Indexing Of Moving Objects Research Articles

Using the VENµS Super-Spectral Camera for detecting moving vehicles

Monitoring transportation for planning, management, and security purposes has become a growing interest for various stakeholders. A methodology for detecting moving vehicles is based on the acquisition time gap between the pushbroom detector sub-arrays. However, this technique requires overcoming differences in ground sampling distance and/or spectral features of the sensor’s bands used for change detection.The current work demonstrates a proof of concept for the VENµS satellite’s capability to detect moving vehicles in a single pass with a relatively low spatial resolution. The VENµS Super-Spectral Camera has a unique stereoscopic capability because the two spectral bands, with the same central wavelength and width, are positioned at the extreme ends of the camera’s focal plane. This design enables a 2.7-sec difference in observation time. The normalized difference moving object index (NDMOI) has been designed to detect moving vehicles using these bands without image preprocessing for dimensionality reduction or geometric corrections, as other sensors require.Results show the successful detection of small- to medium-sized moving vehicles. Especially interesting is the detection of private cars that are, on average, 2–3 m smaller than the VENµS ground sampling distance. Vehicle movement was effectively detected in different backgrounds/environments, e.g., on asphalt and unpaved roads, as well as over bare soil and plowed fields. Furthermore, a multitemporal analysis of moving vehicles during the Covid-19 pandemic in 2020 shows the effectiveness of the proposed methodology.

A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications

The availability of location-aware devices generates tremendous volumes of moving object trajectories. The processing of these large-scale trajectories requires innovative techniques that are capable of adapting to changes in cloud systems to satisfy a wide range of applications and non-programmer end users. We introduce a Resilient Moving Object Index that is capable of balancing both spatial and object localities to maximize the overall performance in numerous environments. It is equipped with compulsory, discrete, and impact factor prediction models. The compulsory and discrete models are used to predict a locality pivot based on three fundamental aspects: computation resources, nature of the trajectories, and query types. The impact factor model is used to predict the influence of contrasting queries. Moreover, we provide a framework to extract efficient training sets and features without adding overhead to the index construction. We conduct an extensive experimental study to evaluate our approach. The evaluation includes two testbeds and covers spatial, temporal, spatio-temporal, continuous, aggregation, and retrieval queries. In most cases, the experiments show a significant performance improvement compared to various indexing schemes on a compact trajectory dataset as well as a sparse dataset. Most important, they demonstrate how our proposed index adapts to change in various environments.

Exploiting velocity distribution skew to speed up moving object indexing

There has been intense research interest 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 in only a few directions. Examples include vehicles on road networks, flights, people walking on the street, 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 the DVA as an axis of the underlying coordinate system. The object is maintained in the index whose DVA is closest to the object׳s current moving direction. Thus, all the objects in an index are moving in a near 1-dimensional space instead of a 2-dimensional space. As a result, the expansion of the search space with time is greatly reduced, from a quadratic function of the maximum speed (of the objects in the search range) to a near linear function of the maximum speed. The VP technique can be applied to a wide range of moving object index structures. Moreover, we make use of new hardware, solid-state drives (SSDs) to further improve the performance of the VP technique. To this end, we designed a SSD friendly version of the outlier index, called the RAM-resident compressed grid (RCG). We implemented the VP technique on two representative moving object indexes, the TPR⁎-tree and the Bx-tree. Extensive experiments validate that the VP technique consistently improves the performance of these index structures.

An experimental analysis of iterated spatial joins in main memory

Many modern applications rely on high-performance processing of spatial data. Examples include location-based services, games, virtual worlds, and scientific simulations such as molecular dynamics and behavioral simulations. These applications deal with large numbers of moving objects that continuously sense their environment, and their data access can often be abstracted as a repeated spatial join. Updates to object positions are interspersed with these join operations, and batched for performance. Even for the most demanding scenarios, the data involved in these joins fits comfortably in the main memory of a cluster of machines, and most applications run completely in main memory for performance reasons. Choosing appropriate spatial join algorithms is challenging due to the large number of techniques in the literature. In this paper, we perform an extensive evaluation of repeated spatial join algorithms for distance (range) queries in main memory. Our study is unique in breadth when compared to previous work: We implement, tune, and compare ten distinct algorithms on several workloads drawn from the simulation and spatial indexing literature. We explore the design space of both index nested loops algorithms and specialized join algorithms, as well as the use of moving object indices that can be incrementally maintained. Surprisingly, we find that when queries and updates can be batched, repeatedly re-computing the join result from scratch outperforms using a moving object index in all but the most extreme cases. This suggests that--given the code complexity of index structures for moving objects -- specialized join strategies over simple index structures, such as Synchronous Traversal over R-Trees, should be the methods of choice for the above applications.

Performance Evaluation of Velocity Partitioned Moving Object Indexing in the Context of PCA and ICA

the advent of new mobile services and access feasibility, moving object indexing has grabbed a great number of new research techniques and challenges. But the data exploitation in this area is based on the straitened or symmetric data. The real world data is multi-dimensional and the objects like GPS enabled devices, flights and vehicles on road networks would be skewed in nature. Velocity of an object is of utmost importance as these objects are dynamic. Velocity Partitioning (VP) is a method to cater the need and to support the real world multi-dimensional data. This technique not only improves the query processing but also indexes the moving objects efficiently. In this paper, Velocity Partitioning technique is adopted and Independent Component Analysis (ICA) technique is used instead of Principle Component Analysis to find velocity axis. In the experimental section, different real-time and synthetic datasets are considered; Indexed the moving objects in both VP with PCA and ICA cases. Indexing structures like R*-tree and B + -trees are used for making the analysis. The performance of state-of-the-art indexing structures like B x and TPR* trees can be improved if VP is applied afore.

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 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 the DVA as an axis of the underlying coordinate system. An object is maintained in the index whose DVA is closest to the object's current moving direction. Thus, all the objects in an index are moving in a near 1-dimensional space instead of a 2-dimensional space. As a result, the expansion of the search space with time is greatly reduced, from a quadratic function of the maximum speed (of the objects in the search range) to a near linear function of the maximum speed. The VP technique can be applied to a wide range of moving object index structures. We have implemented the VP technique on two representative ones, the TPR*-tree and the B x -tree. Extensive experiments validate that the VP technique consistently improves the performance of those index structures.

Biased splitting methods of an index structure based on R*-tree for tracing RFID tag locations

In RFID systems, it is important to track and trace locations of tags attached to physical objectsin many applications such as automated manufacturing, inventory tracking and supply chain management. Queries for tracing tag locations are among the most challenging requirements in RFID based applications. For efficient query processing, trajectories of tags can be modeled and indexed as a line in a three- dimensional domain, with the axes being the tag identifier, the reader identifier, and the time. In a different way of a moving object index, the ranges of coordinates for each domain are quite different so that the distribution of query regions is biased. Since this biased distribution of query regions produces many overlaps with index nodes, many node accesses are occurred at answering queries. To reduce unnecessary overlaps, the splitting scheme of an index should be changed. Previous indexes for tag trajectories, however, do not consider the biased distribution of query regions. To solve this problem, we propose a new biased splitting method based on R*-tree. For efficient insertion of tag trajectories, our method derives the weighted margin for each node by using weights of each axis and margin of nodes. With the weighted margin, we can choose the subtree which has the minimum cost for inserting trajectories and splitting nodes. Proposed splitting method reduces the cost of region query by reducing overlapped area of query region and MBRs. We also evaluate the performance of our method and compare it with the previous methods. Our experiments show that the index based on the proposed splitting method considerably improves the performance of queries than the index based on the previous methods.

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.

Effectively indexing uncertain moving objects for predictive queries

Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modeling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive experiments on the B x -tree that offer insights into the properties of the paper's proposal.

이동객체의 현재 위치정보 관리를 위한 셀 기반 색인 기법

모바일 환경에서 정보 제공 및 처리의 대상이 되는 사람, 자동차, 비행기 둥과 같은 이동객체는 시간이 경과함에 따라 끊임없이 자신의 위치를 변경하는 특징을 가지며, 이러한 정보들을 데이터베이스에서 효과적으로 처리하기 위해서는 연속적인 위치 변경을 수용할 수 있는 저장공간과 색인 유지 및 관리 기술이 필요하다. 기존의 색인 기법들은 색인의 유지 성능보다 질의 처리 능력을 향상시키는데 노력을 기울여 왔기 때문에 복잡하게 이동하며 매우 빈번하게 위치 정보를 갱신하는 이동객체 정보를 관리하기 어렵다. 이 논문에서는 질의 처리 성능뿐만 아니라 이동객체의 빈번한 위치 갱신을 효율적으로 처리하기 위해 고정 그리드 방식의 색인과 R-Tree의 장점을 결합한 ACAR-Tree(Adaptive Cell index with Auxiliary R-Tree)를 제안한다. 제안된 ACAR-Tree는 R-Tree에서 색인의 재구성으로 인하여 갱신 성능이 저하되는 단점을 보완하기 위하여 고정 그리드 방법의 셀 기반의 색인 방법을 기초로 한다. 또한 고정 그리드 방법에서의 데이터 분포가 편중되었을 경우의 색인 성능저하 문제를 해결하기 위하여, 셀과 버켓이 1 : 1로 매핑되는 셀에서 오버플로가 발생하였을 경우 해당 버켓이 부가적인 색인 구조인 보조R-Tree로 전환하여 처리한다. 제안된 방법을 다양한 데이터 분포 및 데이터 크기에서 실험한 결과, 기존의 R-Tree 기반의 색인 방법과 비교하여 색인의 크기가 크게 감소하였으며, 질의 및 갱신 성능에 대해서도 뛰어난 성능을 보였다. In mobile environments, the locations of moving objects such as vehicles, airplanes and users of wireless devices continuously change over time. For efficiently processing moving object information, the database system should be able to deal with large volume of data, and manage indexing efficiently. However, previous research on indexing method mainly focused on query performance, and did not pay attention to update operation for moving objects. In this paper, we propose a novel moving object indexing method, named ACAR-Tree. For processing efficiently frequently updating of moving object location information as well as query performance, the proposed method is based on fixed grid structure with auxiliary R-Tree. This hybrid structure is able to overcome the poor update performance of R-Tree which is caused by reorganizing of R-Tree. Also, the proposed method is able to efficiently deal with skewed-. or gaussian distribution of data using auxiliary R-Tree. The experimental results using various data size and distribution of data show that the proposed method has reduced the size of index and improve the update and query performance compared with R-Tree indexing method.