of conditions and improvements can be found using a GPN ®, we gathered data from thousands of GPN ® and non-GPN ® (regular internet) connections to create a statistical model of spike predictability, frequency, and severity using Markov Chains. Paired with a comparison of latency, spikes, and jitter across GPN ® / non-GPN ® lines, we provide a statistically sound report on the efficacy of WTFast’s GPN ® service.

A DW is needed to efficiently and continuously prepare data for practical real-time data analysis and forecasting with machine learning (ML) algorithms. This paper discusses extraction, transformation, and loading process (ETL) automation and the critical subsystems integration for algorithmic trading ML modelling and stock market forecasting. Additionally, we discuss a new API designed for direct access to the DW. It opens vast opportunities for performance and data processing time to become critical nonfunctional requirements.

This research intends to implement a data warehouse (DW) and ETL (Extract, Transform, Load) process to handle time-series data used by an XGBoost model to predict stock market prices. The ETL pipeline automates data ingestion from Financial Modelling Prep (FMP) API [2], performs necessary transformation, and stores it for future use. Furthermore, it leverages its design to optimize resource usage. The paper also explores the DW performance and estimations for growing workloads.

The increasing energy consumption of Large Language Models (LLMs) poses a significant challenge to their sustainable deployment. Service operators face a fundamental conflict: maximizing inference throughput often leads to prohibitive increases in total energy consumption and job duration. This report presents a rigorous, multi-platform analysis to resolve this performance-versus-efficiency dilemma. We investigate the impact of Activation-aware Weight Quantization (AWQ), a stateof-the-art quantization technique, on the performance and energy consumption of modern LLMs. Our experiments are conducted across three distinct NVIDIA GPU architectures: Hopper H200, Ampere A100, and Ada RTX 5000. Our results confirm that 4-bit AWQ is a dominant optimization, delivering substantial, concurrent gains: throughput is increased by up to 118.5% while energy consumption is reduced by up to 54.4%, all without catastrophic degradation in model accuracy. Our primary contribution is a practical, two-step methodology for optimal deployment: first, apply AWQ quantization, and second, select the batch size that minimizes the Energy-Delay Product (EDP). This strategy allows service operators to achieve a quantifiably optimal balance between performance and total cost, providing a validated path towards the sustainable deployment of AI systems.