Continuous aggregate nearest neighbor queries

Lecture Notes in Computer Science, 2005

Nearest Neighbor (NN) search has been in the core of spatial and spatiotemporal database research during the last decade. The literature on NN query processing algorithms so far deals with either stationary or moving query points over static datasets or future (predicted) locations over a set of continuously moving points. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services.

ICPS '05. Proceedings. International Conference on Pervasive Services, 2005., 2005

In databases of moving objects it is important to answer queries that concern the future positions of the objects. An important query type in such an environment is the nearest-neighbor query, which asks for the k closest objects of a query object during a time interval [t s , t e ]. However, there are cases where the (k+1)-th nearest-neighbor is requested after the execution of the k-NN query. In such a case, either the query must be evaluated again, or we can exploit the previous result and use an incremental method to determine the new answer. We focus on the second alternative and present efficient incremental algorithms that outperform the trivial method which is based on complete re-execution of the query. In addition, we study the problem of keeping the query result consistent in the presence of object insertions, deletions and updates which are very common in a dynamic moving-object environment.

IEEE Transactions on Knowledge and Data Engineering, 2015

Central to many applications involving moving objects is the task of processing k-nearest neighbor (k-NN) queries. Most of the existing approaches to this problem are designed for the centralized setting where query processing takes place on a single server; it is difficult, if not impossible, for them to scale to a distributed setting to handle the vast volume of data and concurrent queries that are increasingly common in those applications. To address this problem, we propose a suite of solutions that can support scalable distributed processing of k-NN queries. We first present a new index structure called Dynamic Strip Index (DSI), which can better adapt to different data distributions than exiting grid indexes. Moreover, it can be naturally distributed across the cluster, therefore lending itself well to distributed processing. We further propose a distributed k-NN search (DKNN) algorithm based on DSI. DKNN avoids having an uncertain number of potentially expensive iterations, and is thus more efficient and more predictable than existing approaches. DSI and DKNN are implemented on Apache S4, an open-source platform for distributed stream processing. We perform extensive experiments to study the characteristics of DSI and DKNN, and compare them with three baseline methods. Experimental results show that our proposal scales well and significantly outperforms the alternative methods.

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*.

Simplified technology and low costs have spurred the use of location-detection devices in moving objects. Usually, these devices will send the moving objects' location information to a spatio-temporal data stream management system, which will be then responsible for answering spatio-temporal queries related to these moving objects. A large spectrum of research have been devoted to continuous spatio-temporal query processing. However, we argue that several outstanding challenges have been either addressed partially or not at all in the existing literature. In particular, in this paper, we focus on the optimization of multi-predicate spatio-temporal queries on moving objects. We present several major challenges related to the lack of spatio-temporal pipelined operators, and the impact of time, space, and their combination on the query plan optimality under different circumstances of query and object distributions. We show that building an adaptive query optimization framework is key in addressing these challenges and coping with the dynamic nature of the environment we are evolving in.

International Journal of Information and Education Technology, 2017

This paper proposed a new algorithm for answering a novel kind of nearest neighbour search, that is, continuous mutual nearest neighbour (CMNN) search. In this kind of query, by providing a set of objects O and a query object q, CMNN continuously returns the set of objects from O, which is among the k 1 nearest neighbours of q; meanwhile, q is one of their k 2 nearest neighbours. CMNN queries are important in many applications such as decision making, pattern recognition and although it is useful in service providing systems, such as police patrol, taxi drivers, mobile car repairs and so forth. In this paper, we have proposed the first work for handling CMNN queries efficiently, without any assumption on object movements. The most important feature of this work is incremental evaluation and scalability. Utilizing an incremental evaluation technique led to a significant decrease in processing time.

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.

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.

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 2005 international workshop on Geographic information systems - GIS '05, 2005

Databases of moving objects are important for air traffic control, ground traffic, and battlefield configurations. We introduce the (historical and spatial) range close-pair query for moving objects as an important problem for such databases. The purpose of a range close-pair query for moving objects is to find pairs of objects that were closer than during time interval I and within spatial range R, where , I and R are user-specified parameters. This paper solves the range close-pair query using two components: the retrieval component and the close-pair identification component. The retrieval component breaks up long trajectories into trajectory segments, which are produced in increasing time order, without the need for sorting. The retrieval component takes advantage of a new index mechanism, the Multiple TSB-tree. The segments are then pipelined to the close-pair identification component. The identification component introduces a novel spatial sweep that sweeps by time and one spatial dimension at the same time. Extensive experimental results are provided, demonstrating the advantages of the new approach when considering close pairs.

21st International Conference on Data Engineering (ICDE'05), 2005

Location-aware environments are characterized by a large number of objects and a large number of continuous queries. Both the objects and continuous queries may change their locations over time. In this paper, we focus on continuous k-nearest neighbor queries (CKNN, for short). We present a new algorithm, termed SEA-CNN, for answering continuously a collection of concurrent CKNN queries. SEA-CNN has two important features: incremental evaluation and shared execution. SEA-CNN achieves both efficiency and scalability in the presence of a set of concurrent queries. Furthermore, SEA-CNN does not make any assumptions about the movement of objects, e.g., the objects velocities and shapes of trajectories, or about the mutability of the objects and/or the queries, i.e., moving or stationary queries issued on moving or stationary objects. We provide theoretical analysis of SEA-CNN with respect to the execution costs, memory requirements and effects of tunable parameters. Comprehensive experimentation shows that SEA-CNN is highly scalable and is more efficient in terms of both I/O and CPU costs in comparison to other R-tree-based CKNN techniques.

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.

2003

Abstract Whereas earlier work on spatiotemporal databases generally focused on geometries changing in discrete steps, the emerging area of moving objects databases supports geometries changing continuously. Two important abstractions are moving point and moving region, modelling objects for which only the time-dependent position, or also the shape and extent are relevant, respectively.

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013

In this paper the concept of safe limit in the framework for continuously moving objects to monitor result changes of spatio temporal queries has been proposed. In this framework the movement of the moving object is monitored with the help of user defined aggregate function over spatio temporal reference in data stream management system. The expected movement of the moving object is plotted on the graph and the spatio temporal queries are answered on the basis of that, until and unless the difference between the expected movement and actual movement is more than the safe limit. This makes the framework more efficient than the previously given framework. The safe limit can be any range with respect to space and time of a moving object which varies according to different parameters such as size of the object, velocity with which it is moving and etc.

International Convention on Information and Communication Technology, Electronics and Microelectronics, 2011

Managing moving objects in DSMS has recently been a focus of a relatively intense research. In this paper, we are concerned with data streams containing a location of a moving object at any time instant. Instead of using spatial and time instant data types separately, for representing spatio-temporal characteristics of data streams, we propose a unified approach based on specialized temporal data type constructor. This type constructor, parameterized with a spatial type representing objects location, yields a corresponding type whose values are pairs, consisting of an instant in time and a value of spatial type. With this approach, it is possible to represent temporal development of objects' location within one time interval by using two consecutive values of this temporal data type. Since moving object is basically obtained just by assembling these temporal developments whose time intervals are disjoint, we also propose an approach for extracting moving objects out of spatio-temporal data streams based on user-defined aggregate functions (UDAF) over spatio-temporal data types. With this approach, it is possible to manage moving objects encapsulated within spatio-temporal data streams using SQL-like query language.