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We propose and study a new ranking problem in versioned databases. Consider a database of versioned objects which have different valid instances along a history (e.g., documents in a web archive). Durable top-<i>k</i> search finds the set of objects that are consistently in the top-<i>k</i> results of a query (e.g., a keyword query) throughout a given time(More)
Given a set of users, their friend relationships, and a distance threshold per friend pair, the proximity detection problem is to find each pair of friends such that the Euclidean distance between them is within the given threshold. This problem plays an essential role in friend-locator applications and massively multiplayer online games. Existing proximity(More)
—The online shortest path problem aims at computing the shortest path based on live traffic circumstances. This is very important in modern car navigation systems as it helps drivers to make sensible decisions. To our best knowledge, there is no efficient system/solution that can offer affordable costs at both client and server sides for online shortest(More)
Given a point set <i>P</i> of customers (e.g., WiFi receivers) and a point set <i>Q</i> of service providers (e.g., wireless access points), where each <i>q</i> &#8712; <i>Q</i> has a capacity <i>q.k</i>, the <i>capacity constrained assignment</i> (CCA) is a matching <i>M</i> &#8838; <i>Q</i> &#215; <i>P</i> such that (i) each point <i>q</i> &#8712;(More)
Consider a set of <i>customers</i> (e.g., WiFi receivers) and a set of <i>service providers</i> (e.g., wireless access points), where each provider has a <i>capacity</i> and the quality of service offered to its customers is anti-proportional to their distance. The <i>Capacity Constrained Assignment</i> (CCA) is a matching between the two sets such that (i)(More)
Deducing trip related information from web-scale datasets has received very large amounts of attention recently. Identifying points of interest (POIs) in geo-tagged photos is one of these problems. The problem can be viewed as a standard clustering problem of partitioning two dimensional objects. In this work, we study spectral clustering which is the first(More)
In order to index Web images, the whole associated texts are partitioned into a sequence of text blocks, then the local relevance of a term to the corresponding image is calculated with respect to both its local occurrence in the block and the distance of the block to the image. Thus, the overall relevance of a term is determined as the sum of all its local(More)
Most existing work on sequence databases use correlation (e.g., Eu-clidean distance and Pearson correlation) as a core function for various analytical tasks. Typically, it requires users to set a length for the similarity queries. However, there is no steady way to define the proper length on different application needs. In this work we focus on discovering(More)