Top-k query processing has recently received a significant amount of attention due to its wide application in information retrieval, multimedia search and recommendation generation. In this work, we consider the problem of how to efficiently answer a top-k query by using previously cached query results. While there has been some previous work on this problem, existing algorithms suffer from either limited scope or lack of scalability. In this paper, we propose two novel algorithms for handling this problem. The first algorithm LPTA + provides significantly improved efficiency compared to the state-of-the-art LPTA algorithm [26] by reducing the number of expensive linear programming problems that need to be solved. The second algorithm we propose leverages a standard space partition-based index structure in order to avoid many of the drawbacks of LPTAbased algorithms, thereby further improving the efficiency of queryprocessing. Throughextensiveexperimentsonvarious datasets, we demonstrate that our algorithms significantly outperform the state of the art.

Recently, a trend has been observed towards supporting rank-aware query operators, such as top-k, that enable users to retrieve only a limited set of the most interesting data objects. As data nowadays is commonly stored distributed over multiple servers, a challenging problem is to support rank-aware queries in distributed environments. In this paper, we propose a novel approach, called DiTo, for efficient top-k processing over multiple servers, where each server stores autonomously a fraction of the data. Towards this goal, we exploit the inherent relationship of top-k and skyline objects, and we employ the skyline objects of servers as a data summarization mechanism for efficiently identifying the servers that store top-k results. Relying on a thresholding scheme, DiTo retrieves the top-k result set progressively, while the number of queried servers and transferred data is minimized. Furthermore, we extend DiTo to support data summarizations of bounded size, thus restricting the cost of summary distribution and maintenance. To this end, we study the challenging problem of finding an abstraction of the skyline set of fixed size that influences the performance of DiTo only slightly. Our experimental evaluation shows that DiTo performs efficiently and provides a viable solution when a high degree of distribution is required.

We introduce a generic framework, termed RIPPLE, for processing rank queries in decentralized systems. Rank queries are particularly challenging, since the search area (i.e., which tuples qualify) can-not be determined by any peer individually. While our proposed framework is generic enough to apply to all decentralized structured systems, we show that when coupled with a particular distributed hash table (DHT) topology, it offers guaranteed worst-case performance. Specifically, rank query processing in our framework exhibits tunable polylogarithmic latency, in terms of the network size. Additionally we provide a means to trade-off latency for communication and processing cost. As a proof of concept, we apply RIPPLE for top-k query processing. Then, we consider skyline queries, and demonstrate that our framework results in a method that has better latency and lower overall communication cost than existing approaches over DHTs. Finally, we provide a RIPPLE-based approach for constructing a k-diversified set, which, to the best of our knowledge, is the first distributed solution for this problem. Extensive experiments with real and synthetic datasets validate the effectiveness of our framework.

using grid networks are now emerging. These applications have new and demanding requirements for efficient query processing. In order to meet these requirements, we have developed the DASCOSA-DB distributed database system. In this chapter, a detailed overview of the architecture and implementation of DASCOSA-DB is given, as well as a description of novel features developed in order to better support typical data-intensive applications running on a grid system: fault-tolerant query processing, dynamic refragmentation, allocation and replication of data fragments, and distributed semantic caching. 1