Reverse Thinking in Spatial Queries

In recent years, an increasing number of researches are conducted on spatial queries regarding the influence of query objects. Among these queries, reverse k nearest neighbors (RkNN) query is the one studied the most extensively. Reverse k furthest neighbors (RkFN) queries is the natural complement of RkNN queries. RkNN query is introduced to reflect the influence of the query object. Since this representation is intuitive, RkNN query has attracted significant attention among the database community. Later, reverse top-k queries was introduced, and also used extensively to represent influence. In many scenarios, when we consider the influence of an spatial object, reverse thinking is involved. That is, whether an object is influential to another object is depending on how the other object assess this object, other than how this object considers the other object. In this thesis, we study three problems involves reverse thinking. We first study the problem of efficiently computing RkFN queries. We are the first to propose a solution for arbitrary value of k. Based on several interesting observations, we present an efficient algorithm to process the RkFN queries. We also present a rigorous theoretical analysis to study various important aspects of the problem and our algorithm. An extensive experimental study demonstrates that our algorithm outperforms the state-of-the-art algorithm even for k=1. The accuracy of our theoretical analysis is also verified. We then study the problem of selecting set of representative products considering both diversity and coverage based on reverse top-k queries. Since this problem is NP-hard, we employ a greedy algorithm. We adopt MinHash and KMV Synopses to assist set operations. Our experimental study demonstrates the performance of the proposed algorithm. We also study the problem of maximizing spatial influence of facility bundle based on RkNN queries. We are the first to study this problem. We prove its NP-hardness, and propose a branch-and-bound best first search algorithm that greedily select the currently best facility until we get the required number of facilities. We introduce the concept of kNN region. It allows us to avoid redundant calculation with dynamic programming technique. Experiments show that our algorithm is orders of magnitudes better than our baseline algorithm

The Flexible Group Spatial Keyword Query

We present a new class of service for location based social networks, called the Flexible Group Spatial Keyword Query, which enables a group of users to collectively find a point of interest (POI) that optimizes an aggregate cost function combining both spatial distances and keyword similarities. In addition, our query service allows users to consider the tradeoffs between obtaining a sub-optimal solution for the entire group and obtaining an optimimized solution but only for a subgroup. We propose algorithms to process three variants of the query: (i) the group nearest neighbor with keywords query, which finds a POI that optimizes the aggregate cost function for the whole group of size n, (ii) the subgroup nearest neighbor with keywords query, which finds the optimal subgroup and a POI that optimizes the aggregate cost function for a given subgroup size m (m <= n), and (iii) the multiple subgroup nearest neighbor with keywords query, which finds optimal subgroups and corresponding POIs for each of the subgroup sizes in the range [m, n]. We design query processing algorithms based on branch-and-bound and best-first paradigms. Finally, we provide theoretical bounds and conduct extensive experiments with two real datasets which verify the effectiveness and efficiency of the proposed algorithms.Comment: 12 page

Model Counting of Query Expressions: Limitations of Propositional Methods

Query evaluation in tuple-independent probabilistic databases is the problem of computing the probability of an answer to a query given independent probabilities of the individual tuples in a database instance. There are two main approaches to this problem: (1) in `grounded inference' one first obtains the lineage for the query and database instance as a Boolean formula, then performs weighted model counting on the lineage (i.e., computes the probability of the lineage given probabilities of its independent Boolean variables); (2) in methods known as `lifted inference' or `extensional query evaluation', one exploits the high-level structure of the query as a first-order formula. Although it is widely believed that lifted inference is strictly more powerful than grounded inference on the lineage alone, no formal separation has previously been shown for query evaluation. In this paper we show such a formal separation for the first time. We exhibit a class of queries for which model counting can be done in polynomial time using extensional query evaluation, whereas the algorithms used in state-of-the-art exact model counters on their lineages provably require exponential time. Our lower bounds on the running times of these exact model counters follow from new exponential size lower bounds on the kinds of d-DNNF representations of the lineages that these model counters (either explicitly or implicitly) produce. Though some of these queries have been studied before, no non-trivial lower bounds on the sizes of these representations for these queries were previously known.Comment: To appear in International Conference on Database Theory (ICDT) 201

MonetDB/XQuery: a fast XQuery processor powered by a relational engine

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met