Indexing mobile objects using dual transformations

With the recent advances in wireless networks, embedded systems, and GPS technology, databases that manage the location of moving objects have received increased interest. In this paper, we present indexing techniques for moving object databases. In particular, we propose methods to index moving objects in order to efficiently answer range queries about their current and future positions. This problem appears in real-life applications such as predicting future congestion areas in a highway system or allocating more bandwidth for areas where a high concentration of mobile phones is imminent. We address the problem in external memory and present dynamic solutions, both for the one-dimensional and the two-dimensional cases. Our approach transforms the problem into a dual space that is easier to index. Important in this dynamic environment is not only query performance but also the update processing, given the large number of moving objects that issue updates. We compare the dual-transformation approach with the TPR-tree, an efficient method for indexing moving objects that is based on time-parameterized index nodes. An experimental evaluation shows that the dual-transformation approach provides comparable query performance but has much faster update processing. Moreover, the dual method does not require establishing a predefined query horizon.Received: 27 April 2003, Accepted: 11 May 2004, Published online: 14 September 2004Edited by: J. VeijalainenGeorge Kollios: Supported by NSF CAREER Award 0133825.Dimitrios Gunopulos: Supported by NSF ITR 0220148, NSF CAREER Award 9984729, NSF IIS-9907477, and NRDRP.Vassilis J. Tsotras: Supported by NSF IIS-9907477, NSF EIA-9983445 and the DoD.     

SaIL: A Spatial Index Library for Efficient Application Integration

With the proliferation of spatial and spatio-temporal data that are produced everyday by a wide range of applications, Geographic Information Systems (GIS) have to cope with millions of objects with diverse spatial characteristics. Clearly, under these circumstances, substantial performance speed up can be achieved with the use of spatial, spatio-temporal and other multi-dimensional indexing techniques. Due to the increasing research effort on developing new indexing methods, the number of available alternatives is becoming overwhelming, making the task of selecting the most appropriate method for indexing the data according to application needs rather challenging. Therefore, developing a library that can combine a variety of indexing techniques under a common application programming interface can prove to be a valuable tool. In this paper we present SaIL (SpAtial Index Library), an extensible framework that enables easy integration of spatial and spatio-temporal index structures into existing applications. We focus on design issues and elaborate on techniques for making the framework generic enough, so that it can support user defined data types, customizable spatial queries, and a broad range of spatial (and spatio-temporal) index structures, in a way that does not compromise functionality, extensibility and, primarily, ease of use. SaIL is publicly available and has already been successfully utilized for research and commercial applications. This work was conducted while the first author was visiting ESRI and was partially supported by ESRI, NSF grants IIS-9907477, EIA-9983445, and IIS-0220148.     

Optimizing spatial Min/Max aggregations

Aggregate computation over a collection of spatial objects appears in many real-life applications. Aggregates are computed on values (weights) associated with spatial objects, for example, the temperature or rainfall over the area covered by the object. In this paper we concentrate on MIN/MAX aggregations: given a query rectangle, find the minimum/maximum weight among all objects intersecting the query rectangle. Traditionally such queries have been performed as range searches. Assuming that objects are indexed by a spatial access method (SAM), the MIN/MAX is computed while retrieving those objects intersecting the query interval. This requires effort proportional to the number of objects satisfying the query, which may be large. A better approach is to maintain aggregate information among the index nodes of the spatial access method; then various index paths can be eliminated during the range search. Yet another approach is to build a specialized index that maintains the aggregate incrementally. We propose four novel optimizations for improving the performance of MIN/MAX queries when an index structure (traditional or specialized) is present. Moreover, we introduce the MR-tree, an R-tree-like dynamic specialized index that incorporates all four optimizations. Our experiments show that the MR-tree offers drastic performance improvement over previous solutions. As a byproduct of this work we present an optimized version of the MSB-tree, an index that has been proposed for the MIN/MAX computation over 1D interval objects.Received: 5 September 2003, Published online: 24 February 2005Edited by: T. ÖzsuVassilis J. Tsotras: This research was supported by NSF Grants IIS-9907477, EIA-9983445, and IIS-0070135 and by the Department of Defense. Correspondence to: D. Zhang     

Operator scheduling in data stream systems

In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams - adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the runtime system memory usage as well as output latency. Our aim is to design a scheduling strategy that minimizes the maximum runtime system memory while maintaining the output latency within prespecified bounds. We first present Chain scheduling, an operator scheduling strategy for data stream systems that is near-optimal in minimizing runtime memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams and multiple queries of the above types. However, during bursts in input streams, when there is a buildup of unprocessed tuples, Chain scheduling may lead to high output latency. We study the online problem of minimizing maximum runtime memory, subject to a constraint on maximum latency. We present preliminary observations, negative results, and heuristics for this problem. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling and its different variants, compare it with competing scheduling strategies, and validate our analytical conclusions.Received: 18 October 2003, Accepted: 16 April 2004, Published online: 14 September 2004Edited by: J. Gehrke and J. HellersteinBrian Babcock: Supported in part by a Rambus Corporation Stanford Graduate Fellowship and NSF Grant IIS-0118173.Shivnath Babu: Supported in part by NSF Grants IIS-0118173 and IIS-9817799.Mayur Datar: Supported in part by Siebel Scholarship and NSF Grant IIS-0118173.Rajeev Motwani: Supported in part by NSF Grant IIS-0118173, an Okawa Foundation Research Grant, an SNRC grant, and grants from Microsoft and Veritas.Dilys Thomas: Supported by NSF Grant EIA-0137761 and NSF ITR Award Number 0331640.     

MINDEX: An efficient index structure for salient-object-based queries in video databases

Several salient-object-based data models have been proposed to model video data. However, none of them addresses the development of an index structure to efficiently handle salient-object-based queries. There are several indexing schemes that have been proposed for spatiotemporal relationships among objects, and they are used to optimize timestamp and interval queries, which are rarely used in video databases. Moreover, these index structures are designed without consideration of the granularity levels of constraints on salient objects and the characteristics of video data. In this paper, we propose a multilevel index structure (MINDEX) to efficiently handle the salient-object-based queries with different levels of constraints. We present experimental results showing the performance of different methods of MINDEX construction.     

Selectivity estimators for multidimensional range queries over real attributes

Estimating the selectivity of multidimensional range queries over real valued attributes has significant applications in data exploration and database query optimization. In this paper, we consider the following problem: given a table of d attributes whose domain is the real numbers and a query that specifies a range in each dimension, find a good approximation of the number of records in the table that satisfy the query. The simplest approach to tackle this problem is to assume that the attributes are independent. More accurate estimators try to capture the joint data distribution of the attributes. In databases, such estimators include the construction of multidimensional histograms, random sampling, or the wavelet transform. In statistics, kernel estimation techniques are being used. Many traditional approaches assume that attribute values come from discrete, finite domains, where different values have high frequencies. However, for many novel applications (as in temporal, spatial, and multimedia databases) attribute values come from the infinite domain of real numbers. Consequently, each value appears very infrequently, a characteristic that affects the behavior and effectiveness of the estimator. Moreover, real-life data exhibit attribute correlations that also affect the estimator. We present a new histogram technique that is designed to approximate the density of multidimensional datasets with real attributes. Our technique defines buckets of variable size and allows the buckets to overlap. The size of the cells is based on the local density of the data. The use of overlapping buckets allows a more compact approximation of the data distribution. We also show how to generalize kernel density estimators and how to apply them to the multidimensional query approximation problem. Finally, we compare the accuracy of the proposed techniques with existing techniques using real and synthetic datasets. The experimental results show that the proposed techniques behave more accurately in high dimensionalities than previous approaches.Received: 30 January 2001, Accepted: 9 June 2003, Published online: 4 March 2004Edited by: Y. IoannidisDimitrios Gunopulos: Supported by NSF ITR-0220148, NSF IIS-9907477 CAREER Award, NSF IIS-9984729, and NRDRP.George Kollios: Supported by NSF IIS-0133825 CAREER Award.Vassilis J. Tsotras: Supported by NSF IIS-9907477 and the US Dept. of Defense.     

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.     

An adaptive updating protocol for reducing moving object database workload

In the last decade, spatio-temporal database research focuses on the design of effective and efficient indexing structures in support of location-based queries such as predictive range queries and nearest neighbor queries. While a variety of indexing techniques have been proposed to accelerate the processing of updates and queries, not much attention has been paid to the updating protocol, which is another important factor affecting the system performance. In this paper, we propose a generic and adaptive updating protocol for moving object databases with less number of updates between objects and the database server, thereby reducing the overall workload of the system. In contrast to the approach adopted by most conventional moving object database systems where the exact locations and velocities last disclosed are used to predict their motions, we propose the concept of Spatio-temporal safe region to approximate possible future locations. Spatio-temporal safe regions provide larger space of tolerance for moving objects, freeing them from location and velocity updates as long as the errors remain predictable in the database. To answer predictive queries accurately, the server is allowed to probe the latest status of objects when their safe regions are inadequate in returning the exact query results. Spatio-temporal safe regions are calculated and optimized by the database server with two contradictory objectives: reducing update workload while guaranteeing query accuracy and efficiency. To achieve this, we propose a cost model that estimates the composition of active and passive updates based on historical motion records and query distribution. More system performance improvements can be obtained by cutting more updates from the clients, when the users of system are comfortable with incomplete but accuracy bounded query results. We have conducted extensive experiments to evaluate our proposal on a variety of popular indexing structures. The results confirm the viability, robustness, accuracy and efficiency of our proposed protocol.     

一种基于固定网络的移动对象运动轨迹索引模型

实际应用中移动对象通常运动在城市固定道路上,针对此特征研究人员已提出一些相关索引模型,但都存在一定的局限性,表现为索引模型只管理对象的历史位置信息或实时位置信息以及只对窗口查询或轨迹查询进行优化.IMTFN是一种基于固定网络的移动对象运动轨迹索引模型,管理移动对象的实时位置信息和历史轨迹信息,并且有效优化窗口查询及轨迹查询操作.IMTFN由一个管理固定网络的2D R^＊-Tree、一组管理移动对象运动轨迹的1D R^＊-Tree以及记录移动对象实时位置信息的Hash结构组成.最后通过实验IMTFN分别与STR-Tree与FNR-Tree进行性能比较,证明IMTFN模型提供速度更快的查询操作.     

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

This paper presents the scalable on-line execution (SOLE) algorithm for continuous and on-line evaluation of concurrent continuous spatio-temporal queries over data streams. Incoming spatio-temporal data streams are processed in-memory against a set of outstanding continuous queries. The SOLE algorithm utilizes the scarce memory resource efficiently by keeping track of only the significant objects. In-memory stored objects are expired (i.e., dropped) from memory once they become insignificant. SOLE is a scalable algorithm where all the continuous outstanding queries share the same buffer pool. In addition, SOLE is presented as a spatio-temporal join between two input streams, a stream of spatio-temporal objects and a stream of spatio-temporal queries. To cope with intervals of high arrival rates of objects and/or queries, SOLE utilizes a load-shedding approach where some of the stored objects are dropped from memory. SOLE is implemented as a pipelined query operator that can be combined with traditional query operators in a query execution plan to support a wide variety of continuous queries. Performance experiments based on a real implementation of SOLE inside a prototype of a data stream management system show the scalability and efficiency of SOLE in highly dynamic environments. This work was supported in part by the National Science Foundation under Grants IIS-0093116, IIS-0209120, and 0010044-CCR.     

Incremental Processing of Continual Range Queries over Moving Objects

Efficient processing of continual range queries over moving objects is critically important in providing location-aware services and applications. A set of continual range queries, each defining the geographical region of interest, can be periodically (re)evaluated to locate moving objects that are currently within individual query boundaries. We study a new query indexing method, called CES-based indexing, for incremental processing of continual range queries over moving objects. A set of containment-encoded squares (CES) are predefined, each with a unique ID. CESs are virtual constructs (VC) used to decompose query regions and to store indirectly precomputed search results. Compared with a prior VC-based approach, the number of VCs visited in a search operation is reduced from (4L²-1)/3 to log(L)+1, where L is the maximal side length of a VC. Search time is hence significantly lowered. Moreover, containment encoding among the CESs makes it easy to identify all those VCs that need not be visited during an incremental query (re)evaluation. We study the performance of CES-based indexing and compare it with a prior VC-based approach     

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.     

Skyline index for time series data

We have developed a new indexing strategy that helps overcome the curse of dimensionality for time series data. Our proposed approach, called skyline index, adopts new skyline bounding regions (SBR) to approximate and represent a group of time series data according to their collective shape. Skyline bounding regions allow us to define a distance function that tightly lower bounds the distance between a query and a group of time series data. In an extensive performance study, we investigate the impact of different distance functions by various dimensionality reduction and indexing techniques on the performance of similarity search, including index pages accessed, data objects fetched, and overall query processing time. In addition, we show that, for k-nearest neighbor queries, the proposed skyline index approach can be coupled with the state of the art dimensionality reduction techniques such as adaptive piecewise constant approximation (APCA) and improve its performance by up to a factor of 3.     

Continuous Query Processing of Spatio-Temporal Data Streams in PLACE

The tremendous increase in the use of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. These environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous query processor includes: (1) New incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extended semantics of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) A shared-execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Experimental evaluation shows promising performance of the continuous query processor of the PLACE server. This work was supported in part by the National Science Foundation under Grants IIS-0093116, IIS-0209120, and 0010044-CCR.     

Efficient Processing of Continual Range Queries for Location-Aware Mobile Services

Efficient processing of continual range queries is important in providing location-aware mobile services. In this paper, we study a new main memory-based approach to indexing continual range queries to support location-aware mobile services. The query index is used to quickly answer the following question continually: “Which moving objects are currently located inside the boundaries of individual queries?” We present a covering tile-based (COVET) query index. A set of virtual tiles are predefined, each with a unique ID. One or more of the virtual tiles are used to strictly cover the region defined by an individual range query. The query ID is inserted into the ID lists associated with the covering tiles. These covering tiles touch each other only at the edges. A COVET index maintains a mapping between a covering tile and all the queries that contain that tile. For any object position, search is conducted indirectly via the covering tiles. More importantly, a COVET-based query index allows query evaluation to take advantage of incremental changes in object locations. Computation can be saved for those objects that have not moved outside the boundaries of covering tiles. Simulations are conducted to evaluate the effectiveness of the COVET index and compare virtual tiles of different shapes and sizes.     

Indexing animated objects using spatiotemporal access methods

We present an approach for indexing animated objects and efficiently answering queries about their position in time and space. In particular, we consider an animated movie as a spatiotemporal evolution. A movie is viewed as an ordered sequence of frames, where each frame is a 2D space occupied by the objects that appear in that frame. The queries of interest are range queries of the form, "find the objects that appear in area S between frames f_i and f_j^" as well as nearest neighbor queries such as, "find the q nearest objects to a given position A between frames f_i and f_j^". The straightforward approach to index such objects considers the frame sequence as another dimension and uses a 3D access method (such as an R-Tree or its variants). This, however, assigns long "lifetime" intervals to objects that appear through many consecutive frames. Long intervals are difficult to cluster efficiently in a 3D index. Instead, we propose to reduce the problem to a partial-persistence problem. Namely, we use a 2D access method that is made partially persistent. We show that this approach leads to faster query performance while still using storage proportional to the total number of changes in the frame evolution, What differentiates this problem from traditional temporal indexing approaches is that objects are allowed to move and/or change their extent continuously between frames. We present novel methods to approximate such object evolutions, We formulate an optimization problem for which we provide an optimal solution for the case where objects move linearly. Finally, we present an extensive experimental study of the proposed methods. While we concentrate on animated movies, our approach is general and can be applied to other spatiotemporal applications as well     

Index set: A practical indexing scheme for object database systems

Efficient indexing in a class hierarchy is essential for the achievement of high performance in query evaluation for object database management systems. In this paper, we present a practical indexing scheme, index set, which provides good index configuration for any real database environment. The proposed scheme considers the distribution of key values, as well as query patterns such as query weight on each class. The index set can easily be applied to any database system, since it uses the well-known B⁺-tree structure. We develop a cost model and, through experiments, demonstrate the performance of the proposed scheme over various class hierarchies.     

Grid-partition index: a hybrid method for nearest-neighbor queries in wireless location-based services

Traditional nearest-neighbor (NN) search is based on two basic indexing approaches: object-based indexing and solution-based indexing. The former is constructed based on the locations of data objects: using some distance heuristics on object locations. The latter is built on a precomputed solution space. Thus, NN queries can be reduced to and processed as simple point queries in this solution space. Both approaches exhibit some disadvantages, especially when employed for wireless data broadcast in mobile computing environments. In this paper, we introduce a new index method, called the grid-partition index, to support NN search in both on-demand access and periodic broadcast modes of mobile computing. The grid-partition index is constructed based on the Voronoi diagram, i.e., the solution space of NN queries. However, it has two distinctive characteristics. First, it divides the solution space into grid cells such that a query point can be efficiently mapped into a grid cell around which the nearest object is located. This significantly reduces the search space. Second, the grid-partition index stores the objects that are potential NNs of any query falling within the cell. The storage of objects, instead of the Voronoi cells, makes the grid-partition index a hybrid of the solution-based and object-based approaches. As a result, it achieves a much more compact representation than the pure solution-based approach and avoids backtracked traversals required in the typical object-based approach, thus realizing the advantages of both approaches. We develop an incremental construction algorithm to address the issue of object update. In addition, we present a cost model to approximate the search cost of different grid partitioning schemes. The performances of the grid-partition index and existing indexes are evaluated using both synthetic and real data. The results show that, overall, the grid-partition index significantly outperforms object-based indexes and solution-based indexes. Furthermore, we extend the grid-partition index to support continuous-nearest-neighbor search. Both algorithms and experimental results are presented. Edited by R. Guting     

Main Memory Evaluation of Monitoring Queries Over Moving Objects

In this paper we evaluate several in-memory algorithms for efficient and scalable processing of continuous range queries over collections of moving objects. Constant updates to the index are avoided by query indexing. No constraints are imposed on the speed or path of moving objects or fraction of objects that move at any moment in time. We present a detailed analysis of a grid approach which shows the best results for both skewed and uniform data. A sorting based optimization is developed for significantly improving the cache hit-rate. Experimental evaluation establishes that indexing queries using the grid index yields orders of magnitude better performance than other index structures such as R*-trees.