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Abstract

The promises inherent in users coming together to form data sharing network communities, bring to the foreground new problems formulated over such dynamic, ever growing, computing, storage, and networking infrastructures. A key open challenge is to harness these highly distributed resources toward the development of an ultra scalable, efficient search engine. From a technical viewpoint, any acceptable solution must fully exploit all available resources dictating the removal of any centralized points of control, which can also readily lead to performance bottlenecks and reliability/availability problems. Equally importantly, however, a highly distributed solution can also facilitate pluralism in informing users about internet content, which is crucial in order to preclude the formation of information-resource monopolies and the biased visibility of content from economically-powerful sources. To meet these challenges, the work described here puts forward MINERVA$\infty$, a novel search engine architecture, designed for scalability and efficiency. MINERVA$\infty$ encompasses a suite of novel algorithms, including algorithms for creating data networks of interest, placing data on network nodes, load balancing, top-k algorithms for retrieving data at query time, and replication algorithms for expediting top-k query processing. We have implemented the proposed architecture and we report on our extensive experiments with real-world, web-crawled, and synthetic data and queries, showcasing the scalability and efficiency traits of MINERVA$\infty$.