Monitoring and Tracking Moving Objects in Mobile Environments

With the increasing in demand on location-based aware services and RFIDs, efficient processing of continuous queries over moving object streams becomes important. In this paper, we propose an efficient in-memory processing of continuous queries on themoving object streams.We model moving objects using function of time and use it in the prediction of usefulness of objects with respect to the continuous queries. To effectively utilize the limited memory, we derive several replacement policies to discard objects that are of no potential interest to the queries and design efficient algorithms with light data structures. Experimental studies are conducted and the results show that our proposed method is both memory and query efficient.

We define continual queries as a useful tool for monitoring of updated information. Continual queries are standing queries that monitor the source data and notify the users whenever new data matches the query. In addition to periodic refresh, continual queries include Epsilon Transaction concepts to allow users to specify query refresh based on the magnitude of updates. To support efficient processing of continual queries, we propose a differential re-evaluation algorithm (DRA), which exploits the structure and information contained in both the query expressions and the database update operations. The DRA design can be seen as a synthesis of previous research on differential files, incremental view maintenance, and active databases.

Efficient continuous query processing is critical for many applications, including monitoring remote sensors network traffic management, and online transaction processing. To overcome resource limitations and achieve real-time responsiveness, continuous query systems research focuses on issues such as punctuation-driven operator-state purging [4], load shedding, operator scheduling [7] and plan optimization and on-line migration [9]. However, when real-time yet accurate results are critical such as in stock market analysis, then there is a limitation to what any of the above optimization techniques can accomplish. Hence, distributed processing must be applied to support required scalability. It has been already proven in traditional database systems [3] that distributed processing results in high scale-up and speed-up capabilities due to aggregated resources. The design of a distributed continuous query system is characterized by an extra complexity, considered in the design of our D-Cape engine. In such a system, data streams may be infinite and initial cost statistics about the data streams are typically unknown. Moreover, cost statistics continue to change over time. Our research on distributed stream processing addresses two critical questions: (1) How to initially distribute query plans given little or possibly no cost information, and (2) How to efficiently adapt the query distribution corresponding to runtime environmental changes. Even though research is now under way in designing distributed continuous query systems [2], [1], results to date, as far as we know, are based on simulations. Our work offers empirical results of distributed continuous query processing using an actual software system [5], [6]. D-Cape, a distributed continuous query processing architecture, employs stream query engines over a cluster of shared-nothing processors. We employ a dedicated distribution manager to manage

A variety of research exists for the processing of continuous queries in large, mobile environments. Each method tries, in its own way, to address the computational bottleneck of constantly processing so many queries. In this paper, we introduce an efficient and scalable system for monitoring continuous queries by leveraging the parallel processing capability of the Graphics Processing Unit. We examine a naive CPU-based solution for continuous range-monitoring queries, and we then extend this system using the GPU. Additionally, with mobile communication devices becoming commodity, location-based services will become ubiquitous. To cope with the very high intensity of location-based queries, we propose a view oriented approach of the location database, thereby reducing computation costs by exploiting computation sharing amongst queries requiring the same view. Our studies show that by exploiting the parallel processing power of the GPU, we are able to significantly scale the number of mobile objects, while maintaining an acceptable level of performance.

With the increase in the number of mobile users, the need for new type of services and applications is also on the rise. Particularly, a growing area of interest is to provide location dependent services to the mobile users. In this paper, we present a system that supports distributed processing of location-dependent continuous queries on distributed mobile data in a mobile environment using mobile agents. We investigate a scenario in which both the querying unit and the object being queried (target object) are capable of movement and the mobile unit is equipped with more powerful capabilities, including the storage of a small database and the capacity of data processing. Since our system deals with continuous queries, we propose a shared incremental update mechanism, which caches the results of the previously evaluated queries and avoids redundant results thereby minimizing the data traffic in wireless environment. To further reduce the data transfer we share the cached data among different queries. Our system uses mobile agents for evaluating queries, which involves data from both the server and mobile unit, and it helps in parallel processing of data and it, can efficiently track the mobile units involved in the system. The conventional query processing method like join processing of data from mobile units and server is directly applied in the mobile environment.

Recently more and more people focus on continuous K-Nearest Neighbor (CKNN) query processing over moving Objects in Road Networks. A CKNN query is to find among all moving objects the K-nearest neighbors (KNNs) of a moving query object during a period of time. The main issue with existing methods is that moving objects change their locations frequently over time and if their location updates cannot be processed in time, the system runs the risk of retrieving the incorrect results of KNN. In this paper, an effective method is proposed to deal with continuous K-Nearest Neighbor query processing. By considering whether a moving object o is moving farther away from or getting closer to a query point q, the object which is definitely not in the KNN result set is effectively excluded. Thus we can reduce the communication cost, meanwhile we can also simplify the network distance computation between moving objects and query q. Comprehensive experiments are conducted and the results verify the effectiveness of the proposed algorithms.

Apache Flink is an open-source system for scalable processing of batch and streaming data. Flink does not natively support efficient processing of spatial data streams, which is a requirement of many applications dealing with spatial data. Besides Flink, other scalable spatial data processing platforms including GeoSpark, Spatial Hadoop, etc. do not support streaming workloads and can only handle static/batch workloads. To fill this gap, we present GeoFlink, which extends Apache Flink to support spatial data types, indexes and continuous queries over spatial data streams. To enable efficient processing of spatial continuous queries and for the effective data distribution across Flink cluster nodes, a gird-based index is introduced. GeoFlink currently supports spatial range, spatial kNN and spatial join queries on point data type. An experimental study on real spatial data streams shows that GeoFlink achieves significantly higher query throughput than ordinary Flink processing.

The growth in the number of mobile users is motivating the new technology of mobile computing to offer many new types of services like location-based services that pose many challenging problems for location-dependent query processing and data management. In this paper, we discuss the various challenges in distributed processing of location-dependent continuous queries in a mobile environment by studying the different scenarios in which both the querying unit and the object being queried are in motion. Then we proceed to classify different types of location dependent queries. We discuss the different solutions to solve these complex location dependent queries using caching techniques that facilitate fast access to data and at the same time minimize the massive network traffic generated due to location dependent query processing in a wireless environment. To further reduce the data transfer the query results are cached so that these results are reusable. The conventional query processing method like join processing of data from mobile units and server is directly applied in the mobile environment. Additionally this paper also focuses on the methods of location-dependent query processing and applications. The main goal of this paper is to study the various location dependent query processing methods along with data management problems involved in evaluation of Location-dependent queries and classify them.

A defining characteristic of continuous queries over on-line data streams, possibly bounded by sliding windows, is the potentially infinite and time-evolving nature of their inputs and outputs. New items continually arrive on the input streams and new results are continually produced. Additionally, inputs expire by falling out of range of their sliding windows and results expire when they cease to satisfy the query. This impacts continuous query processing in two ways. First, data stream systems allow tables to be queried alongside data streams, but in terms of query semantics, it is not clear how updates of tables are different from insertions and deletions caused by the movement of the sliding windows. Second, many interesting queries need to store state, which must be kept up-to-date as time goes on. Therefore, query processing efficiency depends highly on the amount of overhead involved in state maintenance.In this paper, we show that the above issues can be solved by understanding the update patterns of continuous queries and exploiting them during query processing. We propose a classification that defines four types of update characteristics. Using our classification, we present a definition of continuous query semantics that clearly states the role of relations. We then propose the notion of update-pattern-aware query processing, where physical implementations of query operators, including the data structures used for storing intermediate state, vary depending on the update patterns of their inputs and outputs. When tested on IP traffic logs, our update-pattern-aware query plans routinely outperform the existing techniques by an order of magnitude.

Many location-based applications are enabled by handling numerous moving queries over mobile objects. Efficient processing of such queries mainly relies on effective probing, i.e., polling the objects to obtain their current locations (required for processing the queries). With effective probing, one can monitor the current location of the objects with sufficient accuracy for the existing queries, by striking a balance between communication cost of probing and accuracy of the knowledge about current location of the objects. In this paper, we focus on location-based applications that reduce to processing a large set of proximity monitoring queries simultaneously, where each query continuously monitors if a pair of objects are within a certain predefined distance. Accordingly, we propose an effective object probing solution for efficient processing of proximity monitoring queries. In particular, with our proposed solution for the first time we formulate optimal probing as a batch processing problem and propose a method to prioritize probing the objects such that the total number of probes required to answer all queries is minimized. Our extensive experiments demonstrate the efficiency of our proposed solution for a wide range of applications involving up to hundreds of millions of queries.

In this paper, we address the fidelity of information collected from distributed, interactive, pervasive computing environments. Queries in these dynamic networks come in two forms: snapshot queries and continuous queries. The former executes through the network and returns values that satisfy the query criteria at a particular instant in time. The latter expects a result that is updated over time to reflect changes in the system. The fidelity, or quality, of the results of these queries can be significantly impacted by the unpredictability of the dynamic network; as the query is distributed and executed, changes that occur due to network and environmental dynamics can cause the query to miss potential results or to report inconsistent information. Our previous work has shown how we can provide semantically meaningful statements about the fidelity, or quality, of the result of a snapshot query in a dynamic networked environment. In this paper, we demonstrate how this fidelity can also be used to adapt a continuous query's processing to make its execution best match the conditions in the underlying dynamic environment.

The management of complex spatial objects in applications, such as geography and cartography, imposes stringent new requirements on spatial database systems, in particular on efficient query processing. As shown before, the performance of spatial query processing can be improved by decomposing complex spatial objects into simple components. Up to now, only decomposition techniques generating a linear number of very simple components, e.g. triangles or trapezoids, have been considered. In this paper, we will investigate the natural trade-off between the complexity of the components and the redundancy, i.e. the number of components, with respect to its effect on efficient query processing. In particular, we present two new decomposition methods generating a better balance between the complexity and the number of components than previously known techniques. We compare these new decomposition methods to the traditional undecomposed representation as well as to the well-known decomposition into convex polygons with respect to their performance in spatial query processing. This comparison points out that for a wide range of query selectivity the new decomposition techniques clearly outperform both the undecomposed representation and the convex decomposition method. More important than the absolute gain in performance by a factor of up to an order of magnitude is the robust performance of our new decomposition techniques over the whole range of query selectivity.

Query processing is that method or technique in area of mobile atmosphere which deal using mobile environment. This research in that way user can think advancement using query processing and it takes more use for speedily accessing global query processing and it is joint venture of query processing between dissimilar sites includes fixed server movable computer. The necessity saving of energy and also the existence of lopsided features in a moveable computing atmosphere, the predictable query processing for a scattered database cannot be straightly genuine to a moveable computing organization. The mobile environment is a collection of mobile diverse hosts, which are enabled to communicate using wireless links. These wireless links are change giving to the natures of mobile networks. Moreover, nodes in the ad-hoc network have to link without any centralized or help. The usability for the user also changes using query processing tool. Thus, this mechanism that allows the sharing of functionality among different devices in same environment change the way of user communication for searching fast query time in mobile computing environment. The aim of this research is to make innovation in query optimization technique in mobile computing environment. This research focuses on improvement of various query optimization techniques for highly effective and trustworthy query optimization methods.

Query Processing is a key determinant in the overall performance of distributed databases. It requires processing of data at their respective sites and transmission of the same between them. These together constitute a distributed query processing strategy (DQP). DQP aims to arrive at an efficient query processing strategy for a given query. This strategy involves generation of efficient query plans for a distributed query. In case of distributed relational queries, the number of possible query plans grows exponentially with an increase in the number of relations accessed by the query. This number increases further when the relations, accessed by the query, have replicas at different sites. Such a large search space renders it infeasible to find optimal query plans. This paper presents a query plan generation algorithm that attempts to generate optimal query plans, for a given query, using genetic algorithm. The query plans so generated involve fewer sites, thus leading to efficient query processing. Further, experimental results show that the proposed algorithm converges quickly towards optimal query plans for an observed crossover and mutation probability.

Communication-efficient processing of multiple continuous aggregate queries