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18
A Hybrid Prediction Model for Moving Objects
- In ICDE. IEEE
, 2008
"... Abstract — Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object’s movements can be represented by some mathematical formula ..."
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Abstract — Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object’s movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object’s movements are more complicated than what the mathematical formulas can represent. Prediction based on an object’s trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object’s future locations based on its pattern information as well as existing motion functions using the object’s recent movements. Specifically, an object’s trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes. I.
Indexing moving objects using short-lived throwaway indexes
- In Proc. SSTD
, 2009
"... Abstract. 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 appli ..."
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Abstract. 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 throwaway indexes which 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 and trades this 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 like a main-memory adapted B x-tree by orders of magnitude w.r.t. update rates and query rates. Finally, our experimental evaluation uses a workload unmatched by any previous work. 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 which is unmatched by any previous work. 1
Primal or Dual: Which Promises Faster Spatiotemporal Search?
"... The existing predictive spatiotemporal indexes can be classified into two categories, depending on whether they are based on the primal or dual methodology. Although we have gained considerable empirical knowledge about various access methods, currently there is only limited understanding on the the ..."
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The existing predictive spatiotemporal indexes can be classified into two categories, depending on whether they are based on the primal or dual methodology. Although we have gained considerable empirical knowledge about various access methods, currently there is only limited understanding on the theoretical characteristics of the two methodologies. In fact, the experimental results in different papers even contradict each other, regarding the relative superiority of the primal and dual techniques. This paper presents a careful study on the query performance of general primal and dual indexes, and reveals important insight into the behavior of each technique. In particular, we mathematically establish the conditions that determine the superiority of each methodology, and provide rigorous justification for well-known observations that have not been properly explained in the literature. Our analytical findings also resolve the contradiction in the experiments of previous work. To appear in VLDB Journal.
Querying trajectories using flexible patterns
- In EDBT
, 2010
"... The wide adaptation of GPS and cellular technologies has created many applications that collect and maintain large repositories of data in the form of trajectories. Previous work on querying/analyzing trajectorial data typically falls into methods that either address spatial range and NN queries, or ..."
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Cited by 8 (4 self)
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The wide adaptation of GPS and cellular technologies has created many applications that collect and maintain large repositories of data in the form of trajectories. Previous work on querying/analyzing trajectorial data typically falls into methods that either address spatial range and NN queries, or, similarity based queries. Nevertheless, trajectories are complex objects whose behavior over time and space can be better captured as a sequence of interesting events. We thus facilitate the use of motion “pattern ” queries which allow the user to select trajectories based on specific motion patterns. Such patterns are described as regular expressions over a spatial alphabet that can be implicitly or explicitly anchored to the time domain. Moreover, we are interested in “flexible ” patterns that allow the user to include “variables” in the query pattern and thus greatly increase its expressive power. In this paper we introduce a framework for efficient processing of flexible pattern queries. The framework includes an underlying indexing structure and algorithms for query processing using different evaluation strategies. An extensive performance evaluation of this framework shows significant performance improvement when compared to existing solutions. 1.
Boosting Moving Object Indexing through Velocity Partitioning
"... There have been intense research interests in moving object indexing in the past decade. However, existing work did not exploit the important property of skewed velocity distributions. In many real world scenarios, objects travel predominantly along only a few directions. Examples include vehicles o ..."
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There have been intense research interests in moving object indexing in the past decade. However, existing work did not exploit the important property of skewed velocity distributions. In many real world scenarios, objects travel predominantly along only a few directions. Examples include vehicles on road networks, flights, people walking on the streets, etc. The search space for a query is heavily dependent on the velocity distribution of the objects grouped in the nodes of an index tree. Motivated by this observation, we propose the velocity partitioning (VP) technique, which exploits the skew in velocity distribution to speed up query processing using moving object indexes. The VP technique first identifies the “dominant velocity axes (DVAs) ” using a combination of principal components analysis (PCA) and k-means clustering. Then, a moving object index (e.g., a TPR-tree) is created based on each DVA, using
Continuous Online Index Tuning in Moving Object Databases
"... In a moving object database (MOD), the dataset, e.g., the location of objects and their distribution, and the workload change frequently. Traditional static indexes are not able to cope well with such changes, i.e., their effectiveness and efficiency are seriously affected. This calls for the develo ..."
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In a moving object database (MOD), the dataset, e.g., the location of objects and their distribution, and the workload change frequently. Traditional static indexes are not able to cope well with such changes, i.e., their effectiveness and efficiency are seriously affected. This calls for the development of novel indexes that can be reconfigured automatically based on the state of the system. In this paper, we design and present the ST 2 B-tree, a Self-Tunable Spatio-Temporal B +-Tree index for MODs. In ST 2 B-tree, the data space is partitioned into regions of different density with respect to a set of reference points. Based on the density, objects in a region are managed using a grid of appropriate granularity- intuitively, a dense region employs a grid with fine granularity, while a sparse region uses a grid with coarse granularity. In this way, the ST 2 B-tree adapts itself to workload diversity in space. To enable online tuning, the ST 2 B-tree employs a “multi-tree ” indexing technique. The underlying B +-tree is logically divided into two subtrees. Objects are dispatched to either subtree depending on their last update time. The two subtrees are rebuilt periodically and alternately. Whenever a subtree is rebuilt, it is tuned to optimize performance by picking an appropriate setting (e.g., the set of reference points and grid granularity) based on the most recent data and workload. To cut down the overhead of rebuilding, we propose
Evaluation of Range Queries with Predicates on Moving Objects
"... Abstract-A well-studied query type on moving objects is the continuous range query. An interesting and practical situation is that instead of being continuously evaluated, the query may be evaluated at different degrees of continuity, e.g. every 2 seconds (close to continuous), every 10 minutes or ..."
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Abstract-A well-studied query type on moving objects is the continuous range query. An interesting and practical situation is that instead of being continuously evaluated, the query may be evaluated at different degrees of continuity, e.g. every 2 seconds (close to continuous), every 10 minutes or at irregular time intervals (close to snapshot). Furthermore, the range query may be stacked under predicates applied to the returned objects. An example is the count predicate that requires the number of objects in the range to be at least γ. The conjecture is that these two practical considerations can help reduce communication costs. We propose a safe region-based solution that exploits these two practical considerations. An extensive experimental study shows that our solution can reduce communication costs by a factor of 9.5 compared to an existing state-of-the-art system.
Efficient, Dynamic Indexing and Aggregation of Moving Objects
, 2006
"... Average and Count-Range estimation algorithms for d dimensions based on a new spatiotemporal index. Each query runs in constant time and is based on a skew aware indexing technique with static size buckets. This allows constant time inserts, deletes and updates for highly dynamic spatiotemporal data ..."
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Average and Count-Range estimation algorithms for d dimensions based on a new spatiotemporal index. Each query runs in constant time and is based on a skew aware indexing technique with static size buckets. This allows constant time inserts, deletes and updates for highly dynamic spatiotemporal databases. The technique is also decomposable to allow partial results to be calculated simultaneously and recombined in linear time. We performed extensive experiments which show that the Max-Count estimation algorithm runs up to 35 times faster than a precise algorithm with accuracy above 95%.
The B dual-Tree: Indexing Moving Objects by Space Filling Curves
"... Abstract Existing spatiotemporal indexes suffer from either large update cost or poor query performance, except for the B x-tree (the state-of-the-art), which consists of multiple B +-trees indexing the 1D values transformed from the (multi-dimensional) moving objects based on a space filling curve ..."
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Abstract Existing spatiotemporal indexes suffer from either large update cost or poor query performance, except for the B x-tree (the state-of-the-art), which consists of multiple B +-trees indexing the 1D values transformed from the (multi-dimensional) moving objects based on a space filling curve (Hilbert, in particular). This curve, however, does not consider object velocities, and as a result, query processing with a B x-tree retrieves a large number of false hits, which seriously compromises its efficiency. It is natural to wonder “can we obtain better performance by capturing also the velocity information, using a Hilbert curve of a higher dimensionality?”. This paper provides a positive answer by developing the B dual-tree, a novel spatiotemporal access method leveraging pure relational methodology. We show, with theoretical evidence, that the B dual-tree indeed outperforms the B x-tree in most circumstances. Furthermore, our technique can effectively answer progressive spatiotemporal queries, which are poorly supported by B x-trees.
ABSTRACT Handling Frequent Updates of Moving Objects ∗
"... A critical issue in moving object databases is to develop appropriate indexing structures for continuously moving object locations so that queries can still be performed efficiently. However, such location changes typically cause a high volume of updates, which in turn poses serious problems on main ..."
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A critical issue in moving object databases is to develop appropriate indexing structures for continuously moving object locations so that queries can still be performed efficiently. However, such location changes typically cause a high volume of updates, which in turn poses serious problems on maintaining index structures. In this paper we propose a Lazy Group Update (LGU) algorithm for disk-based index structures of moving objects. LGU contains two key additional structures to group “similar ” updates so that they can be performed together: a disk-based insertion buffer (I-Buffer) for each internal node, and a memory-based deletion table (D-Table) for the entire tree. Different strategies of “pushing down ” an overflow I-Buffer to the next level are studied. Comprehensive empirical studies over uniform and skewed datasets, as well as simulated street traffic data show that LGU achieves a significant improvement on update throughput while allowing a reasonable performance for queries.
