Challenges in Spatio-temporal Stream Query Optimization

Location-detection devices are used ubiquitously in moving objects due to the everyday decreasing cost and simplified technology. Usually, these devices will send the moving objects' location information to a spatio-temporal data stream management system that will be then responsible for answering spatio-temporal queries related to these moving objects. Most of the existing work focused on the continuous spatio-temporal query execution. However, several outstanding challenges have been either addressed partially or not at all in the existing literature. In this paper, we focus on the optimization of multi-predicate spatio-temporal queries on moving objects. In particular, we provide a costing mechanism for continuous spatio-temporal queries. We provide for the optimization of the parameters of the spatiotemporal operators. Finally, we propose the adaptive execution of the continuous queries for spatio-temporal data stream management systems.

GeoInformatica, 2005

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

2004

The emergence of location-aware services calls for new real-time spatio-temporal query processing algorithms that deal with large numbers of mobile objects and queries. In this demo, we present PLACE (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server addresses scalability by adopting an incremental evaluation mechanism for answering concurrently executing continuous spatiotemporal queries. The PLACE server supports a wide variety of stationery and moving continuous spatio-temporal queries through a set of pipelined spatio-temporal operators. The large numbers of moving objects generate real-time spatio-temporal data streams.

High Technology Letters, 2021

The extensive usages of cellular devices, hand-held devices, and GPS equipments facilitates environments where almost all entities are much aware of its own localities. Such locations demand for novel query processing systems to competently upkeep location-aware servers. Enormous amount of continuous fixed and moving ST queries, if any deferral of query response outcomes in an outdated response where moving-objects are unremittingly fluctuating their locations. Queries are enhanced centrally based on multiple measures such as spatial topological associations, temporal and attribute correlations. Simulation results shows that the proposed scheme is highly scalable for large scale spatio-temporal queries and also has the added advantage of minimizing the energy consumption due to query and data transmission.

In this paper, we introduce PLACE*, a distributed spatio-temporal data stream management system for moving objects. PLACE* supports continuous spatio-temporal queries that hop among a network of regional servers. To minimize the execution cost, a new Query-Track-Participate (QTP) query processing model is proposed inside PLACE*. In the QTP model, a query is continuously answered by a querying server, a tracking server, and a set of participating servers. In this paper, we focus on query plan generation, execution and update algorithms for continuous range queries in PLACE* using QTP. An extensive experimental study demonstrates the effectiveness of the proposed algorithms in PLACE*.

2006

In this paper, we propose, SCUBA, a Scalable Cl uster Based Algorithm for evaluating a large set of continuous queries over spatio-temporal data streams. The key idea of SCUBA is to group moving objects and queries based on common spatio-temporal properties at run-time into moving clusters to optimize query execution and thus facilitate scalability. SCUBA exploits shared cluster-based execution by abstracting the evaluation of a set of spatio-temporal queries as a spatial join first between moving clusters. This cluster-based filtering prunes true negatives. Then the execution proceeds with a fine-grained within-moving-cluster join process for all pairs of moving clusters identified as potentially joinable by a positive cluster-join match. A moving cluster can serve as an approximation of the location of its members. We show how moving clusters can serve as means for intelligent load shedding of spatio-temporal data to avoid performance degradation with minimal harm to result quality. Our experiments on real datasets demonstrate that SCUBA can achieve a substantial improvement when executing continuous queries on spatio-temporal data streams.

Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS '02, 2002

Database applications for moving objects pose new challenges in modeling, querying, and maintenance of objects whose locations are rapidly changing over time. Previous work on modeling and querying spatio-temporal databases and constraint databases focus primarily on snapshots of changing databases. In this paper we study query evaluation techniques for moving object databases where moving objects are being updated frequently. We consider a constraint database approach to moving objects and queries. We classify moving object queries into: "past", "continuing", and "future" queries. We argue that while traditional constraint query evaluation techniques are suitable for past queries, new techniques are needed for continuing and future queries. Motivated by nearest-neighbor queries, we define a query language based on a single "generalized distance" function f mapping from objects to continuous functions from time to R. Queries in this language may be past, continuing, or future. We show that if f maps to polynomials, queries can be evaluated efficiently using the plane sweeping technique from computational geometry. Consequently, many known distance based queries can be evaluated efficiently.

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004., 2004

Real-time spatio-temporal query processing needs to effectively handle a large number of moving objects and continuous spatio-temporal queries. In this paper, we use shared execution as a mechanism to support scalability in location-aware servers. Our main idea is to maintain a query table that stores information about continuous spatio-temporal queries. Then, answering spatio-temporal queries is abstracted as a spatial join among the moving objects and queries. Three query join policies are proposed aiming to minimize the cost of the join operation under the shared execution paradigm, namely the Clock-triggered Join Policy, the Incremental Join Policy, and the Hot Join Policy. We introduce the concept of a No-Action Region that is used in conjunction with the hot join policy. We propose algorithms that calculate the No-Action region for objects and queries. Experimental performance demonstrates that the No-Action region is more efficient than other approaches when used along with the hot join policy. Experiments also demonstrate that the hot join policy outperforms the clock-triggered join policy and the incremental join policy in terms of both I/O and CPU costs.

Due to the recent growth of the World Wide Web, numerous spatio-temporal applications can obtain their required information from publicly available web sources. We consider those sources maintaining moving objects with predefined paths and schedules, and investigate different plans to perform queries on the integration of these data sources efficiently. Examples of such data sources are networks of railroad paths and schedules for trains running between cities connected through these networks. A typical query on such data sources is to find all trains that pass through a given point on the network within a given time interval. We show that traditional filter+semi-join plans would not result in efficient query response times on distributed spatio-temporal sources. Hence, we propose a novel spatio-temporal filter, called deviation filter, that exploits both the spatial and temporal characteristics of the sources in order to improve the selectivity. We also report on our experiments in comparing the performances of the alternative query plans and conclude that the plan with spatio-temporal filter is the most viable and superior plan. *

Lecture Notes in Computer Science, 2004

The ability to represent and query continuously moving objects is important in many applications of spatio-temporal database systems. In this paper we develop data structures for answering various queries on moving objects, including range and proximity queries, and study tradeoffs between various performance measures-query time, data structure size, and accuracy of results.