CAWAL: A novel unified analytics framework for enterprise web applications and multi-server environments
- URL: http://arxiv.org/abs/2503.23244v1
- Date: Sat, 29 Mar 2025 22:55:33 GMT
- Title: CAWAL: A novel unified analytics framework for enterprise web applications and multi-server environments
- Authors: Özkan Canay, Ümit Kocabıçak,
- Abstract summary: This paper presents the Combined Analytics and Web Application Log (CAWAL) framework as an alternative model and an on-premises framework.<n>CAWAL enables precise data collection and cross-domain tracking in web farms while complying with data ownership and privacy regulations.<n> Integrated into an enterprise-grade web application, CAWAL has demonstrated superior performance, achieving approximately 24% and 85% lower response times compared to Open Web Analytics (OWA) and Matomo, respectively.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In web analytics, cloud-based solutions have limitations in data ownership and privacy, whereas client-side user tracking tools face challenges such as data accuracy and a lack of server-side metrics. This paper presents the Combined Analytics and Web Application Log (CAWAL) framework as an alternative model and an on-premises framework, offering web analytics with application logging integration. CAWAL enables precise data collection and cross-domain tracking in web farms while complying with data ownership and privacy regulations. The framework also improves software diagnostics and troubleshooting by incorporating application-specific data into analytical processes. Integrated into an enterprise-grade web application, CAWAL has demonstrated superior performance, achieving approximately 24% and 85% lower response times compared to Open Web Analytics (OWA) and Matomo, respectively. The empirical evaluation demonstrates that the framework eliminates certain limitations in existing tools and provides a robust data infrastructure for enhanced web analytics.
Related papers
- GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics [9.549568621873386]
GateLens is an Algebra-based tool for analyzing datasets in the automotive domain.<n>It achieves higher F1 scores and handling complex and ambiguous queries with greater robustness.<n>It reduces analysis time by over 80% while maintaining high accuracy and reliability.
arXiv Detail & Related papers (2025-03-27T17:48:32Z) - Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework [0.0]
This study presents an approach that uses session and page view data collected through the CAWAL framework, enriched through specialized processes, for advanced predictive modeling and anomaly detection in web usage mining applications.<n>The results show that this approach offers detailed insights into user behavior and system performance metrics, making it a reliable solution for improving large-scale web portals' efficiency, reliability, and scalability.
arXiv Detail & Related papers (2025-02-01T12:21:59Z) - Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions [59.5243730853157]
Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets.<n>This article conducts a comparative analysis of three advanced federated LLM (FedLLM) frameworks that integrate knowledge distillation (KD) and split learning (SL) to mitigate these issues.
arXiv Detail & Related papers (2025-01-08T11:37:06Z) - PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality [5.276674920508729]
Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices.<n>Despite FA's broad relevance, the applicability of existing FA systems is limited by compromised accuracy; lack of flexibility for data analytics; and an inability to scale effectively.<n>We describe our approach to combine privacy, scalability, and practicality to build and deploy a system that overcomes these limitations.
arXiv Detail & Related papers (2024-12-03T10:03:12Z) - InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation [79.09622602860703]
We introduce InsightBench, a benchmark dataset with three key features.<n>It consists of 100 datasets representing diverse business use cases such as finance and incident management.<n>Unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics.
arXiv Detail & Related papers (2024-07-08T22:06:09Z) - Optimal Event Monitoring through Internet Mashup over Multivariate Time
Series [77.34726150561087]
This framework supports the services of model definitions, querying, parameter learning, model evaluations, data monitoring, decision recommendations, and web portals.
We further extend the MTSA data model and query language to support this class of problems for the services of learning, monitoring, and recommendation.
arXiv Detail & Related papers (2022-10-18T16:56:17Z) - Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review [62.997667081978825]
This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.
The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
arXiv Detail & Related papers (2022-04-29T08:06:05Z) - Distributed data analytics [8.415530878975751]
Recommendation systems are a key component of online service providers.
Financial industry has adopted ML to harness large volumes of data in areas such as fraud detection, risk-management, and compliance.
arXiv Detail & Related papers (2022-03-26T14:10:51Z) - Reproducible Performance Optimization of Complex Applications on the
Edge-to-Cloud Continuum [55.6313942302582]
We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum.
Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour.
Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.
arXiv Detail & Related papers (2021-08-04T07:35:14Z) - Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn.
We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
arXiv Detail & Related papers (2020-12-01T11:46:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.