Recently, result diversification has attracted a lot of attention as a means to improve the quality of results retrieved by user queries. In this article, we introduce a novel definition of diversity called DisC diversity. Given a tuning parameter r , which we call radius, we consider two items to be similar if their distance is smaller than or equal to r . A DisC diverse subset of a result contains items such that each item in the result is represented by a similar item in the diverse subset and the items in the diverse subset are dissimilar to each other. We show that locating a minimum DisC diverse subset is an NP-hard problem and provide algorithms for its approximation. We extend our definition to the multiple radii case, where each item is associated with a different radius based on its importance, relevance, or other factors. We also propose adapting DisC diverse subsets to a different degree of diversification by adjusting r , that is, increasing the radius (or zooming-out) and decreasing the radius (or zooming-in). We present efficient implementations of our algorithms based on the M-tree, a spatial index structure, and experimentally evaluate their performance.

Result diversification has recently attracted considerable attention as a means of increasing user satisfaction in recommender systems, as well as in web and database search. In this paper, we focus on the problem of selecting the k-most diverse items from a result set. Whereas previous research has mainly considered the static version of the problem, in this paper, we exploit the dynamic case in which the result set changes over time, as for example, in the case of notification services. We define the CONTINUOUS k-DIVERSITY PROBLEM along with appropriate constraints that enforce continuity requirements on the diversified results. Our proposed approach is based on cover trees and supports dynamic item insertion and deletion. The diversification problem is in general NP-hard; we provide theoretical bounds that characterize the quality of our cover tree solution with respect to the optimal one. Since results are often associated with a relevance score, we extend our approach to account for relevance. Finally, we report experimental results concerning the efficiency and effectiveness of our approach on a variety of real and synthetic datasets.

The explosion of big data emphasizes the need for scalable data diversification, especially for applications based on web, scientific, and business databases. However, achieving effective diversification in a multi-user environment is a rather challenging task due to the inherent high processing costs of current data diversification techniques. In this paper, we address the concurrent diversification of multiple search results using various approximation techniques that provide orders of magnitude reductions in processing cost, while maintaining comparable quality of diversification as compared to sequential methods. Our extensive experimental evaluation shows the scalability exhibited by our proposed methods under various workload settings.

To assist users in database exploration, we present the YmalDB system, a database system enhanced with a recommendation functionality. Along with the results of each user query, YmalDB computes and presents to the users additional results, called Ymal (i.e., "You May Also Like") results, that are highly related with the results of their original query. Such results are computed using the most interesting sets of attribute values, called faSets, that appear either in the results of the original query or in the results of an appropriately expanded one. The interestingness of a faSet is based on its frequency both in the query result and in the database.

Data diversification provides users with a concise and meaningful view of the results returned by search queries. In addition to taming the information overload, data diversification also provides the benefits of reducing data communication costs as well as enabling data exploration. The explosion of big data emphasizes the need for data diversification in modern data management platforms, especially for applications based on web, scientific, and business databases. Achieving effective diversification, however, is rather a challenging task due to the inherent high processing costs of current data diversification techniques. This challenge is further accentuated in a multi-user environment, in which multiple search queries are to be executed and diversified concurrently. In this paper, we propose the DoS scheme, which addresses the problem of scalable diversification of multiple search results. Our experimental evaluation shows the scalability exhibited by DoS under various workload settings, and the significant benefits it provides compared to sequential methods.

Search result diversification has attracted considerable attention as a means of improving the quality of results retrieved by user queries. In this demonstration, we present Poikilo, a tool to assist users in locating and evaluating diverse results. We provide implementations of a wide suite of models and algorithms to compute and compare diverse results. Users can tune various diversification parameters, combine diversity with relevance and also see how diverse results change over time in the case of streaming data.

Result diversification has recently attracted considerable attention as a means of increasing user satisfaction in recommender systems, as well as in web and database search. In this paper, we focus on the problem of selecting the k-most diverse items from a result set. Whereas previous research has mainly considered the static version of the problem, in this paper, we exploit the dynamic case in which the result set changes over time, as for example, in the case of notification services. We define the Continuous k-Diversity Problem along with appropriate constraints that enforce continuity requirements on the diversified results. Our proposed approach is based on cover trees and supports dynamic item insertion and deletion. The diversification problem is in general NP-complete; we provide theoretical bounds that characterize the quality of our solution based on cover trees with respect to the optimal solution. Finally, we report experimental results concerning the efficiency and effectiveness of our approach on a variety of real and synthetic datasets.

Recently, result diversification has attracted a lot of attention as a means to improve the quality of results retrieved by user queries. In this paper, we propose a new, intuitive definition of diversity called DisC diversity. A DisC diverse subset of a query result contains objects such that each object in the result is represented by a similar object in the diverse subset and the objects in the diverse subset are dissimilar to each other. We show that locating a minimum DisC diverse subset is an NP-hard problem and provide heuristics for its approximation. We also propose adapting DisC diverse subsets to a different degree of diversification. We call this operation zooming. We present efficient implementations of our algorithms based on the M-tree, a spatial index structure, and experimentally evaluate their performance.

Typically, users interact with database systems by formulating queries. However, many times users do not have a clear understanding of their information needs or the exact content of the database, thus, their queries are of an exploratory nature. In this paper, we propose assisting users in database exploration by recommending to them additional items that are highly related with the items in the result of their original query. Such items are computed based on the most interesting sets of attribute values (or faSets) that appear in the result of the original user query. The interestingness of a faSet is defined based on its frequency both in the query result and in the database instance. Database frequency estimations rely on a novel approach that employs an e-tolerance closed rare faSets representation. We report evaluation results of the efficiency and effectiveness of our approach on both real and synthetic datasets.

Keyword-based search in relational databases allows users to discover relevant information without knowing the database schema or using complicated queries. However, such searches may return an overwhelming number of results, often loosely related to the user intent. In this paper, we propose personalizing keyword database search by utilizing user preferences. Query results are ranked based on both their relevance to the query and their preference degree for the user. To further increase the quality of results, we consider two new metrics that evaluate the goodness of the result as a set, namely coverage of many user interests and content diversity. We present an algorithm for processing preference queries that uses the preferential order between keywords to direct the joining of relevant tuples from multiple relations. We then show how to reduce the complexity of this algorithm by sharing computational steps. Finally, we report evaluation results of the efficiency and effectiveness of our approach.