Mohamed A. Soliman

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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)
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)
We present a generic framework to make wrapper induction algorithms tolerant to noise in the training data. This enables us to learn wrappers in a completely unsupervised manner from automatically and cheaply obtained noisy training data, e.g., using dictionaries and regular expressions. By removing the site-level supervision that wrapper-based techniques(More)
Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes needs to handle new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records' scores induces(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)
The INhibitor of Growth (ING) family of type II tumour suppressors are encoded by five genes in mammals (ING1-ING5), most of which encode multiple isoforms via splicing, and all of which contain a highly conserved plant homeodomain (PHD) finger motif. Since their discovery approximately ten years ago, significant progress has been made in understanding(More)
Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes introduces new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records’ scores induces a(More)
Ranking queries report the top-<i>K</i> results according to a user-defined scoring function. A widely used scoring function is the weighted summation of multiple scores. Often times, users cannot precisely specify the weights in such functions in order to produce the preferred order of results. Adopting uncertain/incomplete scoring functions (e.g., using(More)
ING proteins interact with core histones through their plant homeodomains (PHDs) and with histone acetyltransferase (HAT) and histone deacetylase (HDAC) complexes to alter chromatin structure. Here we identify a lamin interaction domain (LID) found only in ING proteins, through which they bind to and colocalize with lamin A. Lamin knockout (LMNA(-/-)) cells(More)