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Journal articles on the topic 'Spatio-temporal indexing'

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Author: Grafiati

Published: 10 March 2023

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1

Li, Wu, Wu, and Zhao. "An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks." ISPRS International Journal of Geo-Information 8, no. 11 (November 12, 2019): 512. http://dx.doi.org/10.3390/ijgi8110512.

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Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.

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Feng, Bin, Qing Zhu, Mingwei Liu, Yun Li, Junxiao Zhang, Xiao Fu, Yan Zhou, Maosu Li, Huagui He, and Weijun Yang. "An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization." ISPRS International Journal of Geo-Information 7, no. 9 (September 8, 2018): 371. http://dx.doi.org/10.3390/ijgi7090371.

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Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

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Fatima, Nikhat, Ayesha Ameen, and Syed Raziuddin. "STQP: Spatio-Temporal Indexing and Query Processing." International Journal of Computer Applications 150, no. 10 (September 15, 2016): 5–9. http://dx.doi.org/10.5120/ijca2016911514.

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4

He, Zhenwen, Menno-Jan Kraak, Otto Huisman, Xiaogang Ma, and Jing Xiao. "Parallel indexing technique for spatio-temporal data." ISPRS Journal of Photogrammetry and Remote Sensing 78 (April 2013): 116–28. http://dx.doi.org/10.1016/j.isprsjprs.2013.01.014.

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5

Ni, Jinfeng, and Chinya V. Ravishankar. "Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations." IEEE Transactions on Knowledge and Data Engineering 19, no. 5 (May 2007): 663–78. http://dx.doi.org/10.1109/tkde.2007.1006.

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6

Idris, F. M., and S. Panchanathan. "Spatio-temporal indexing of vector quantized video sequences." IEEE Transactions on Circuits and Systems for Video Technology 7, no. 5 (1997): 728–40. http://dx.doi.org/10.1109/76.633489.

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7

Cho, Hyung-Ju, and Chin-Wan Chung. "Indexing range sum queries in spatio-temporal databases." Information and Software Technology 49, no. 4 (April 2007): 324–31. http://dx.doi.org/10.1016/j.infsof.2006.05.005.

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8

A, John, Sugumaran M, and Rajesh R S. "INDEXING AND QUERY PROCESSING TECHNIQUES IN SPATIO-TEMPORAL DATA." ICTACT Journal on Soft Computing 06, no. 03 (April 1, 2016): 1198–217. http://dx.doi.org/10.21917/ijsc.2016.0167.

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9

Vazirgiannis, Michael, Yannis Theodoridis, and Timos Sellis. "Spatio-temporal composition and indexing for large multimedia applications." Multimedia Systems 6, no. 4 (July 1, 1998): 284–98. http://dx.doi.org/10.1007/s005300050094.

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10

Cho, Hyung-Ju, Jun-Ki Min, and Chin-Wan Chung. "An adaptive indexing technique using spatio-temporal query workloads." Information and Software Technology 46, no. 4 (March 2004): 229–41. http://dx.doi.org/10.1016/j.infsof.2003.07.001.

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11

Chen, Liang-Hua, and Chih-Wen Su. "Video Caption Extraction Using Spatio-Temporal Slices." International Journal of Image and Graphics 18, no. 02 (April 2018): 1850009. http://dx.doi.org/10.1142/s0219467818500092.

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Captions in videos play an important role for video indexing and retrieval. In this paper, we propose a novel algorithm to extract multilingual captions from video. Our approach is based on the analysis of spatio-temporal slices of video. If the horizontal (or vertical) scan line contains some pixels of caption region then the corresponding spatio-temporal slice will have bar-code like patterns. By integrating the structure information of bar-code like patterns in horizontal and vertical slices, the spatial and temporal positions of video captions can be located accurately. Experimental results show that the proposed algorithm is effective and outperforms some existing techniques.

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12

Lee, Dong-Won, and Byoung-Woo Oh. "An Efficient Indexing Technique for Processing of Spatio-temporal Data." JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 7, no. 2 (December 31, 2017): 65–72. http://dx.doi.org/10.14801/jaitc.2017.7.2.65.

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13

Ke, Shengnan, Jun Gong, Songnian Li, Qing Zhu, Xintao Liu, and Yeting Zhang. "A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases." Sensors 14, no. 7 (July 21, 2014): 12990–3005. http://dx.doi.org/10.3390/s140712990.

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14

Rawassizadeh, Reza, Chelsea Dobbins, Mohammad Akbari, and Michael Pazzani. "Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering." Sensors 19, no. 3 (January 22, 2019): 448. http://dx.doi.org/10.3390/s19030448.

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Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events withina cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.

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15

He, Zhenwen, Chonglong Wu, Gang Liu, Zufang Zheng, and Yiping Tian. "Decomposition tree: a spatio-temporal indexing method for movement big data." Cluster Computing 18, no. 4 (August 11, 2015): 1481–92. http://dx.doi.org/10.1007/s10586-015-0475-3.

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Jun, Bong-Gi. "Design of Spatio-temporal Indexing for searching location of RFID Objects." Journal of the Korea Society of Computer and Information 19, no. 5 (May 31, 2014): 71–78. http://dx.doi.org/10.9708/jksci.2014.19.5.071.

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17

Zhan, Shaobin, and Yongsheng Liang. "On Researching the Vehicle Monitoring Platform Based on Spatio-Temporal Indexing Mechanism." International Journal of Online Engineering (iJOE) 9, S7 (October 22, 2013): 34. http://dx.doi.org/10.3991/ijoe.v9is7.3191.

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18

Zolotov, V. A., and V. A. Semenov. "Effective spatio-temporal indexing methods for visual modeling of large industrial projects." Proceedings of the Institute for System Programming of RAS 26, no. 2 (2014): 175–96. http://dx.doi.org/10.15514/ispras-2014-26(2)-8.

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19

Vasistha, Prachi, and Rajiv Ganguly. "Assessment of spatio-temporal variations in lake water body using indexing method." Environmental Science and Pollution Research 27, no. 33 (July 22, 2020): 41856–75. http://dx.doi.org/10.1007/s11356-020-10109-3.

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20

Ma, Z. T., C. M. Li, Z. Wu, and P. D. Wu. "RESEARCH AND PRACTICE ON SPATIO-TEMPORAL BIG DATA CLOUD PLATFORM OF THE BELT AND ROAD INITIATIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 389–96. http://dx.doi.org/10.5194/isprs-archives-xlii-4-389-2018.

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<p><strong>Abstract.</strong> Spatio-temporal big data cloud platform is an important spatial information infrastructure that can provide different period spatial information data services, various spatial analysis services and flexible API services. Activities of policy coordination, facilities connectivity and unimpeded trade on the Belt and Road Initiative (B&R) will create huge demands to the spatial information infrastructure. This paper focuses on researching a distributed spatio-temporal big data engine and an extendable cloud platform framework suits for the B&R and some key technologies to implement them. A distributed spatio-temporal big data engine based on Cassandra&trade; and an extendable 4-tier architecture cloud platform framework is put forward according to the spirit of parallel computing and cloud service. Four key technologies are discussed: 1) a storage and indexing method for distributed spatio-temporal big data, 2) an automatically collecting, processing, mapping and updating method of authoritative spatio-temporal data for web mapping service, 3) a schema of services aggregation based on nodes registering and services invoking based on view extension, 4) a distributed deployment and extension method of the cloud platform. We developed a distributed spatio-temporal big data centersoftware and founded the main node platform portal with MapWorld&trade; map services and some thematic information services inChina and built some local platform portals for those countries in the B&R area. The management and analysis services for spatio-temporal big data were built in flexible styles on this platform. Practices show that we provide a flexible and efficient solution tobuild the distributed spatio-temporal big data center and cloud platform, more node portals can be aggregated to the main portal bypublishing their own web services and registering them in the aggregation schema. The data center and platform can support thestorage and management of massive data well and has higher fault tolerance and better scalability.</p>

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Pelekis, Nikos, Elias Frentzos, Nikos Giatrakos, and Yannis Theodoridis. "HERMES." International Journal of Knowledge-Based Organizations 5, no. 2 (April 2015): 19–41. http://dx.doi.org/10.4018/ijkbo.2015040102.

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This paper presents HERMES, a prototype DB engine that defines a powerful query language for trajectory databases, which enables the support of mobility-centric applications, such as Location-Based Services (LBS). HERMES extends the data definition and manipulation language of Object-Relational DBMS (ORDBMS) with spatio-temporal semantics and functionality based on advanced spatio-temporal indexing and query processing techniques. Its implementation over two ORDBMS and its utilization in various domains proves the expressive power and applicability of HERMES in different application domains where knowledge regarding mobility data is essential. As a proof-of-concept, in this paper HERMES is applied to a case study related with vehicle traffic analysis, demonstrating its flexibility and usefulness for delivering custom-defined LBS.

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22

Chen, Xiaoying, Chong Zhang, Bin Ge, and Weidong Xiao. "Efficient Historical Query in HBase for Spatio-Temporal Decision Support." International Journal of Computers Communications & Control 11, no. 5 (August 31, 2016): 613. http://dx.doi.org/10.15837/ijccc.2016.5.2611.

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Comparing to last decade, technologies to gather spatio-temporal data are more and more developed and easy to use or deploy, thus tens of billions, even trillions of sensed data are accumulated, which poses a challenge to spatio-temporal Decision Support System (stDSS). Traditional database hardly supports such huge volume, and tends to bring performance bottleneck to the analysis platform. Hence in this paper, we argue to use NoSQL database, HBase, to replace traditional back-end storage system. Under such context, the well-studied spatio-temporal querying techniques in traditional database should be shifted to HBase system parallel. However, this problem is not solved well in HBase, as many previous works tackle the problem only by designing schema, i.e., designing row key and column key formation for HBase, which we don’t believe is an effective solution. In this paper, we address this problem from nature level of HBase, and propose an index structure as a built-in component for HBase. STEHIX (Spatio-TEmporal Hbase IndeX) is adapted to two-level architecture of HBase and suitable for HBase to process spatio-temporal queries. It is composed of index in the meta table (the first level) and region index (the second level) for indexing inner structure of HBase regions. Base on this structure, three queries, range query, kNN query and GNN query are solved by proposing algorithms, respectively. For achieving load balancing and scalable kNN query, two optimizations are also presented. We implement STEHIX and conduct experiments on real dataset, and the results show our design outperforms a previous work in many aspects.

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23

Sathya, S., and P. Sugumaran. "Survey of Indexing Techniques on Moving Objects in Spatio Temporal Networks for PPF." International Journal of Communication and Networking System 5, no. 1 (June 15, 2015): 43–46. http://dx.doi.org/10.20894/ijcnes.103.005.001.012.

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24

Gorawski, Marcin, and Adam Dyga. "Indexing of Spatio-Temporal Telemetric Data Based on Adaptive Multi-Dimensional Bucket Index." Fundamenta Informaticae 90, no. 1-2 (2009): 73–86. http://dx.doi.org/10.3233/fi-2009-0006.

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25

Ayeelyan, John, Sugumarn Muthukumarasamy, and Rengan Rajesh. "DTNH Indexing Method: Past Present and Future Data Prediction for Spatio-Temporal Data." International Journal of Intelligent Engineering and Systems 10, no. 3 (June 30, 2017): 426–34. http://dx.doi.org/10.22266/ijies2017.0630.48.

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26

Chiu, Chih-Yi, Hsin-Min Wang, and Chu-Song Chen. "Fast min-hashing indexing and robust spatio-temporal matching for detecting video copies." ACM Transactions on Multimedia Computing, Communications, and Applications 6, no. 2 (March 2010): 1–23. http://dx.doi.org/10.1145/1671962.1671966.

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27

Shu-Ching Chen, Mei-Ling Shyu, S. Peeta, and Chengcui Zhang. "Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems." IEEE Transactions on Intelligent Transportation Systems 4, no. 3 (September 2003): 154–67. http://dx.doi.org/10.1109/tits.2003.821290.

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28

Botea, Viorica, Daniel Mallett, Mario A. Nascimento, and Jörg Sander. "PIST: An Efficient and Practical Indexing Technique for Historical Spatio-Temporal Point Data." GeoInformatica 12, no. 2 (August 17, 2007): 143–68. http://dx.doi.org/10.1007/s10707-007-0030-3.

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29

Kakkar, D., and B. Lewis. "BUILDING A BILLION SPATIO-TEMPORAL OBJECT SEARCH AND VISUALIZATION PLATFORM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 97–100. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-97-2017.

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With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a prototype spatio-temporal visualization platform called the Billion Object Platform or BOP. The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. The BOP is now loaded with the latest billion geo-tweets, and is fed a real-time stream of about 1 million tweets per day. The geo-tweets are enriched with sentiment and census/admin boundary codes when they enter the system. The system is open source and is currently hosted on Massachusetts Open Cloud (MOC), an OpenStack environment with all components deployed in Docker orchestrated by Kontena. This paper will provide an overview of the BOP architecture, which is built on an open source stack consisting of Apache Lucene, Solr, Kafka, Zookeeper, Swagger, scikit-learn, OpenLayers, and AngularJS. The paper will further discuss the approach used for harvesting, enriching, streaming, storing, indexing, visualizing and querying a billion streaming geo-tweets.

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30

Richly, Keven, Rainer Schlosser, and Martin Boissier. "Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications." Proceedings of the VLDB Endowment 15, no. 13 (September 2022): 4079–92. http://dx.doi.org/10.14778/3565838.3565858.

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Based on the performance requirements of modern spatio-temporal data mining applications, in-memory database systems are often used to store and process the data. To efficiently utilize the scarce DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes). However, the selection of cost and performance balancing configurations is challenging due to the vast number of possible setups consisting of mutually dependent individual decisions. In this paper, we introduce a novel approach to jointly optimize the compression, sorting, indexing, and tiering configuration for spatio-temporal workloads. Further, we consider horizontal data partitioning, which enables the independent application of different tuning options on a fine-grained level. We propose different linear programming (LP) models addressing cost dependencies at different levels of accuracy to compute optimized tuning configurations for a given workload and memory budgets. To yield maintainable and robust configurations, we extend our LP-based approach to incorporate reconfiguration costs as well as a worst-case optimization for potential workload scenarios. Further, we demonstrate on a real-world dataset that our models allow to significantly reduce the memory footprint with equal performance or increase the performance with equal memory size compared to existing tuning heuristics.

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31

Kim, Daehoon, Seungmin Rho, Sanghoon Jun, and Eenjun Hwang. "Classification and indexing scheme of large-scale image repository for spatio-temporal landmark recognition." Integrated Computer-Aided Engineering 22, no. 2 (February 1, 2015): 201–13. http://dx.doi.org/10.3233/ica-140478.

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32

Kaneko, Hiroyuki, and Toshihiro Osaragi. "Extraction of The Spatio-temporal Activity Patterns Using Laser-scanner Trajectory Data." AGILE: GIScience Series 1 (July 15, 2020): 1–20. http://dx.doi.org/10.5194/agile-giss-1-9-2020.

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Abstract. A pedestrian tracking system on highly accurate laser scanners is an effective method to understand the usage of the facility space. While this system is capable of gathering an enormous volume of tracking data, specialized skills and significant amounts of labor are needed to get a reliable bird’s-eye view of the spatio-temporal characteristics of the observed data. In this paper, two methods to extract patterns of spatio-temporal activity are described. These can provide a broad overview of the office-worker’s activities in the office throughout a workday and an easily under-stood visualization that indicates what time segment, what location and what activities are taking place. One is a time segment extraction model that identifies characteristic time intervals in the time series data of office-worker’s activities using a classification model based on information loss minimization model. The other is a day scene extraction model that identifies daily scenes from simultaneous behavior patterns in spatio-temporal distributions using a latent class model with PLSI (Probabilistic latent semantic indexing). These methods provide viewpoints for separating their activities of a workday into time segments of appropriate size in order to obtain a grasp of how the activities vary with the time of day. Simultaneous behavior patterns in time, space, and activity are extracted, thereby allowing representation of typical scenes such as morning meetings and extended conversations between co-workers.

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33

Chu, Hawon, Young-Kyoon Suh, Ryong Lee, Minwoo Park, Rae-Young Jang, Sang-Hwan Lee, and Sa-Kwang Song. "A Trie-based Indexing Scheme for Efficient Retrieval of Massive Spatio-Temporal IoT Sensor Data." Journal of KIISE 47, no. 12 (December 31, 2020): 1199–207. http://dx.doi.org/10.5626/jok.2020.47.12.1199.

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34

Halaoui, H. F. "A spatio‐temporal indexing structure for efficient retrieval and manipulation of discretely changing spatial data." Journal of Spatial Science 53, no. 2 (December 2008): 1–12. http://dx.doi.org/10.1080/14498596.2008.9635146.

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35

CHEN, SHU-CHING, MEI-LING SHYU, CHENGCUI ZHANG, and R. L. KASHYAP. "IDENTIFYING OVERLAPPED OBJECTS FOR VIDEO INDEXING AND MODELING IN MULTIMEDIA DATABASE SYSTEMS." International Journal on Artificial Intelligence Tools 10, no. 04 (December 2001): 715–34. http://dx.doi.org/10.1142/s0218213001000738.

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The identification of the overlapped objects is a great challenge in object tracking and video data indexing. For this purpose, a backtrack-chain-updation split algorithm is proposed to assist an unsupervised video segmentation method called the "simultaneous partition and class parameter estimation" (SPCPE) algorithm to identify the overlapped objects in the video sequence. The backtrack-chain-updation split algorithm can identify the split segment (object) and use the information in the current frame to update the previous frames in a backtrack-chain manner. The split algorithm provides more accurate temporal and spatial information of the semantic objects so that the semantic objects can be indexed and modeled by multimedia input strings and the multimedia augmented transition network (MATN) model. The MATN model is based on the ATN model that has been used in artificial intelligence (AI) areas for natural language understanding systems, and its inputs are modeled by the multimedia input strings. In this paper, we will show that the SPCPE algorithm together with the backtrack-chain-updation split algorithm can significantly enhance the efficiency of spatio-temporal video indexing by improving the accuracy of multimedia database queries related to semantic objects.

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Kvet, Michal, Emil Kršák, and Karol Matiaško. "Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture." Applied Sciences 11, no. 3 (January 20, 2021): 916. http://dx.doi.org/10.3390/app11030916.

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Current intelligent information systems require complex database approaches managing and monitoring data in a spatio-temporal manner. Many times, the core of the temporal system element is created on the relational platform. In this paper, a summary of the temporal architectures with regards to the granularity level is proposed. Object, attribute, and synchronization group perspectives are discussed. An extension of the group temporal architecture shifting the processing in the spatio-temporal level synchronization is proposed. A data reflection model is proposed to cover the transaction integrity with reflection to the data model evolving over time. It is supervised by our own Extended Temporal Log Ahead Rule, evaluating not only collisions themselves, but the data model is reflected, as well. The main emphasis is on the data retrieval process and indexing with regards to the non-reliable data. Undefined value categorization supervised by the NULL_representation data dictionary object and memory pointer layer is provided. Therefore, undefined (NULL) values can be part of the index structure. The definition and selection of the technology of the master index is proposed and discussed. It allows the index to be used as a way to identify blocks with relevant data, which is of practical importance in temporal systems where data fragmentation often occurs. The last part deals with the syntax of the Select statement extension covering temporal environment with regards on the conventional syntax reflection. Event_definition, spatial_positions, model_reflection, consistency_model, epsilon_definition, monitored_data_set, type_of_granularity, and NULL_category clauses are introduced. Impact on the performance of the data manipulation operations is evaluated in the performance section highlighting temporal architectures, Insert, Update and Select statements forming core performance characteristics.

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37

Zheng, Yan Ling. "Research on Modeling and Indexing of Trajectories of Moving Objects in Road Networks." Advanced Materials Research 756-759 (September 2013): 1234–39. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1234.

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Proposed a new index structure, named MG2R*, can efficiently store and retrieve the past, present and future positions of network-constrained moving objects. It is a two-tier structure. The upper is a MultiGrid-R*-Tree (MGRT for short) that is used to index the road network. The lower is a group of independent R*-Tree. Each R*-Tree is relative to a route in the road network, can index the spatiotemporal trajectory of the moving objects in the road. Moreover, moving objects query is implemented based on this index structure. It compared to other index structures for road-network-based moving objects, such as MON-Tree, the experimental results shown that the MG2R* can effectively improve the query performance of the spatio-temporal trajectory of network-constrained moving objects.

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Vo, A. V., N. Chauhan, D. F. Laefer, and M. Bertolotto. "A 6-DIMENSIONAL HILBERT APPROACH TO INDEX FULL WAVEFORM LiDAR DATA IN A DISTRIBUTED COMPUTING ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 671–78. http://dx.doi.org/10.5194/isprs-archives-xlii-4-671-2018.

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<p><strong>Abstract.</strong> Laser scanning data are increasingly available across the globe. To maximize the data's usability requires proper storage and indexing. While significant research has been invested in developing storage and indexing solutions for laser scanning point clouds (i.e. using the discrete form of the data), little attention has been paid to developing equivalent solutions for full waveform (FWF) laser scanning data, especially in a distributed computing environment. Given the growing availability of FWF sensors and datasets, FWF data management solutions are increasingly needed. This paper presents an attempt towards establishing a scalable solution for handling large FWF datasets by introducing the distributed computing solution for FWF data. The work involves a FWF database built atop HBase &ndash; the distributed database system running on Hadoop commodity clusters. By combining a 6-dimensional (6D) Hilbert spatial code and a temporal index into a compound indexing key, the database system is capable of supporting multiple spatial, temporal, and spatio-temporal queries. Such queries are important for FWF data exploration and dissemination. The proposed spatial decomposition at a fine resolution of 0.05<span class="thinspace"></span>m allows the storage of each LiDAR FWF measurement (i.e. pulse, waves, and points) on a single row of the database, thereby providing the full capabilities to add, modify, and remove each measurement record anatomically. While the feasibility and capabilities of the 6D Hilbert solution are evident, the Hilbert decomposition is not due to the complications from the combination of the data’s high dimensionality, fine resolution, and large spatial extent. These factors lead to a complex set of both attractive attributes and limitation in the proposed solution, which are described in this paper based on experimental tests using a 1.1 billion pulse LiDAR scan of a portion of Dublin, Ireland.</p>

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39

Ray, Suprio, and Bradford Nickerson. "Temporally relevant parallel top-k spatial keyword search." Journal of Spatial Information Science, no. 24 (June 20, 2022): 115–56. http://dx.doi.org/10.5311/josis.2022.24.199.

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New spatio-textual indexing methods are needed to support efficient search and update of the massive amounts of spatially referenced text being generated. Location based services using geo-tagged documents provide valuable ranked recommendations about nearby restaurants, services, sales, emergency events, and visitor attractions. Consequently, top-k spatial keyword search queries (TkSKQ) have received a lot of attention from the research community. Several spatio-textual indexes have been proposed to efficiently support TkSKQ. Some of these indexes support updates based on live document streams, but the ranking schemes employed by them do not simultaneously incorporate temporal relevance, textual similarity and spatial proximity. Moreover, existing approaches have limited or no capability to exploit parallelism with document ingestion and query execution. We present a parallel spatio-textual index, Pastri, to address the aforementioned issues. Pastri can be updated incrementally over real-time spatio-textual document streams. To support temporally relevant ranking of continuously generated document streams, we propose a dynamic ranking scheme. Our approach retrieves the top-k documents that are most temporally relevant at the time of a query execution. We implemented Pastri and we integrate it within a system with a persistent document store and several thread pools to exploit parallelism at various levels. Experimental evaluation involving real-world datasets and synthetic datasets (that we created) demonstrates that our system is able to sustain high document update throughput. Furthermore, Pastri's TkSKQ search performance is one to two orders of magnitude faster than other spatio-textual indexes.

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Doulkeridis, Christos, Akrivi Vlachou, Nikos Pelekis, and Yannis Theodoridis. "A Survey on Big Data Processing Frameworks for Mobility Analytics." ACM SIGMOD Record 50, no. 2 (August 24, 2021): 18–29. http://dx.doi.org/10.1145/3484622.3484626.

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In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.

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Zhu, Lilu, Xiaolu Su, and Xianqing Tai. "A High-Dimensional Indexing Model for Multi-Source Remote Sensing Big Data." Remote Sensing 13, no. 7 (March 30, 2021): 1314. http://dx.doi.org/10.3390/rs13071314.

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With continuous improvement of earth observation technology, source, and volume of remote sensing data are gradually enriched. It is critical to realize unified organization and to form data sharing service capabilities for massive remote sensing data effectively. We design a hierarchical multi-dimensional hybrid indexing model (HMDH), to address the problems in underlying organization and management, and improve query efficiency. Firstly, we establish remote sensing data grid as the smallest unit carrying and processing spatio-temporal information. We implement the construction of the HMDH in two steps, data classification based on fuzzy clustering algorithm, and classification optimization based on recursive neighborhood search algorithm. Then, we construct a hierarchical “cube” structure, filled with continuous space filling curves, to complete the coding of the HMDH. The HMDH reduces the amount of data to 6–17% and improves the accuracy to more than eight times than traditional grid model. Moreover, it can reduce the query time to 25% in some query scenarios than algorithms selected as the baseline in this paper. The HMDH model proposed can be used to solve the efficiency problems of fast and joint retrieval of remote sensing data. It extends the pattens of data sharing service and has a high application value.

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Dritsas, Elias, Andreas Kanavos, Maria Trigka, Gerasimos Vonitsanos, Spyros Sioutas, and Athanasios Tsakalidis. "Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark." Algorithms 13, no. 8 (July 28, 2020): 182. http://dx.doi.org/10.3390/a13080182.

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Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed to prevent data re-identifcation and concerns us in the context of this work. Data modeling has also gained significant attention from the big data perspective. It is believed that the advancing distributed environments will provide users with several solutions for efficient spatio-temporal data management. GeoSpark will be utilized in the current work as it is a key solution that has been widely employed for spatial data. Specifically, it works on the top of Apache Spark, the main framework leveraged from the research community and organizations for big data transformation, processing and visualization. To this end, we focused on trajectory data representation so as to be applicable to the GeoSpark environment, and a GeoSpark-based approach is designed for the efficient management of real spatio-temporal data. Th next step is to gain deeper understanding of the data through the application of k nearest neighbor (k-NN) queries either using indexing methods or otherwise. The k-anonymity set computation, which is the main component for privacy preservation evaluation and the main issue of our previous works, is evaluated in the GeoSpark environment. More to the point, the focus here is on the time cost of k-anonymity set computation along with vulnerability measurement. The extracted results are presented into tables and figures for visual inspection.

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Sim, Chun-Bo, and Jae-U. Jang. "A Signature-based Video Indexing Scheme using Spatio-Temporal Modeling for Content-based and Concept-based Retrieval on Moving Objects." KIPS Transactions:PartD 9D, no. 1 (February 1, 2002): 31–42. http://dx.doi.org/10.3745/kipstd.2002.9d.1.031.

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Alqahtani, Omar, and Tom Altman. "A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications." Applied Sciences 10, no. 20 (October 16, 2020): 7220. http://dx.doi.org/10.3390/app10207220.

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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.

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Weir, Michael K., and Li Hui Chen. "Extending Learning Feasibility Through Feedforward Sequential Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 6 (December 20, 1998): 228–33. http://dx.doi.org/10.20965/jaciii.1998.p0228.

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In this paper, a sequence-based neural network approach called feedforward sequential learning (FSL) is proposed for extending the range of feasibility for feedforward networks in the three areas of architecture, training, and generalization. The extension is enabled through a spatio-temporal indexing scheme that decomposes the task into a sequence of simpler subproblems. Each subproblem is then solved by a separate weight state. The separate trained weight states are then combined into a continuous final weight state sequence to enable smooth generalization. FSL can be used to train mappings of analog or discrete I/O with underlying continuity for pattern association or classification. Implementation of FSL is illustrated and tested by learning the 2-spirals problem and an extended 4-spiral version. Training is found to be faster and more robust than its single-state counterpart. The generalization obtained indicates that the underlying patterns are classified more smoothly with FSL. Overall, the results suggest FSL to be a feasible approach to consider for complex and decomposable tasks.

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Ribeiro de Almeida, Damião, Cláudio de Souza Baptista, Fabio Gomes de Andrade, and Amilcar Soares. "A Survey on Big Data for Trajectory Analytics." ISPRS International Journal of Geo-Information 9, no. 2 (February 1, 2020): 88. http://dx.doi.org/10.3390/ijgi9020088.

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Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.

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Budikova, Petra, Jan Sedmidubsky, Jan Horvath, and Pavel Zezula. "Efficient Retrieval of Human Motion Episodes Based on Indexed Motion-Word Representations." International Journal of Semantic Computing 15, no. 02 (June 2021): 189–213. http://dx.doi.org/10.1142/s1793351x21400031.

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With the increasing availability of human motion data captured in the form of 2D or 3D skeleton sequences, more complex motion recordings need to be processed. In this paper, we focus on similarity-based indexing and efficient retrieval of motion episodes — medium-sized skeleton sequences that consist of multiple semantic actions and correspond to some logical motion unit (e.g. a figure skating performance). As a first step toward efficient retrieval, we apply the motion-word technique to transform spatio-temporal skeleton sequences into compact text-like documents. Based on these documents, we introduce a two-phase retrieval scheme that first finds a set of candidate query results and then re-ranks these candidates with more expensive application-specific methods. We further index the motion-word documents using inverted files, which allows us to retrieve the candidate documents in an efficient and scalable manner. We also propose additional query-reduction techniques that accelerate both the retrieval phases by removing semantically irrelevant parts of the motion query. Experimental evaluation is used to analyze the effects of the individual proposed techniques on the retrieval efficiency and effectiveness.

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McFerren, G., and T. van Zyl. "GEOSPATIAL DATA STREAM PROCESSING IN PYTHON USING FOSS4G COMPONENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 931–37. http://dx.doi.org/10.5194/isprs-archives-xli-b7-931-2016.

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One viewpoint of current and future IT systems holds that there is an increase in the scale and velocity at which data are acquired and analysed from heterogeneous, dynamic sources. In the earth observation and geoinformatics domains, this process is driven by the increase in number and types of devices that report location and the proliferation of assorted sensors, from satellite constellations to oceanic buoy arrays. Much of these data will be encountered as self-contained messages on data streams - continuous, infinite flows of data. Spatial analytics over data streams concerns the search for spatial and spatio-temporal relationships within and amongst data “on the move”. In spatial databases, queries can assess a store of data to unpack spatial relationships; this is not the case on streams, where spatial relationships need to be established with the incomplete data available. Methods for spatially-based indexing, filtering, joining and transforming of streaming data need to be established and implemented in software components. This article describes the usage patterns and performance metrics of a number of well known FOSS4G Python software libraries within the data stream processing paradigm. In particular, we consider the RTree library for spatial indexing, the Shapely library for geometric processing and transformation and the PyProj library for projection and geodesic calculations over streams of geospatial data. We introduce a message oriented Python-based geospatial data streaming framework called Swordfish, which provides data stream processing primitives, functions, transports and a common data model for describing messages, based on the Open Geospatial Consortium Observations and Measurements (O&M) and Unidata Common Data Model (CDM) standards. We illustrate how the geospatial software components are integrated with the Swordfish framework. Furthermore, we describe the tight temporal constraints under which geospatial functionality can be invoked when processing high velocity, potentially infinite geospatial data streams. The article discusses the performance of these libraries under simulated streaming loads (size, complexity and volume of messages) and how they can be deployed and utilised with Swordfish under real load scenarios, illustrated by a set of Vessel Automatic Identification System (AIS) use cases. We conclude that the described software libraries are able to perform adequately under geospatial data stream processing scenarios - many real application use cases will be handled sufficiently by the software.

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Liu, Haicheng, Peter van Oosterom, Theo Tijssen, Tom Commandeur, and Wen Wang. "Managing large multidimensional hydrologic datasets: A case study comparing NetCDF and SciDB." Journal of Hydroinformatics 20, no. 5 (May 10, 2018): 1058–70. http://dx.doi.org/10.2166/hydro.2018.136.

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Abstract Management of large hydrologic datasets including storage, structuring, clustering, indexing, and query is one of the crucial challenges in the era of big data. This research originates from a specific problem: time series extraction at specific locations takes a long time when a large multidimensional (MD) dataset is stored in the NetCDF classic or the 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file-based solutions and a MD array database management system applying a chunked storage structure are benchmarked to determine the best solution for storing and querying large MD hydrologic datasets. Expert consultancy was conducted to establish benchmark sets, with the HydroNET-4 system being utilized to provide the benchmark environment. In the final benchmark tests, the effect of data storage configurations, elaborating chunk size, dimension order (spatio-temporal clustering) and compression on the query performance, is explored. Results indicate that for big hydrologic MD data management, the properly chunked NetCDF-4 solution without compression is, in general, more efficient than the SciDB DBMS. However, benefits of a DBMS should not be neglected, for example, the integration with other data types, smart caching strategies, transaction support, scalability, and out-of-the-box support for parallelization.

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Vo, A. V., D. F. Laefer, M. Trifkovic, C. N. L. Hewage, M. Bertolotto, N. A. Le-Khac, and U. Ofterdinger. "A HIGHLY SCALABLE DATA MANAGEMENT SYSTEM FOR POINT CLOUD AND FULL WAVEFORM LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 507–12. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-507-2020.

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Abstract. The massive amounts of spatio-temporal information often present in LiDAR data sets make their storage, processing, and visualisation computationally demanding. There is an increasing need for systems and tools that support all the spatial and temporal components and the three-dimensional nature of these datasets for effortless retrieval and visualisation. In response to these needs, this paper presents a scalable, distributed database system that is designed explicitly for retrieving and viewing large LiDAR datasets on the web. The ultimate goal of the system is to provide rapid and convenient access to a large repository of LiDAR data hosted in a distributed computing platform. The system is composed of multiple, share-nothing nodes operating in parallel. Namely, each node is autonomous and has a dedicated set of processors and memory. The nodes communicate with each other via an interconnected network. The data management system presented in this paper is implemented based on Apache HBase, a distributed key-value datastore within the Hadoop eco-system. HBase is extended with new data encoding and indexing mechanisms to accommodate both the point cloud and the full waveform components of LiDAR data. The data can be consumed by any desktop or web application that communicates with the data repository using the HTTP protocol. The communication is enabled by a web servlet. In addition to the command line tool used for administration tasks, two web applications are presented to illustrate the types of user-facing applications that can be coupled with the data system.

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