An Indexing Method of Continuous Spatiotemporal Queries for Stream Data Processing Rules of Detected Target Objects

Real-time performance is important in rule-based continuous spatiotemporal query processing for risk analysis and decision making of target objects collected by sensors of combat vessels. The existing Rete algorithm, which creates a compiled node link structure for executing rules, is known to be the best. However, when a large number of rules are to be processed and the stream data to be performed are large, the Rete technique has an overhead of searching for rules to be bound. This paper proposes a hashing indexing technique for Rete nodes to the overhead of searching for spatiotemporal condition rules that must be bound when rules are expressed in a node link structure. A performance comparison evaluation experiment was conducted with Drool, which implemented the Rete method, and the method that implemented the hash index method presented in this paper. For performance measurement, processing time was measured for the change in the number of rules, the change in the number of objects, and the distribution of objects. The hash index method presented in this paper improved performance by at least 18% compared to Drool.

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

Spatiotemporal data has always been big data. In these days, big data analytics for spatiotemporal data is receiving considerable attention to allow users to analyze huge amounts of data. Traditional big data platforms cannot handle all the challenges of processing spatio-temporal data. Although some big data platforms have been proposed to process a massive volume of spatiotemporal data, neither is considered a clear winner for all possible scenarios. This paper presents the SmarT query engine, a machine learning-based solution that chooses the best big data platform for processing spatiotemporal queries on the fly. In a detailed experimental evaluation, considering the Apache Spark, Elasticsearch, and SciDB big data platforms, the response time decreased up to 22% when using SmarT.

Queries Processing in Wireless Sensor Network

For the super-excellence applications used to control the water level in rivers, temperature handles a very large volume of information and does not stop constantly changing. These spatio-temporal data collected by a network of sensors form a set of thematic, integrated, non-volatile and historical data organized to help decision-making. Usually this process is performed with temporal, spatial and spatiotemporal queries. This in turn increases the execution time of the query load. In the literatures, several techniques have been identified such as materialized views (MV), indexes, fragmentation, scheduling, and buffer management. These techniques do not consider the update of the request load and the modification at the database level. In this chapter, we propose an optimal dynamic selection solution based on indexes and VMs. the solution is optimal when it meets the entire workload with a reasonable response time. The proposed approach supports modification at the database level and at the workload level to ensure the validity of the optimal solution for this the knapsack algorithm was used.

Incorporating Spatial Queries into Semantic Sensor Streams on the Internet of Things

In the Internet of Things (IoT) environment, the use of sensors and sensor readings is significant in research and industry. The number of sensors is increasing exponentially, adding a tremendous amount of data to the Web. Therefore, the efficient management of sensors and observation data is becoming important. Especially, the location and time of observations are expected to play a vital role in IoT. However, existing researches mainly focus on the temporal properties of data stream. It is necessary to consider the spatial features in addition to the temporal ones. In this article, the authors propose a spatiotemporal query language which integrates spatial and temporal features. Also, they propose an efficient method of building a spatiotemporal index and processing the proposed query language. To evaluate the proposed method, the authors conduct experiments through implementation. The experimental results show that the proposed method deals with spatiotemporal queries within a reasonable time.

Pruning Optimization over Threshold-Based Historical Continuous Query

With the increase in mobile location service applications, spatiotemporal queries over the trajectory data of moving objects have become a research hotspot, and continuous query is one of the key types of various spatiotemporal queries. In this paper, we study the sub-domain of the continuous query of moving objects, namely the pruning optimization over historical continuous query based on threshold. Firstly, for the problem that the processing cost of the Mindist-based pruning strategy is too large, a pruning strategy based on extended Minimum Bounding Rectangle overlap is proposed to optimize the processing overhead. Secondly, a best-first traversal algorithm based on E3DR-tree is proposed to ensure that an accurate pruning candidate set can be obtained with accessing as few index nodes as possible. Finally, experiments on real data sets prove that our method significantly outperforms other similar methods.

THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management

The rapid development of distributed technology has made it possible to store and query massive trajectory data. As a result, a variety of schemes for big trajectory data management have been proposed. However, the factor of data transmission is not considered in most of these, resulting in a certain impact on query efficiency. In view of that, we present THBase, a coprocessor-based scheme for big trajectory data management in HBase. THBase introduces a segment-based data model and a moving-object-based partition model to solve massive trajectory data storage, and exploits a hybrid local secondary index structure based on Observer coprocessor to accelerate spatiotemporal queries. Furthermore, it adopts certain maintenance strategies to ensure the colocation of relevant data. Based on these, THBase designs node-locality-based parallel query algorithms by Endpoint coprocessor to reduce the overhead caused by data transmission, thus ensuring efficient query performance. Experiments on datasets of ship trajectory show that our schemes can significantly outperform other schemes.

Incorporating Spatial Queries into Semantic Sensor Streams on the Internet of Things

In the Internet of Things (IoT) environment, the use of sensors and sensor readings is significant in research and industry. The number of sensors is increasing exponentially, adding a tremendous amount of data to the Web. Therefore, the efficient management of sensors and observation data is becoming important. Especially, the location and time of observations are expected to play a vital role in IoT. However, existing researches mainly focus on the temporal properties of data stream. It is necessary to consider the spatial features in addition to the temporal ones. In this article, the authors propose a spatiotemporal query language which integrates spatial and temporal features. Also, they propose an efficient method of building a spatiotemporal index and processing the proposed query language. To evaluate the proposed method, the authors conduct experiments through implementation. The experimental results show that the proposed method deals with spatiotemporal queries within a reasonable time.