In the era of huge datasets, the top-k search becomes an effective way to decrease the search time of top-k objects. The original top-k search requires a monotone combination function and lists of objects ordered by attribute values. Our approach of the top-k search is motivated by complex user preferences over product catalogues. Such user preferences are composed of the local user preferences...

Advances in sensing and data gathering technologies have resulted in an explosion in the volume of data that is being generated, processed, and archived. In particular, this information overload calls for new methods for querying large spatial datasets, since users are often not interested in merely retrieving a list of all data items satisfying a query, but would, instead, like a more informat...

On natural language text collections, finding the k documents most relevant to a query is generally solved with inverted indexes. On general string collections, however, more sophisticated data structures are necessary. Navarro and Nekrich [SODA 2012] showed that a linear-space index can solve such top-k queries in optimal time O(m + k), where m is the query length. Konow and Navarro [DCC 2013]...

Sequence mining is an important data mining task. In order to retrieve interesting sequences from a large database, a minimum support threshold is needed to be specified. Unfortunately, specification of the appropriated support threshold is very difficult for users who are novice to mining queries and task specific data. To avoid this difficulty of specification of the appropriated support thre...

Information streams provide today a prevalent way of publishing and consuming content on the Web, especially due to the great success of social networks. Top-k queries over the streams of interest allow limiting results to the most relevant content, while continuous processing of such queries is the most effective approach in large scale systems. However, current systems fail in combining conti...

Utility mining, which refers to the discovery of itemsets with utilities higher than a user-specified minimum utility threshold, is an important task and has a wide range of applications, especially in e-commerce. But setting an appropriate minimum utility threshold is a difficult problem. If the minimum threshold is set to low, too many high utility itemsets will be generated and it takes a lo...

There has been much interest in answering top-k queries on probabilistic data in various applications such as market analysis, personalised services, and decision making. In relation to probabilistic data, the most common problem in answering top-k queries is selecting the semantics of results according to their scores and top-k probabilities. In this paper, we propose a novel top-k best probab...

There are several applications, such as play lists of songs or movies, and shopping carts, where users are interested in finding top-k packages, consisting of sets of items. In response to this need, there has been a recent flurry of activity around extending classical recommender systems (RS), which are effective at recommending individual items, to recommend packages, or sets of items. The fe...

In recent years, top-k query processing has attracted much attention in large-scale scenarios, where computing only the k “best” results is often sufficient. One line of research targets the so-called top-k join problem, where the k best final results are obtained through joining partial results. In this paper, we study the top-k join problem in a Linked Data setting, where partial results are ...

Advanced Data Mining applications require more and more support from relational database engines. Especially clustering applications in high dimensional features space demand a proper support of multiple Top-k queries in order to perform projected clustering. Although some research tackles to problem of optimizing restricted ranking (top-k) queries, there is no solution considering more than on...