Ihab F. Ilyas

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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)
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)
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)
Uncertainty pervades many domains in our lives. Current real-life applications, e.g., location tracking using GPS devices or cell phones, multimedia feature extraction, and sensor data management, deal with different kinds of uncertainty. Finding the nearest neighbor objects to a given query point is an important query type in these applications. In this(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 repair(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)
We propose XSEED, a synopsis of path queries for cardinality estimation that is accurate, robust, efficient, and adaptive to memory budgets. XSEED starts from a very small kernel, and then incrementally updates information of the synopsis. With such an incremental construction, a synopsis structure can be dynamically configured to accommodate different(More)