基于时间窗口聚类的时序数据索引压缩

子序列匹配是时间序列挖掘的经典课题,旨在发现大型数据集中的相似数据序列.很多文献关注固定时间段的序列的查询.但对于多种不同时间段的查询的问题仍然未解决好.基于时间段的查询含义是有时间窗口限制的查询.为了满足多时间段上的查询,简单地为每个时间段的子序列构建索引既耗时又耗存储空间.从目前的文献来看,已有的索引无法满足具有不...     

Processing moving queries over moving objects using motion-adaptive indexes

This paper describes a motion-adaptive indexing scheme for efficient evaluation of moving continual queries (MCQs) over moving objects. It uses the concept of motion-sensitive bounding boxes (MSBs) to model moving objects and moving queries. These bounding boxes automatically adapt their sizes to the dynamic motion behaviors of individual objects. Instead of indexing frequently changing object positions, we index less frequently changing object and query MSBs, where updates to the bounding boxes are needed only when objects and queries move across the boundaries of their boxes. This helps decrease the number of updates to the indexes. More importantly, we use predictive query results to optimistically precalculate query results, decreasing the number of searches on the indexes. Motion-sensitive bounding boxes are used to incrementally update the predictive query results. Furthermore, we introduce the concepts of guaranteed safe radius and optimistic safe radius to extend our motion-adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion-adaptive indexing scheme is efficient for the evaluation of both moving continual range queries and moving continual kNN queries.     

Adapting metric indexes for searching in multi-metric spaces

An important research issue in multimedia databases is the retrieval of similar objects. For most applications in multimedia databases, an exact search is not meaningful. Thus, much effort has been devoted to develop efficient and effective similarity search techniques. A recent approach that has been shown to improve the effectiveness of similarity search in multimedia databases resorts to the usage of combinations of metrics (i.e., a search on a multi-metric space). In this approach, the desirable contribution (weight) of each metric is chosen at query time. It follows that standard metric indexes cannot be directly used to improve the efficiency of dynamically weighted queries, because they assume that there is only one fixed distance function at indexing and query time. This paper presents a methodology for adapting metric indexes to multi-metric indexes, that is, to support similarity queries with dynamic combinations of metric functions. The adapted indexes are built with a single distance function and store partial distances to estimate the dynamically weighed distances. We present two novel indexes for multimetric space indexing, which are the result of the application of the proposed methodology.     

A scalable P2P platform for the knowledge grid

The knowledge grid needs to operate with a scalable platform to provide large-scale intelligent services. A key function of such a platform is to efficiently support various complex queries in a dynamic large-scale network environment. This paper proposes a platform to support index-based path queries by incorporating a semantic overlay with an underlying structured P2P network that provides object location and management services. Various distributed indexing structures can be dynamically formed by publishing, semantic objects as indexing nodes. Queries are forwarded along the chains of semantic object pointers to search for objects. We investigate the deployment of a scalable distributed trie index for broadcast queries on key strings, propose a decentralized load balancing method for solving the problem of uneven load distribution incurred by heterogeneity of loads and node capacities and by the distributed trie index, and give an approach for improving the availability of the semantic overlay and its trie index. Experiments demonstrate the scalability of the proposed platform.     

Indexing spatiotemporal archives

Spatiotemporal objects – that is, objects that evolve over time – appear in many applications. Due to the nature of such applications, storing the evolution of objects through time in order to answer historical queries (queries that refer to past states of the evolution) requires a very large specialized database, what is termed in this article a spatiotemporal archive. Efficient processing of historical queries on spatiotemporal archives requires equally sophisticated indexing schemes. Typical spatiotemporal indexing techniques represent the objects using minimum bounding regions (MBR) extended with a temporal dimension, which are then indexed using traditional multidimensional index structures. However, rough MBR approximations introduce excessive overlap between index nodes, which deteriorates query performance. This article introduces a robust indexing scheme for answering spatiotemporal queries more efficiently. A number of algorithms and heuristics are elaborated that can be used to preprocess a spatiotemporal archive in order to produce finer object approximations, which, in combination with a multiversion index structure, will greatly improve query performance in comparison to the straightforward approaches. The proposed techniques introduce a query efficiency vs. space tradeoff that can help tune a structure according to available resources. Empirical observations for estimating the necessary amount of additional storage space required for improving query performance by a given factor are also provided. Moreover, heuristics for applying the proposed ideas in an online setting are discussed. Finally, a thorough experimental evaluation is conducted to show the merits of the proposed techniques. Edited by B. Seeger A short version of this article appeared as “Efficient indexing of spatiotemporal objects” in the Proceedings of Extending Database Technology 2002 [19]. This work was partially supported by NSF grants IIS-9907477, EIA-9983445, NSF IIS 9984729, NSF ITR 0220148, NSF IIS-0133825, NRDRP, and the U.S. Department of Defense.     

A distributed spatial index for error-prone wireless data broadcast

Information is valuable to users when it is available not only at the right time but also at the right place. To support efficient location-based data access in wireless data broadcast systems, a distributed spatial index (called DSI) is presented in this paper. DSI is highly efficient because it has a linear yet fully distributed structure that naturally shares links in different search paths. DSI is very resilient to the error-prone wireless communication environment because interrupted search operations based on DSI can be resumed easily. It supports search algorithms for classical location-based queries such as window queries and kNN queries in both of the snapshot and continuous query modes. In-depth analysis and simulation-based evaluation have been conducted. The results show that DSI significantly out-performs a variant of R-trees tailored for wireless data broadcast environments.     

无线数据广播环境下路网最近邻居节点的查询

针对大规模用户数量,首次提出结合无线数据广播技术,在路网环境中进行最近邻居节点查询.该方法使用基于Voronoi图的算法转化路网信息,将处理后的路网信息作为广播数据,不仅提高了用户的查询效率,还减少广播数据中的冗余信息.在数据调度上,采用Hilbert曲线对数据排序,从而保持广播数据的空间临近性.实验表明,上述方法在为用户提供高效查询的情况下有效减少了访问时间.     

A partition index for XML and semi-structured data

XML and other semi-structured data can be represented by a graph model. The paths in a data graph are used as a basic constructor of a query. Especially, by using patterns on paths, a user can formulate more expressive queries. Patterns in a path enlarge the search space of a data graph and current research for indexing semi-structured data focuses on reducing the search space. However, the existing indexes cannot reduce the search space when a data graph has some references.

In this paper, we introduce a partitioning technique for all paths in a data graph and an index graph which can effectively find appropriate path partitions for a path query with patterns.     

Precision Evaluation of Search Engines

In this paper, we present a general approach for statistically evaluating precision of search engines on the Web. Search engines are evaluated in two steps based on a large number of sample queries: (a) computing relevance scores of hits from each search engine, and (b) ranking the search engines based on statistical comparison of the relevance scores. In computing relevance scores of hits, we study four relevance scoring algorithms. Three of them are variations of algorithms widely used in the traditional information retrieval field. They are cover density ranking, Okapi similarity measurement, and vector space model algorithms. In addition, we develop a new three-level scoring algorithm to mimic commonly used manual approaches. In ranking the search engines in terms of precision, we apply a statistical metric called probability of win. In our experiments, six popular search engines, AltaVista, Fast, Google, Go, iWon, and NorthernLight, were evaluated based on queries from two domains of interest: parallel and distributed processing, and knowledge and data engineering. The first query set contains 1726 queries collected from the index terms of papers published in the IEEE Transactions on Knowledge and Data Engineering. The second set contains 1383 queries collected from the index terms of papers published in the IEEE Transactions on Parallel and Distributed Systems. Search engines were queried and compared in two different search modes: the default search mode and the exact phrase search mode. Our experimental results show that these six search engines performed differently under different search modes and scoring methods. Overall, Google was the best. NorthernLight was mostly second in the default search mode, whereas iWon was mostly second in the exact phrase search mode.     

DART+: Direction-aware bichromatic reverse k nearest neighbor query processing in spatial databases

This article presents a novel type of queries in spatial databases, called the direction-aware bichromatic reverse k nearest neighbor(DBRkNN) queries, which extend the bichromatic reverse nearest neighbor queries. Given two disjoint sets, P and S, of spatial objects, and a query object q in S, the DBRkNN query returns a subset P′ of P such that k nearest neighbors of each object in P′ include q and each object in P′ has a direction toward q within a pre-defined distance. We formally define the DBRkNN query, and then propose an efficient algorithm, called DART, for processing the DBRkNN query. Our method utilizes a grid-based index to cluster the spatial objects, and the B⁺-tree to index the direction angle. We adopt a filter-refinement framework that is widely used in many algorithms for reverse nearest neighbor queries. In the filtering step, DART eliminates all the objects that are away from the query object more than a pre-defined distance, or have an invalid direction angle. In the refinement step, remaining objects are verified whether the query object is actually one of the k nearest neighbors of them. As a major extension of DART, we also present an improved algorithm, called DART+, for DBRkNN queries. From extensive experiments with several datasets, we show that DART outperforms an R-tree-based naive algorithm in both indexing time and query processing time. In addition, our extension algorithm, DART+, also shows significantly better performance than DART.     

The D-tree: an index structure for planar point queries in location-based wireless services

Location-based services (LBSs), considered as a killer application in the wireless data market, provide information based on locations specified in the queries. In this paper, we examine the indexing issue for querying location-dependent data in wireless LBSs; in particular, we focus on an important class of queries, planar point queries. To address the issues of responsiveness, energy consumption, and bandwidth contention in wireless communications, an index has to minimize the search time and maintain a small storage overhead. It is shown that the traditional point-location algorithms and spatial index structures fail to achieve either objective or both. This paper proposes a new index structure, called D-tree, which indexes spatial regions based on the divisions that form the boundaries of the regions. We describe how to construct a binary D-tree index, how to process queries based on the D-tree, and how to page the binary D-tree. Moreover, two parameterized methods for partitioning the original space, called fixed grid assignment (FGA) and adaptive grid assignment (AGA), are proposed to enhance the D-tree. The performance of the D-tree is evaluated using both synthetic and real data sets. Experimental results show that the proposed D-tree outperforms the well-known indexes such as the R/sup */-tree, and that both the FGA and AGA approaches can achieve different performance trade-offs between the index search time and storage overhead by fine-tuning their algorithmic parameters.     

面向移动对象连续k近邻查询的双层索引结构

移动对象连续k近邻（CKNN）查询是指给定一个连续移动的对象集合,对于任意一个k近邻查询q,实时计算查询q的k近邻并在查询有效时间内对查询结果进行实时更新.现实生活中,交通出行、社交网络、电子商务等领域许多基于位置的应用服务都涉及移动对象连续k近邻查询这一基础问题.已有研究工作解决连续k近邻查询问题时,大多需要通过多次迭代确定一个包含k近邻的查询范围,而每次迭代需要根据移动对象的位置计算当前查询范围内移动对象的数量,整个迭代过程的计算代价占查询代价的很大部分.为此,提出了一种基于网络索引和混合高斯函数移动对象分布密度的双重索引结构（grid GMM index,GGI）,并设计了移动对象连续k近邻增量查询算法（incremental search for continuous k nearest neighbors,IS-CKNN）.GGI索引结构的底层采用网格索引对海量移动对象进行维护,上层构建混合高斯模型模拟移动对象在二维空间中的分布.对于给定的k近邻查询q,IS-CKNN算法能够基于混合高斯模型直接确定一个包含q的k近邻的查询区域,减少了已有算法求解该区域的多次迭代过程;当移动对象和查询q位置发生变化时,进一步提出一种高效的增量查询策略,能够最大限度地利用已有查询结果减少当前查询的计算量.最后,在滴滴成都网约车数据集以及两个模拟数据集上进行大量实验,充分验证了算法的性能.     

Z曲线网格划分的最近邻查询

为了解决高维空间最近邻查询问题,在网格划分的基础上,利用Z曲线对网格排序并将二维空间中的点映射到一维空间中。考虑到点的分布和网格形状对查询的影响,提出最小查询层和方向变换的概念。只要给出查询点与任意点之间的方向变换,即可求出该点所在的网格Z值,从而求出任意查询层的所有网格Z值。证明了最近邻查询只需访问至最小查询层后再访问两层。基于此提出了最近邻查询算法,它适用于数据点任意分布的情况,该算法能够得到精确解。     

Two-dimensional indexing to provide one-integrated-memory view of distributed memory for a massively-parallel search engine

We propose two-dimensional indexing—a novel in-memory indexing architecture that operates over distributed memory of a massively-parallel search engine. The goal of two-dimensional indexing is to provide a one-integrated-memory view as in a single node system using one large integrated memory. In two-dimensional indexing, we partition the entire index into n× m fragments and distribute them over the memories of multiple nodes in such a way that each fragment is entirely stored in main memory of one node. The proposed architecture is not only scalable as it uses a scaled-out shared-nothing architecture but also is capable of achieving low query response time as it processes queries in main memory. We also propose the concept of the one-memory point, which is the amount of the memory space required to completely store the entire index in main memory providing a one-integrated-memory view. We first prove the effectiveness of two-dimensional indexing with single-keyword queries, and then, extend the notion so as to be able to handle multiple-keyword queries. To handle multiple-keyword queries, we adopt pre-join that materializes a multiple-keyword query a priori as well as a new notion of semi-memory join that obviates extensive communication overhead to perform join across multiple nodes. In experiments using the real-life search query set over a database consisting of 100 million Web documents crawled, we show that two-dimensional indexing can effectively provide a one-integrated-memory view without too much of additional memory compared with the single node system using one large integrated memory. We also show that, with a six-node prototype, in an ideal case, it significantly improves the query processing performance over a disk-based search engine with an equivalent amount of in-memory buffer but without two-dimensional indexing — by up to 535.54 times. This improvement is expected to get larger as the system is scaled-out with a larger number of machines.

     

Continuous visible k nearest neighbor query on moving objects

A visible k nearest neighbor (Vk NN) query retrieves k objects that are visible and nearest to the query object, where “visible” means that there is no obstacle between an object and the query object. Existing studies on the Vk NN query have focused on static data objects. In this paper we investigate how to process the query on moving objects continuously. We propose an effective filtering-and-refinement framework for evaluating this type of queries. We exploit spatial proximity and visibility properties between the query object and data objects to prune search space under this framework. A detailed cost analysis and a comprehensive experimental study are conducted on the proposed framework. The results validate the effectiveness of the pruning techniques and verify the efficiency of the proposed framework. The proposed framework outperforms a straightforward solution by an order of magnitude in terms of both communication and computation costs.     

MOVIES: indexing moving objects by shooting index images

With the exponential growth of moving objects data to the Gigabyte range, it has become critical to develop effective techniques for indexing, updating, and querying these massive data sets. To meet the high update rate as well as low query response time requirements of moving object applications, this paper takes a novel approach in moving object indexing. In our approach, we do not require a sophisticated index structure that needs to be adjusted for each incoming update. Rather, we construct conceptually simple short-lived index images that we only keep for a very short period of time (sub-seconds) in main memory. As a consequence, the resulting technique MOVIES supports at the same time high query rates and high update rates, trading this property for query result staleness. Moreover, MOVIES is the first main memory method supporting time-parameterized predictive queries. To support this feature, we present two algorithms: non-predictive MOVIES and predictive MOVIES. We obtain the surprising result that a predictive indexing approach—considered state-of-the-art in an external-memory scenario—does not scale well in a main memory environment. In fact, our results show that MOVIES outperforms state-of-the-art moving object indexes such as a main-memory adapted B ^x -tree by orders of magnitude w.r.t. update rates and query rates. In our experimental evaluation, we index the complete road network of Germany consisting of 40,000,000 road segments and 38,000,000 nodes. We scale our workload up to 100,000,000 moving objects, 58,000,000 updates per second and 10,000 queries per second, a scenario at a scale unmatched by any previous work.     

Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks

In this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in Euclidean space as well as in spatial networks. Existing techniques are sensitive toward objects and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We present a framework for continuous reverse k nearest neighbor (RkNN) queries by assigning each object and query with a safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. This significantly improves the computation cost. As a byproduct, our framework also reduces the communication cost in client–server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. We also conduct a rigid cost analysis for our Euclidean space RkNN algorithm. We show that our techniques can also be applied to answer bichromatic RkNN queries in Euclidean space as well as in spatial networks. Furthermore, we show that our techniques can be extended for the spatial networks that are represented by directed graphs. The extensive experiments demonstrate that our techniques outperform the existing techniques by an order of magnitude in terms of computation cost and communication cost.     

Range nearest-neighbor query

A range nearest-neighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearest-neighbor queries and has many applications. In this paper, we consider the ranges as (hyper)rectangles and propose efficient in-memory processing and secondary memory pruning techniques for RNN queries in both 2D and high-dimensional spaces. These techniques are generalized for kRNN queries, which return the k nearest neighbors for every point in the range. In addition, we devise an auxiliary solution-based index EXO-tree to speed up any type of NN query. EXO-tree is orthogonal to any existing NN processing algorithm and, thus, can be transparently integrated. An extensive empirical study was conducted to evaluate the CPU and I/O performance of these techniques, and the study showed that they are efficient and robust under various data sets, query ranges, numbers of nearest neighbors, dimensions, and cache sizes.     

Path dictionary: a new access method for query processing inobject-oriented databases

We present a new access method, called the path dictionary index (PDI) method, for supporting nested queries on object-oriented databases. PDI supports object traversal and associative search, respectively, with a path dictionary and a set of attribute indexes built on top of the path dictionary. We discuss issues on indexing and query processing in object-oriented databases; describe the operations of the new mechanism; develop cost models for its storage overhead and query and update costs; and compare the new mechanism to the path index method. The result shows that the path dictionary index method is significantly better than the path index method over a wide range of parameters in terms of retrieval and update costs and that the storage overhead grows slowly with the number of indexed attributes     

Answering XML Queries Using Path-Based Indexes: A Survey

The problem of answering XML queries using path-based indexes is to find efficient methods for accelerating the XML query with pre-designed index structures over the XML database. This problem received increasing interests and have been lucubrated in recent years. Regular path expression is the core of the XML query languages e.g., XPath and XQuery. Most of the state-of-the-art path-based XML indexes, therefore, hammer at how to efficiently answer the path-based XML queries. This paper surveys various approaches to indexing XML data proposed in the literature. We give a step by step analysis to show the evolution of index structures for XML path information, based on tree structures or more commonly, directed labeled graphs. For each approach, we first present the specific issue it aims to tackle, and then the proposed solution presented. Furthermore, construction, physical data storage and maintenance costs, are analyzed.