Traditional information retrieval (IR) models, such as keyword-based and vector-based techniques, have long been used in centralized systems. However, the Web’s re-decentralization, with its focus on data ownership and privacy, calls for a re-evaluation of these methods in these settings. While standards for decentralized search enhance privacy to some extent, they also introduce computational overhead, black-box decision-making, and infrastructure complexity. Despite these challenges, traditional IR techniques remain largely unexplored in such environments. This article presents an innovative application of traditional IR models in the decentralized Web by adapting them for Personal Online Data Stores (PODs), where search parties have varying access rights. We explore their role in source selection, document ranking, and result merging, extending them to meet decentralized search demands. Using Solid PODs and a synthetic medical dataset, we evaluate these models in a privacy-sensitive environment. Our findings demonstrate that extended IR methods provide an effective balance of performance, interpretability, and efficiency. These approaches hold strong potential as privacy-preserving alternatives for decentralized search on a re-decentralized Web. Notably, our top-performing model achieved competitive results in top-item retrieval compared to centralized search systems, maintaining high relevance scores under both limited and full data access conditions.

The growing demand for data ownership and privacy is reshaping how information is accessed, managed, integrated, and recommended. Building on the inaugural DESERE workshop at The Web Conference 2024, this second edition advances research on Decentralised Search and Recommendation platforms such as Personal Online Datastores (PODs), where users retain control of their data and explicitly manage permissions. As ecosystems decentralise, traditional information retrieval must be revisited while standards for new techniques and system designs are developed to ensure efficient, accurate, and privacy-preserving search. The Second DESERE workshop at CIKM 2025 focuses on infrastructures and retrieval algorithms for user-controlled data. It convenes a cross-disciplinary community spanning data retrieval, management and integration, semantic technologies, recommendation systems, privacy-aware computing, and search efficiency to explore approaches that prioritize user agency, data ownership, and scalable retrieval across PODs and related architectures. Through paper presentations, panels, and interactive sessions, the workshop will highlight challenges, opportunities, and solutions for privacy-preserving IR. These discussions are especially relevant to domains where user-centric design and data stewardship are critical-such as personal finance, education, and high-stakes areas like criminal justice and health.

Data privacy and sovereignty are open challenges in today’s Web, which the Solid ( https://solidproject.org ) ecosystem aims to meet by providing personal online datastores (pods) where individuals can control access to their data. Solid allows developers to deploy applications with access to data stored in pods, subject to users’ permission. For the decentralised Web to succeed, the problem of search over pods with varying access permissions must be solved. The ESPRESSO framework takes the first step in exploring such a search architecture, enabling large-scale keyword search across Solid pods with varying access rights. This paper provides a comprehensive experimental evaluation of the performance and scalability of decentralised keyword search across pods on the current ESPRESSO prototype. The experiments specifically investigate how controllable experimental parameters influence search performance across a range of decentralised settings. This includes examining the impact of different text dataset sizes (0.5 MB to 50 MB per pod, divided into 1 to 10,000 files), different access control levels (10%, 25%, 50%, or 100% file access), and a range of configurations for Solid servers and pods (from 1 to 100 pods across 1 to 50 servers). The experimental results confirm the feasibility of deploying a decentralised search system to conduct keyword search at scale in a decentralised environment.

Abstract The increasing centralization of the Web raises serious concerns regarding privacy, security, and user autonomy. In response, there has been a renewed interest in the development of secure personal information management systems and a movement towards decentralization. Decentralized personal online data stores (pods) represent a revolutionary example within this movement, built on the W3C’s existing guidelines – an approach exemplified by initiatives such as ( https://solidproject.org ). In the Solid paradigm, individuals store their personal data in pods and have absolute discretion when choosing to grant access to different users and applications. A barrier to the adoption of the pod approach is the predominant reliance on centralized indexes for search functionality in current Web and Web-based systems. This paper introduces the framework, which is designed to facilitate this new paradigm of large-scale searches within personal data stores while respecting the individual pod owners’ data access governance. The current ESPRESSO prototype integrates access control within pod indexes to enhance distributed keyword-based search. ESPRESSO’s unique contribution not only enhances search capabilities on the decentralized Web but also paves the way for future explorations in decentralized search technologies.

Leveraging Big Data (BD) processing frameworks to process large-scale Resource Description Framework (RDF) datasets holds a great interest in optimizing query performance. Modern BD services are complicated data systems, where tuning the configurations notably affects the performance. Benchmarking different frameworks and configurations provides the community with best practices towards selecting the most suitable configurations. However, most of these benchmarking efforts are classified as descriptive or diagnostic analytics. Moreover, there is no standardization for comparing and contrasting these benchmarks based on quantitative ranking techniques. This paper aims to fill this timely research gap by proposing ranking criteria (called Bench-ranking) that provide prescriptive analytics via ranking functions. In particular, Bench-ranking starts by describing the current state-of-the-art single-dimensional ranking limitations. Next, we discuss the recent benchmarking requirements for sophisticated approaches over multi-dimensional ranking. Finally, we discuss the ranking criteria goodness by reviewing its conformance and coherence metrics. We validate Bench-ranking by conducting an empirical study using large RDF datasets under a relational BD engine, i.e., Apache Spark-SQL. The proposed ranking techniques provide the practitioners with clear insights to make an informed decision, especially with experimental trade-offs for such complex solution space.

International audience