Special section on spatial and temporal databases

Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '99, 1999

We consider the problem of performing nearest-neighbor queries e ciently over large high-dimensional databases. Assuming that a full database scan to determine the nearest neighborentries is not acceptable, we study the possibility of constructing an index structure over the database. It is well-accepted that traditional database indexing algorithms fail for high-dimensional data (say d > 10 or 20 depending on the scheme). Some arguments have a d v ocated that nearest-neighbor queries do not even make sense for high-dimensional data since the ratio of maximum and minimum distance goes to 1 as dimensionality increases. We show that these arguments are based on over-restrictive assumptions, and that in the general case it is meaningful and possible to perform such queries. We present an approach for deriving a multidimensional index to support approximate nearestneighbor queries over large databases. Our approach, called DBIN, scales to high-dimensional databases by exploiting statistical properties of the data. The approach is based on statistically modeling the density of the content of the data table. DBIN uses the density model to derive a single index over the data table and requires physically rewriting data in a new table sorted by the newly created index (i.e. create what is known as a clustered-index in the database literature). The indexing scheme produces a mapping between a query point (a data record) and an ordering on the clustered index values. Data is then scanned according to the index until the probability that the nearest-neighbor has been found exceeds some threshold. We present theoretical and empirical justi cation for DBIN. The scheme supports a family of distance functions which includes the traditional Euclidean distance measure.

K-Nearest Neighbor (k-NN) queries are used in GIS and CAD/CAM applications to find the k spatial objects closest to some given query points. Most previous k-NN research has assumed that the spatial databases to be queried are local, and that the query processing algorithms have direct access to their spatial indices, e.g. R-trees. Clearly, this assumption does not hold when k-NN queries are directed at remote spatial databases that operate autonomously. While it is possible to replicate some or all the spatial objects from the remote databases in a local database and build a separate index structure for them, such an alternative is infeasible when the database is huge, or there are large number of spatial databases to be queried. In this paper, we therefore propose a k-NN query processing algorithm that uses one or more window query to retrieve the nearest neighbors of a given query point. We also propose three different methods to estimate the ranges to be used by the window queries. Each range estimation method requires different statistical knowledge about the spatial databases. Our experiments on the TIGER data have shown that our proposed algorithm coupled with different range estimation methods can handle k-NN queries efficiently. Apart from not requiring direct access to the spatial indices, the window queries used in our proposed algorithm can be easily supported by non-spatial database systems containing spatial objects.

IEEE Access

In this paper, based upon Voronoi Diagram, we propose GridVoronoi which is a novel spatial index that enables users to find the spatial nearest neighbour (NN) from two-dimensional (2D) datasets in almost O(1) time. GridVoronoi augments the Voronoi Diagram with a virtual grid to promptly find out (in a geometric space) which Voronoi cell contains the query point. It consists of an off-line data preprocessing phase and an on-line query processing phase. In the off-line phase, the digital geographical space is partitioned with a Voronoi Diagram and a virtual grid, respectively. Next, for each square unit (i.e., grid cell), the corresponding Voronoi cells that contain or intersect with this square are derived and kept in a hashmap-like structure. In the on-line phase, for each real-time spatial NN query, the algorithm first identifies which virtual square(s) contain(s) this query; then looks up the hashmap structure to find the corresponding Voronoi cell(s) for this grid cell and the final result for the query. Overall, GridVoronoi significantly reduces the time complexity in finding spatial NN in 2D space, thus improves the efficiency of real-time spatial NN queries and Location Based Services. INDEX TERMS Geospatial analysis, Nearest neighbour methods, Query processing, Spatial databases.

International Journal of Future Computer and Communication

2007 IEEE 23rd International Conference on Data Engineering Workshop, 2007

With the proliferation of mobile devices (e.g., PDAs, cell phones, etc.), location-based services have become more and more popular in recent years. However, users have to reveal their location information to access location-based services with existing service infrastructures. It is possible that adversaries could collect the location information, which in turn invades user's privacy. There are existing solutions for query processing on spatial networks and mobile user privacy protection in Euclidean space. However there is no solution for solving queries on spatial networks with privacy protection. Therefore, we aim to provide network distance spatial query solutions which can preserve user privacy by utilizing K-anonymity mechanisms. In this paper, we present two novel query algorithms, PSNN and PSRQ, for answering nearest neighbor queries and range queries on spatial networks without revealing private information of the query initiator. The effectiveness of our privacy protected algorithms has been validated using real world road networks. In addition, we demonstrate the appeal of our technique using extensive simulation results.

GeoInformatica, 2005

Nearest neighbor query is one of the most important operations in spatial databases and their application domains, such as location-based services and advanced traveler information systems. This paper addresses the problem of finding the in-route nearest ...

2003

The database research community prides itself on scalable technologies. Yet database systems traditionally do not excel on one important scalability dimension: the degree of distribution. This limitation has hampered the impact of database technologies on massively distributed systems like the Internet.

2011

Given a set of objects and a query q, a point p is called the reverse k nearest neighbor (RkNN) of q if q is one of the k closest objects of p. In this paper, we introduce the concept of influence zone which is the area such that every point inside this area is the RkNN of q and every point outside this area is not the RkNN. The influence zone has several applications in location based services, marketing and decision support systems. It can also be used to efficiently process RkNN queries. First, we present efficient algorithm to compute the influence zone. Then, based on the influence zone, we present efficient algorithms to process RkNN queries that significantly outperform existing best known techniques for both the snapshot and continuous RkNN queries. We also present a detailed theoretical analysis to analyse the area of the influence zone and IO costs of our RkNN processing algorithms. Our experiments demonstrate the accuracy of our theoretical analysis.

2009

In this paper, we present a Distributed Incremental Nearest Neighbor algorithm (DINN) for finding closest objects in an incremental fashion over data distributed among computer nodes, each able to perform its local Incremental Nearest Neighbor (local-INN) algorithm. We prove that our algorithm is optimum with respect to both the number of involved nodes and the number of local-INN invocations. An implementation of our DINN algorithm, on a real P2P system called MCAN, was used for conducting an extensive experimental evaluation on a real-life dataset.

User’s locations and there interested are different. Collecting large amount of data to the user is very difficult. Sometime information is very sensitive; handling the information is very difficult task. Information storage also very big issue.so data owner does not make data accessible in all customers. It’s allowed only paying customers. User send their current location points and want to know about nearest POIs in NN but data Owner does not have that much storage capacity so we are using cloud service. Cloud provides power full storage at low cost but Cloud is not fully trusted. So we are using processing of NN queries in an untrusted outsourced environment, whereas at an equivalent time protective the POI and querying users’ location positions. Our techniques based on mutable order preserving encoding (mOPE). It is a secure order-preserving encryption. We provide performance of the optimizations process is increase and decrease the computational cost

The Vldb Journal

In this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in Euclidean space as well as in spatial networks. Existing techniques are sensitive toward objects and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We present a framework for continuous reverse k nearest neighbor (RkNN) queries by assigning each object and query with a safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. This significantly improves the computation cost. As a byproduct, our framework also reduces the communication cost in client–server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. We also conduct a rigid cost analysis for our Euclidean space RkNN algorithm. We show that our techniques can also be applied to answer bichromatic RkNN queries in Euclidean space as well as in spatial networks. Furthermore, we show that our techniques can be extended for the spatial networks that are represented by directed graphs. The extensive experiments demonstrate that our techniques outperform the existing techniques by an order of magnitude in terms of computation cost and communication cost.

Journal of Digital Forensics, Security and Law

Private indexing is a set of approaches for analyzing research data that are similar or resemble similar ones. This is used in the database to keep track of the keys and their values. The main subject of this research is private indexing in record linkage to secure the data. Because unique personal identification numbers or social security numbers are not accessible in most countries or databases, data linkage is limited to attributes such as date of birth and names to distinguish between the number of records and the real-life entities they represent. For security reasons, the encryption of these identifiers is required. Privacypreserving record linkage, frequently used to link private data within several databases from different companies, prevents sensitive information from being exposed to other companies. This research used a combined method to evaluate the data, using classic and new indexing methods. A combined approach is more secure than typical standard indexing in terms of privacy. Multibit tree indexing, which groups comparable data in many ways, creates a scalable tree-like structure that is both space and time flexible, as it avoids the need for redundant block structures. Because the record pair numbers to compare are the Cartesian product of both the file record numbers, the work required grows with the number of records to compare in the files. The evaluation findings of this research showed that combined method is scalable in terms of the number of databases to be linked, the database size, and the time required.

Mathematics

The increasing trend of GPS-enabled smartphones has led to the tremendous usage of Location-Based Service applications. In the past few years, a significant amount of studies have been conducted to process All nearest neighbor (ANN) queries. An ANN query on a road network extracts and returns all the closest data objects for all query objects. Most of the existing studies on ANN queries are performed either in Euclidean space or static road networks. Moreover, combining the nearest neighbor query and join operation is an expensive procedure because it requires computing the distance between each pair of query objects and data objects. This study considers the problem of processing the ANN queries on a dynamic road network where the weight, i.e., the traveling distance and time varies due to various traffic conditions. To address this problem, a shared execution-based approach called standard clustered loop (SCL) is proposed that allows efficient processing of ANN queries on a dynami...

Journal of Cloud Computing, 2017

Data owners with large volumes of data can outsource spatial databases by taking advantage of the cost-effective cloud computing model with attractive on-demand features such as scalability and high computing power. Data confidentiality in outsourced databases is a key requirement and therefore, untrusted third-party service providers in the cloud should not be able to view or manipulate the data. This paper proposes DISC (Dynamic Index for Spatial data on the Cloud), a secure retrieval scheme to answer range queries over encrypted databases at the Cloud Service Provider. The dynamic spatial index is also able to support dynamic updates on the outsourced data at the cloud server. To be able to support secure query processing and updates on the Cloud, spatial transformation is applied to the data and the spatial index is encrypted using Order-Preserving Encryption. With transformation and cryptography techniques, DISC achieves a balance between efficient query execution and data confidentiality in a cloud environment. Additionally, a more secure scheme, DISC * , is proposed to balance the trade-off between query results returned and security provided. The security analysis section studies the various attacks handled by DISC. The experimental study demonstrates that the proposed scheme achieves a lower communication cost in comparison to existing cloud retrieval schemes.

Vol. 19 No. 3 MARCH 2021 International Journal of Computer Science and Information Security (IJCSIS), 2021

The prevailing infrastructure of ubiquitous computing paradigm on the one hand making significant development for integrating technology in the daily life but on the other hand raising concerns for privacy and confidentiality. As Location based services (LBS) equip users to query information specific to a location with respect to temporal and spatial factors thus LBS put under extreme criticism when it comes to location privacy and user confidentiality. Here in this paper we are addressing the significance of our proposed scheme, a query processing architecture for privacy preservation in LBS, by providing flexible and efficient LBS model to ensure accurate and qualitative result set by employing some indexing scheme at location anonymizer as well as by Identifying possible adversary attacks to breach user privacy in the previous work with respect to location privacy and query privacy. Realizing the need for a unanimous query processing model which can operate in centralize as well as distributed environment, also flexible enough to provide privacy for public queries (snapshot/continuous) as well as private queries (snapshot/continuous) for public and private locations. Finally we will quantify the benefits of our approach using sampled results through experiments that the proposed cloaking algorithm is scalable, efficient and robust to support anonymity irrespective of scale of user queries in real time scenario.