Guest Editor’s editorial: ranking in databases

Abstract

We are dealing with increasingly large volumes of data as well as more complex and diverse types of data. Examples of such data include log data like transaction logs and web logs, text, image and multimedia data, scientific data and sensor data. There is an increasing need for effective techniques to search, explore and analyze such datasets. Similarity queries and top-k ranking are important paradigms to search and explore such datasets. The purpose of this special issue on ranking in databases is to cover the new directions of research in this area. This issue contains four research papers, which are briefly discussed as follows. The paper “Combining CPU and GPU architectures for fast similarity search” proposes a novel, less-explored but nevertheless important direction to speed up similarity search. While most previous works focus on new indexing techniques, this paper studies how to parallelize similarity search using combination of many-core GPU devices and multicore CPU processors. The paper shows how modern architectures can be used to speed up ranking algorithms. In the paper “On optimality-ratio and coverage in ranking of joined search results”, the authors study a novel ranking problem. Instead of ranking individual items, they consider ranking of combinations of items, e.g., a combination of a hotel and two restaurants. They study the semantics and query processing algorithms in this context. The paper shows the kind of new ranking problems that emerge in these new-age applications. The paper titled “Distributed top-k query processing by exploiting skyline summaries” studies top-k processing in distributed environments. With increasing volumes of data, the data is typically distributed over multiple servers. Processing top-k