Time relaxed spatiotemporal trajectory joins

Improving Distance-Join Query processing with Voronoi-Diagram based partitioning in SpatialHadoop

A crowd enabled approach for processing nearest neighbor and range queries in incomplete databases with accuracy guarantee

In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed extensively in previous literature, the implications of constrained nearest neighbor queries have not been discussed. Due to their versatility, CNN queries are suitable to a wide range of applications from GIS systems to reverse nearest neighbor queries and multimedia applications. We develop methods for answering CNN queries with different properties and advantages. We prove the optimality (with respect to I/O cost) of one of the techniques proposed in this paper. The superiority of the proposed technique is shown by a performance analysis.KeywordsNear NeighborConvex PolygonRange QueryQuery ResultQuery PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Travel planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of finding probabilistic nearest neighbors and planning the corresponding travel routes in traffic-aware spatial networks (TANN queries) to avoid traffic congestions. We propose and study two probabilistic TANN queries: (1) a time-threshold query like “what is my closest restaurant with the minimum congestion probability to take at most 45 min?”, and (2) a probability-threshold query like “what is the fastest path to my closest petrol station whose congestion probability is less than 20 %?”. We believe that this type of queries may benefit users in many popular mobile applications, such as discovering nearby points of interest and planning convenient travel routes for users. The TANN queries are challenged by two difficulties: (1) how to define probabilistic metrics for nearest neighbor queries in traffic-aware spatial networks, and (2) how to process the TANN queries efficiently under different query settings. To overcome these challenges, we define a series of new probabilistic metrics and develop two efficient algorithms to compute the TANN queries. The performances of TANN queries are verified by extensive experiments on real and synthetic spatial data.

For the performance of geographic information systems, it has been widely recognized that efficient data structures and algorithms supporting proximity based access to spatial data are a crucial ingredient. Because of that, in the last years, a lot of data structures for storage of multidimensional non-point objects have been proposed in the literature (for an overview see our tutorial introduction in this volume). Almost all of these approaches are made to support queries based on spatial neighborhood of objects, so-called proximity queries. Common examples of such proximity queries are range queries, returning all objects intersecting a given (rectangular) region, or nearest neighbor queries, determining the object nearest to a given point. But a geographic or cartographic information system should also support queries that do not exclusively refer to the spatial neighborhood of objects. For example, different versions of spatial data must be organized such that proximity queries to any version of the data can be answered efficiently. Or, given a set of object classes, a spatial query that involves an arbitrary combination of these classes should also be supported. Because of the growing size of data sets to be handled in geographic information systems, an appropriate data organisation to support such and several other application-specific queries becomes more and more important. In this paper, we will present two approaches for access structures supporting the two types of queries mentioned above. Handling of versioned, spatial data is the topic of Section 2, while Section 3 discusses queries on some classes selected from the set of object classes stored in a geographic information system.

Data structures in a real-time environment

The performance of nearest neighbor (NN) queries degrades noticeably with increasing dimensionality of the data due to reduced selectivity of high-dimensional data and an increased number of seek operations during NN-query execution. If the NN-radii were known in advance, the disk accesses could be reordered such that seek operations are minimized. We therefore propose a new way of estimating the NN-radius based on the fractal dimensionality and sampling. It is applicable to any page-based index structure. We show that the estimation error is considerably lower than for previous approaches. In the second part of the paper, we present two applications of this technique. We show how the radius estimations can be used to transform k-NN queries into at most two range queries, and how it can be used to reduce the number of page reads during all-NN queries. In both cases, we observe significant speedups over traditional techniques for synthetic and real-world data.

In this paper, we present Pampoo, a novel distributed framework for efficient query processing in P2P systems. We propose a new locality preserving data structure Skip-trie as its substrate. Skip-trie incorporates the advantages of skip graph with features of traditional trie. Thus, Pampoo can efficiently support various types of queries such as range queries and k nearest neighbor queries. We study the time cost of search and update operations on Skip-trie structure under our Pampoo framework. We further briefly present a repairing strategy to boost the robustness of Pampoo system. Extensive experiments are conducted to verify the effectiveness and efficiency of our approach.

This paper presents a system framework to support continuous nearest surrounder (NS) queries in moving object environments. NS query finds the nearest objects at individual distinct angles from a query point. This query distinguishes itself from other conventional spatial queries such as range queries and nearest neighbor queries by considering both distance and angular aspects of objects with respect to a query point. One of NS query applications is to monitor the nearest objects around an observation point. In our framework, a centralized server is dedicated to collect object location updates, to determine affected NS queries of each object location update, to compute the incremental result change of affected queries and to deliver result updates to corresponding interested users/applications that initiate the queries. In particular, we propose algorithms namely, safe region formation and partial query evaluation, that can significantly improve the system performance. Through simulations, we validate our proposed algorithms over a wide range of settings

A fundamental problem for peer-to-peer (P2P) applications in mobile-pervasive computing environment is to efficiently identify the node that stores particular data items and download them while preserving battery power. In this paper, we propose a P2P Minimum Boundary Rectangle (PMBR, for short) which is a new spatial index specifically designed for mobile P2P environments. A node that contains desirable data item (s) can be easily identified by reading the PMBR index. Then, we propose a selective tuning algorithm, called Distributed exponential Sequence Scheme (DSS, for short), that provides clients with the ability of selective tuning of data items, thus preserving the scarce power resource. The proposed algorithm is simple but efficient in supporting linear transmission of spatial data and processing of location-aware queries. The results from theoretical analysis and experiments show that the proposed algorithm with the PMBR index is scalable and energy efficient in both range queries and nearest neighbor queries.

There are two basic concerns for supporting multi-dimensional range query in P2P overlay networks. The first is to preserve data locality in the process of data space partitioning, and the second is the maintenance of data locality among data items with an exponentially expanding rate and an exponentially extending rate. The first problem has been well addressed by using recursive decomposition schemes, such as Quad tree, K-d tree, Z-order, and Hilbert curve. While the second problem was recently identified by our novel data structure: HD Tree. This paper is a follow-up to our previous work in HD Tree. In this paper, we explore how data locality can be easily maintained, and how range query can be efficiently supported in HD Tree. This is done by introducing two basic routing strategies, hierarchical routing and distributed routing. Although hierarchical routing can be applied to any two nodes in the system, it generates high volume traffic towards nodes near the root, and has very limited options to cope with a node failure. On the other hand, distributed routing concerns source and destination pairs only at the same depth, but traffic load is bound to some nodes at two neighboring depths, and multiple options can be found to redirect a routing request. Because HD Tree supports multiple routes between any two nodes in the system, routing in HD Tree is very flexible, and can be designed for many purposes, like fault tolerance, or dynamic load balancing. Distributed Routing Oriented Combined Routing algorithm is one such routing strategies implemented so far. It is a hybrid algorithm combining advantages from both the hierarchical routing and the distributed routing. The experimental results show that the DROCR algorithm achieves considerable performance gain over the equivalent tree routing at the highest depth examined. In the experiment of supporting multi-dimensional range query, we employ the Z-order space filling curve over the HD Tree overlay layer. We are expecting that the performance of range query will vary proportionally with the change of range size, and reasonably with the increase of dimensionality.

Uncertain data has emerged as a key data type in many applications. New and efficient query processing techniques need to be developed due to the inherent complexity of this new type of data. In this paper, we investigate a special type of query, range queries, on uncertain data. We propose a threshold interval indexing structure that aims to balance different time consuming factors to achieve an optimal overall query performance. We also present a more efficient version of our structure which loads its primary tree into memory for faster processing. Experimental results are presented to justify the efficiency of the proposed query processing technique.

Semantic Web database is an RDF database due to increased use of Semantic Web in real life applications; one can find heavy growth in RDF database. As there is a tremendous increase in RDF data, performance and scalability issues are of main concern. This chapter discusses improving and scaling up query performance for increasingly growing Semantic Web. It discusses current Semantic Web data storage techniques, which have been found to scale poorly and have poor query performance. It discusses the partitioning techniques vertical and horizontal partitioning to improve query performance. To further improve the query performance, along with these partitioning techniques, various compression techniques can also be used. Relational data offers faster execution of queries as compared to RDF data. To demonstrate these ideas, semantic data is converted to relational data and then query performance improvement techniques are applied. The scaling up of Semantic Web data is also discussed.

We introduce Rosetta, a probabilistic range filter designed specifically for LSM-tree based key-value stores. The core intuition is that we can sacrifice filter probe time because it is not visible in end-to-end key-value store performance, which in turn allows us to significantly reduce the filter false positive rate for every level of the tree. Rosetta indexes all binary prefixes of a key using a hierarchically arranged set of Bloom filters. It then converts each range query into multiple probes, one for each non-overlapping binary prefix. Rosetta has the ability to track workload patterns and adopt a beneficial tuning for each individual LSM-tree run by adjusting the number of Bloom filters it uses and how memory is spread among them to optimize the FPR/CPU cost balance. We show how to integrate Rosetta in a full system, RocksDB, and we demonstrate that it brings as much as a 40x improvement compared to default RocksDB and 2-5x improvement compared to state-of-the-art range filters in a variety of workloads and across different levels of the memory hierarchy (memory, SSD, hard disk). We also show that, unlike state-of-the-art filters, Rosetta brings a net benefit in RocksDB's overall performance, i.e., it improves range queries without losing any performance for point queries.

Similarity search in metric spaces represents an important paradigm for content-based retrieval of many applications. Existing centralized search structures can speed-up retrieval, but they do not scale up to large volume of data because the response time is linearly increasing with the size of the searched file. Four scalable and distributed similarity search structures will be presented. By exploiting parallelism in a dynamic network of computers, they all achieve practically constant search time for similarity range or nearest neighbor queries in data-sets of arbitrary sizes. Moreover, a small amount of replicated routing information on each server increases logarithmically. At the same time, the potential for interquery parallelism is increasing with the growing data-sets because the relative number of servers utilized by individual queries is decreasing. All these properties are verified by experiments on a prototype system using real-life data-sets. Results are used to establish specific pros and cons of individual approaches in different situations.