Leveraging computation sharing and parallel processing in location-dependent query 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.

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.

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.

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.

Most of the mobile phones have GPS sensors which make location based service (LBS) applicable. LBS brings not only convenience but also location privacy leak to us. Achieving anonymity and sending private queries are two main privacy-preserving courses in LBS. A novel location privacy-preserving method is proposed based on Voronoi graph partition on road networks. Firstly, based on the prediction of a user's moving direction, a cooperative k-anonymity method is proposed without constructing cloaking regions which may lead to efficiency decline in continuous query. And then, a query algorithm is proposed without providing any user's actual location, replaced by continuous anchor sequence, to LBS provider. This algorithm can work out precise results according to candidate sets returned by LBS provider and it also solves uneven distribution problem in SpaceTwist. Performance analysis and experiments show that our method achieves a preferable tradeoff between QoS and location privacy preserving; it has obvious advantages compared with other methods.

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.

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.

Monitoring and tracking LBS applications require data-base and application support to model and manage moving objects in both database and application domains. Such services must also provide efficient processing of continuous queries over moving objects. In contrast to regular queries that are evaluated only once, a continuous query remains active over a period of time.

Recent development in wireless communication technology and mobile equipment is making location-based services (LBSs) more popular day by day. However, because users continuously send queries to a server by using their exact locations in the LBSs, private information can be in danger. Therefore, a mechanism for users’ privacy protection is required for the safe and comfortable use of LBSs. For this, we, in this paper, propose a grid-based cloaking area creation scheme in order to support continuous queries in LBSs. Our scheme creates a cloaking area rapidly by using grid-based cell expansion to efficiently support the continuous LBSs. In addition, to generate a cloaking area which lowers the exposure probability of a mobile user to a minimum level, our scheme computes a privacy protection degree by granting weights to the mobile users. Finally, we show from our performance analysis that our cloaking scheme shows better performance than the existing cloaking scheme.KeywordsPrivacy protectionContinuous QueriesCloaking scheme

Communication-efficient processing of multiple continuous aggregate queries

<p>Due to the growth in mobile phones, the location based service (LBS) market is growing tremendously fast. Many mobile phone applications uses LBS such as store finder, car navigation system etc. LBS provide services to mobile users based on location & data profile of user’s hence users private information may get violated. In order to protect users private information many solutions are offered but most of them only addressed on snapshot query and no support for continual query and Moving Query Moving Object(MQMO).</p> <p><a href="https://journalnx.com/journal-article/20150568">https://journalnx.com/journal-article/20150568</a></p>

Recently, the ubiquitousness of smartphones and tablet computers have changed the style of people’s daily life. With this tendency, location based service (LBS) has become one of the prosperous types of service along with the wireless and positioning technology development. However, as the LBS server needs precise location information about the user to provide service result, the procedure of LBS may reveal location privacy, especially when a user is utilizing continuous query along the road. In continuous query, attributes of the user are released inadvertently with per-query, and the information can be collected by an adversary as background knowledge to correlate the location trajectory and infer the personal privacy. Although, a user can employ a central server (CS) to provide privacy preservation for his location, the trustfulness of CS still is without testified and it is usually considered as an un-trusted entity. Thus, in this paper, the trustfulness of CS is verified by a game tree, and then with the result we propose a hash based attribute anonymous scheme (short for HBAA) to obfuscate the attributes released in each query along the road. With the help of HBAA, the CS has no opportunity to get any information about the user who sends his query for generalization service. Furthermore, as the set of attributes is transmitted into a fixed length of hash value, the processing time that spent in attribute generalization is stripped down and the performance of executive efficiency is improved. At last, security analysis and simulation experiment are proposed, and then results of security proving as well as simulation experiments further reflect the superiority of our proposed scheme.

Because of increased stream data, managing stream data has become quite important. This paper describes our data stream management system, which employs an architecture combining a stream processing engine and DBMS. Based on the architecture, the system processes both continuous queries and traditional one-shot queries. Our proposed query language supports not only filtering, join, and projection over data streams, but also continuous persistence requirements for stream data. Users can also specify continuous queries that integrate streaming data and historical data stored in DBMS. Another contribution of this paper is feasibility validation of queries. Processing queries on streams with frequent inputs may cause the system to overflow its capacity. Specifically, the maximum writing rate to DBMS is a significant bottleneck when we try to store stream data into DBMS. Our system detects infeasible queries in advance.

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.

Interests in skyline query processing have significantly increased since it can be used in many applications, such as multi-criteria decision making, data mining, and user preference queries. In this paper, we propose continuous skyline queries over dynamic data set. Since the dynamic data set constantly changes as time passes, continuous skyline queries processing over dynamic data set becomes ever more complicated compared with static data set. We propose a multiple layer grids scheme for efficiently processing continuous skyline queries over skewed data set. The proposed scheme divides the work space into multiple layer grids and creates the skyline influence regions of each layer grid based on the existing data set. The continuous skyline queries are handled only when the dynamic data points update within the skyline influence regions of each layer grid. Since the multiple layer grids are divided dynamically, it is feasible even for the highly skewed data set. Experiments based on various synthetic data sets and real data sets show that our proposed scheme outperforms the existing schemes.