Query reranking as a service

Query Reranking As A Service

Many web databases are “hidden” behind proprietary search interfaces that enforce the top-k output constraint, i.e., each query returns at most k of all matching tuples, preferentially selected and returned according to a proprietary ranking function. In this paper, we initiate research into the novel problem of skyline discovery over top-k hidden web databases. Since skyline tuples provide cri...

Reranking Medline Citations by Relevance to a Difficult Biological Query

We have initialized research aimed at automatically extracting Medline citations of biomedical articles and reranking them according to their relevance to a certain biomedical property difficult to express as PubMed query. Our proposed approach to this problem is to train support vector machines as classifiers able to distinguish relevant citations from the rest of retrieved citations. We used ...

Quaestor: Query Web Caching for Database-as-a-Service Providers

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results an...

Query Privacy in Sensing-as-a-Service Platforms

The Internet of Things (IoT) promises to revolutionize the way we interact with the physical world. Even though this paradigm is still far from being completely realized, there already exist Sensing-as-aService (SaaS) platforms that allow users to query for IoT data. While this model offers tremendous benefits, it also entails increasingly challenging privacy issues. In this paper, we concentra...

A Foundational Framework for Service Query Optimization