Top-k query processing plays an important role in data retrieval to give an answer to a user quickly. To our best knowledge, the traditional top-k query processing works with a local database. As the size of a database is lager, the database is stored in a distributed network, and it requires

Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty makes traditional techniques inapplicable. We introduce new probabilistic formulations for top-k queries. Our formulations are based

probabilistic formulations for top-k queries. Our formulations are based on marriage of traditional top-k semantics with possible worlds semantics. In the light of these formulations, we construct a framework that encapsulates a novel probabilistic model and efficient query processing techniques to tackle

Abstract. Recently, there has been an increased interest in incorporating in database management systems rank-aware query operators, such as top-k queries, that allow users to retrieve only the most interesting data objects. In this paper, we propose a cache-based approach for efficiently

contributors, location-based service providers (LBSPs), and system users. The data collector gathers reviews about points-of-interest (POIs) from data contributors, while LBSPs purchase POI data sets from the data collector and allow users to perform spatial top-k queries which ask for the POIs in a certain

, we present a novel framework for top-k query processing in largescale P2P networks, where the dataset is horizontally distributed to peers. Our proposed framework returns the exact results to the user, while minimizing the number of queried super-peers and transferred data. Through simulations we

Abstract — We consider a novel problem of top-k query processing under budget constraints. We provide both a framework and a set of algorithms to address this problem. Existing algorithms for top-k processing are budget-oblivious, i.e., they do not take budget constraints into account when making

directly into the physical world, many new challenges arise for top-k query processing. From the client’s perspective, different users may request different set of information, with different priorities and at different times. Thus, the top-k search not only should be multi-dimensional, but also across

Top-k query processing is an important building block for ranked retrieval, with applications ranging from text and data integration to distributed aggregation of network logs and sensor data. Top-k queries operate on index lists for a query’s elementary conditions and aggregate scores for result

. In this paper, we present a novel approach, called SPEERTO, for top-k query processing in large-scale peer-topeer networks, where the dataset is horizontally distributed over the peers. Towards this goal, we explore the applicability of the skyline operator for efficiently routing top-k queries in a large super