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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 on "marriage" of traditional top-k semantics and possible(More)
Efficient processing of top-<i>k</i> queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top-<i>k</i> processing in domains such as the Web, multimedia search, and distributed systems has shown a great impact on performance. In this survey, we describe and classify top-<i>k</i>(More)
This paper introduces RankSQL, a system that provides a systematic and principled framework to support efficient evaluations of ranking (<i>top-k</i>) queries in relational database systems (RDBMS), by extending relational algebra and query optimization. Previously, <i>top-k</i> query processing is studied in the middleware scenario or in RDBMS in a(More)
The rich dependency structure found in the columns of real-world relational databases can be exploited to great advantage, but can also cause query optimizers---which usually assume that columns are statistically independent---to underestimate the selectivities of conjunctive predicates by orders of magnitude. We introduce CORDS, an efficient and scalable(More)
Ranking is an important property that needs to be fully supported by current relational query engines. Recently, several rank-join query operators have been proposed based on rank aggregation algorithms. Rank-join operators progressively rank the join results while performing the join operation. The new operators have a direct impact on traditional query(More)
Despite the increasing importance of data quality and the rich theoretical and practical contributions in all aspects of data cleaning, there is no single end-to-end off-the-shelf solution to (semi-)automate the detection and the repairing of violations w.r.t. a set of heterogeneous and ad-hoc quality constraints. In short, there is no commodity platform(More)
Data cleaning is an important problem and data quality rules are the most promising way to face it with a declarative approach. Previous work has focused on specific formalisms, such as functional dependencies (FDs), conditional functional dependencies (CFDs), and matching dependencies (MDs), and those have always been studied in isolation. Moreover, such(More)
Violations of functional dependencies (FDs) are common in practice , often arising in the context of data integration or Web data extraction. Resolving these violations is known to be challenging for a variety of reasons, one of them being the exponential number of possible " repairs ". Previous work has tackled this problem either by producing a single(More)
In this paper we present GDR, a Guided Data Repair framework that incorporates user feedback in the cleaning process to enhance and accelerate existing automatic repair techniques while minimizing user involvement. GDR consults the user on the updates that are most likely to be beneficial in improving data quality. GDR also uses machine learning methods to(More)
Ranking and aggregation queries are widely used in data exploration, data analysis, and decision-making scenarios. While most of the currently proposed ranking and aggregation techniques focus on deterministic data, several emerging applications involve data that is unclean or uncertain. Ranking and aggregating uncertain (probabilistic) data raises new(More)