Abstract. Given a spatial location and a set of keywords, a top-k spatial keyword query returns the k best spatio-textual objects ranked according to their proximity to the query location and relevance to the query keywords. There are many applications handling huge amounts of geotagged data, such as Twitter and Flickr, that can benefit from this query. Unfortunately, the state-of-the-art approaches require non-negligible processing cost that incurs in long response time. In this paper, we propose a novel index to improve the performance of top-k spatial keyword queries named Spatial Inverted Index (S2I). Our index maps each distinct term to a set of objects containing the term. The objects are stored differently according to the document frequency of the term and can be retrieved efficiently in decreasing order of keyword relevance and spatial proximity. Moreover, we present algorithms that exploit S2I to process top-k spatial keyword queries efficiently. Finally, we show through extensive experiments that our approach outperforms the state-of-the-art approaches in terms of update and query cost.

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Understanding the structure of complex networks and uncovering the properties of their constituents has been for many decades at the center of study of several fundamental sciences, such as discrete mathematics and graph theory. Especially during the previous decade, we have witnessed an explosion in complex network data, with two cornerstone paradigms being the biological networks and the social networks. The large scale, but also the complexity, of these types of networks constitutes the need for efficient graph mining algorithms. In both examples, one of the most important tasks is to identify closely connected network components comprising nodes that share similar properties. In the case of biological networks, this could mean the identification of proteins that bind together to carry their biological function, while in the social networks, this can be seen as the identification of communities. Motivated by this analogy, we apply the Power Graph Analysis methodology, for the first time to the best of our knowledge, to the field of community mining. The model was introduced in bioinformatics research and in this work is applied to the problem of community detection in complex networks. The advances in the field of community mining allow us to experiment with widely accepted benchmark data sets, and our results show that the suggested methodology performs favorably against state of the art methods for the same task, especially in networks with large numbers of nodes.

Abstract. In applications such as market analysis, it is of great interest to product manufacturers to have their products ranked as highly as possible for a significant number of customers. However, customer preferences change over time, and product manufacturers are interested in monitoring the evolution of the popularity of their products, in order to discover those products that are consistently highly ranked. To take into account the temporal dimension, we define the continuous influential query and present algorithms for efficient processing and retrieval of continuous influential data objects. Furthermore, our algorithms support incremental retrieval of the next continuous influential data object in a natural way. To evaluate the performance of our algorithms, we conduct a detailed experimental study for various setups.

Abstract. With the rapid growth of the Web, keyword-based searches become extremely ambiguous. To guide users to identify the results of their interest, in this paper, we consider an alternative way for presenting the results of a keyword search. In particular, we propose a framework for organizing the results into groups that contain results with similar content and refer to similar temporal characteristics. Moreover, we provide summaries of results as hints for query refinement. A summary of a result set is expressed as a set of popular keywords in the result set. Finally, we report evaluation results of the effectiveness of our approach.