Research on MR-Tree Spatial Query Authenticated Index Introduced Neighbor Relationship

With rapid developments of network technologies, database outsourcing is emerging as an important new trend beside the "application-as-a-service". In this model, data owners ship their data to external service providers. Service providers do data management tasks and offer their clients a mechanism to manipulate outsourced databases. Since a service provider is not always fully trusted, security and privacy of outsourced data are significant issues. These problems are referred to as data confidentiality, user privacy, data privacy and query assurance. Among them, query assurance takes a crucial role to the success of the database outsourcing model. To the best of our knowledge, however, query assurance, especially for outsourced XML databases, has not been concerned reasonably in any previous work. In this paper, we propose a novel index structure, nested Merkle B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+ </sup> -tree, combining the advantages of B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -tree and Merkle hash tree to completely deal with three issues of query assurance known as correctness, completeness and freshness in dynamic outsourced XML databases. Experimental results with real datasets prove the efficiency of our proposed solution

Spatial databases have become a critical part of modern applications. Some important applications of a spatial database include Geographic Information System (GIS), Computer Aided Design(CAD), image processing and robotics. Spatial queries retrieve the required geographic data from spatial databases. In this thesis, we classify spatial queries into two categories based on their objective with regards to the notion of influence. The first category consists of the queries that aim to find the important/influential facilities. Some queries that fall into this category include range queries, k nearest neighbor queries, top-k queries and skyline queries. The second category is to find the influenced users. Queries in this category include reverse k nearest neighbor (RkNN) queries, reverse top-k queries and reverse skyline queries. In this thesis, we present efficient algorithms to solve queries in both categories

Clients of outsourced databases need Query Authentication (QA) guaranteeing the integrity (correctness and completeness), and authenticity of the query results returned by potentially compromised providers.Existing results provide QA assurances for a limited class of queries by deploying several software cryptographic constructs. Here, we show that, to achieve QA, however, it is significantly cheaper and more practical to deploy server-hosted, tamper-proof co-processors, despite their higher acquisition costs. Further, this provides the ability to handle arbitrary queries.To reach this insight, we extensively survey existing QA work and identify interdependencies and efficiency relationships. We then introduce CorrectDB, a new DBMS with full QA assurances, leveraging server-hosted, tamper-proof, trusted hardware in close proximity to the outsourced data.

( r , QI)-Transform: Reversible Data Anonymity Based on Numeric Type of Data in Outsourced Database

Modern location-based applications rely extensively on the efficient processing of spatial data and queries. Spatial query engines are commonly engineered as an extension to a relational database or a cluster-computing framework. Large parts of the spatial processing runtime is spent on evaluating spatial predicates and traversing spatial indexing structures. Typical high-level implementations of these spatial structures incur significant interpretive overhead, which increases latency and lowers throughput. A promising idea to improve the performance of spatial workloads is to leverage native code generation techniques that have become popular in relational query engines. However, architecting a spatial query compiler is challenging since spatial processing has fundamentally different execution characteristics from relational workloads in terms of data dimensionality, indexing structures, and predicate evaluation. In this paper, we discuss the underlying reasons why standard query compilation techniques are not fully effective when applied to spatial workloads, and we demonstrate how a particular style of query compilation based on techniques borrowed from partial evaluation and generative programming manages to avoid most of these difficulties by extending the scope of custom code generation into the data structures layer. We extend the LB2 main-memory query compiler, a relational engine developed in this style, with spatial data types, predicates, indexing structures, and operators. We show that the spatial extension matches the performance of specialized library code and outperforms relational and map-reduce extensions.

Spatial queries include spatial selection query, spatial join query, nearest neighbor query, etc. Most of spatial queries are computing intensive and individual query evaluation may take minutes or even hours. Parallelization seems a good solution for such problems. However, parallel programs must communicate efficiently, balance work across all nodes, and address problems such as failed nodes. We describe MapReduce and show how spatial queries can be naturally expressed in this model, without explicitly addressing any of the details of parallelization. We present performance evaluations for several spatial queries and prove that MapReduce is also appropriate for small scale clusters and computing intensive applications.

With the rapid advent of Virtual Reality (VR) technology and virtual tour applications, there is a research need on spatial queries tailored for simultaneous movements in both the physical and virtual worlds. Traditional spatial queries, designed mainly for one world, do not consider the entangled dual worlds in VR. In this paper, we first investigate the fundamental shortest-path query in VR as the building block for spatial queries, aiming to avoid hitting boundaries and obstacles in the physical environment by leveraging Redirected Walking (RW) in Computer Graphics. Specifically, we first formulate Dual-world Redirected-walking Obstacle-free Path (DROP) to find the minimum-distance path in the virtual world, which is constrained by the RW cost in the physical world to ensure immersive experience in VR. We prove DROP is NP-hard and design a fully polynomial-time approximation scheme, Dual Entangled World Navigation (DEWN), by finding Minimum Immersion Loss Range (MIL Range). Afterward, we show that the existing spatial query algorithms and index structures can leverage DEWN as a building block to support kNN and range queries in the dual worlds of VR. Experimental results and a user study with implementation in HTC VIVE manifest that DEWN outperforms the baselines with smoother RW operations in various VR scenarios.

With the increasing use of smart mobile devices, users can connect and coordinate with local people or events that match their interests through geosocial networking applications. This calls for techniques to support processing the Multiple-User Location-based Query (MULQ), which returns a group of users with k Point of Interests (POIs) based on their locations and individual preferences. When the query processing is outsourced to a service provider capable of handling voluminous spatial data objects, the query authentication becomes highly desirable because the service provider is beyond the administrative domain of a data owner. In this paper, we design an MRS-tree to index POIs, based on which, we propose a MULQ processing and authentication solution. We further proposed a bitmap-based dominance relationship comparison algorithm to improve its efficiency. A set of experiments are carried out, which shows the validity of our solutions under various parameters.

Query efficiency of geographic information system is a critical factor which depends on the query algorithm, and query algorithm is dependent on indexing structure. Based on the R-Tree spatial index, a method optimizing the query index is proposed for GIS most adjacent query and multi-dimensional data structure is designed. Query performance of the structure is analyzed theoretically and advantages are proved. The spatial overlap is effectively reduced and the query efficiency is improved. Application results show that the method can meet the application requirements with low-speed operating environment, and has a certain predictability constraints for query time.

Developed from Nearest Neighbor (NN) queries, Aggregate Nearest Neighbor (ANN) queries return the object that minimizes an aggregate distance function with respect to a set of query points. Because of the multiple query points, ANN queries are much more complex than NN queries. For optimizing the query processing and improving the query efficiency, many ANN queries algorithms utilizes pruning strategies, with or without an index structure. Obviously, the pruning effect highly depends on the tightness of the bound estimation. In this paper, we figure out a property in vector space and develop some efficient bound estimations for two most popular types of ANN queries. Based on these bounds, we design the indexed and non-index ANN algorithms, and conduct experimental studies. Our algorithms show good performance, especially for high dimensional queries, for both real dataset and synthetic datasets.

Sensor management plays an important role in the field of Internet of Things. Therefore, the requests of spatial approximate query increase dramatically. Indexing is no doubt a feasible way for efficient spatial approximate search. However, there is a lack of an effective index structure for spatial approximate query. In this paper, we propose a new type of index structure called SR-tree for providing more intelligent retrieval, which is based on R-tree and inverted table. Our index can support for spatial approximate search and work freely either in memory or external memory. The experimental results show that the structure proposed can provide high scalability and fast response time.

The emerging new services for GPS and mobile users have developed applications that access and exploit spatial objects with new components (e.g. text and social network). Web objects, including blogs, tweets, photos and videos, are embedded into a map by the APIs of map service providers, where textual messages are associated with geographic information. Location-based social networking services, arising from Facebook and Foursquare, allow users to browse and share their traces of locations among the social networks. Among this class of applications, the highlight is that the retrieved spatial objects (e.g. points of interest and moving users) are featured with new components. Integrating such new components into the spatial query processing has produced large amounts of promising results. However, handling new components along with the retrieval of spatial objects increases the complexity of such joint query processing significantly. Thus, management over data from such multiple domains has been received considerable attention from database research community.\n\nIn this thesis, we introduce three interesting problems and study their sophisticated solutions for processing spatial objects with new components: (i) category-aware optimal route query (CORQ), (ii) social and spatial ranking query (SSRQ), and (iii) efficient notification of meeting point (ENMP) query. Our results for (i) and (ii) facilitate the retrieval of spatial objects from multiple domains while our solutions for (iii) provide effective tools for synchronous management of multiple moving users from a social network. Category-aware optimal route queries (CORQ) are generalized from the traveling salesman problem and enable users to retrieve shortest routes covering selected categories. Social and spatial ranking queries (SSRQ) are relevant to spatial object recommendations using social information and allow users to obtain the spatial objects that not only are near their locations but also impress them with high social influence. Efficient notification of meeting point (ENMP) queries are variants of aggregate nearest neighbor queries and provide real-time rearrangement for multiple moving users according to their locations.\n\nQuery processing in such multiple domains is complicated due to the mixture of domain information and their integration within one search. Naïve algorithms for these problems incur either numerous expensive evaluations or massive communication cost, which render them inapplicable to large datasets. Our main research purpose is to design efficient and effective solutions for the proposed problems, that avoid the aforementioned shortcomings of naïve algorithms.

In the Outsourced Database (ODB) model, entities outsource their data management needs to a third-party service provider. Such a service provider offers mechanisms for its clients to create, store, update, and access (query) their databases. This work provides mechanisms to ensure data integrity and authenticity for outsourced databases. Specifically, this article provides mechanisms that assure the querier that the query results have not been tampered with and are authentic (with respect to the actual data owner). It investigates both the security and efficiency aspects of the problem and constructs several secure and practical schemes that facilitate the integrity and authenticity of query replies while incurring low computational and communication costs.

With the widespread application of Geographic Information System (GIS), three-dimensional spatial data, as the reflection of the real world entity, has an increasing amount of data, and the phenomenon of uneven data distribution appears. If a single spatial index structure is used to store and manage these data, there will be a waste of storage space and low query efficiency. A hybrid index structure based on 3D multi-level adaptive grid and R+ tree was proposed to solve these problems. The index structure was mainly composed of two structures, multi-level grid and R+ tree. Firstly, the data set was processed by the multi-level automatic grid algorithm based on normal distribution, and the length, width and height of the grid were obtained. Secondly, a multi-level adaptive grid structure was used to partition the data space quickly and effectively, and the advantage of zero overlap of the intermediate nodes of the R+ tree was used for efficient indexing. Finally, the maintenance and query algorithms of the index structure were given in detail, which solved the problem of low index establishment and retrieval efficiency under the condition of uneven distribution of massive data sets. In this paper, a data set subject to Gauss distribution was used to simulate the distribution of three-dimensional data. Through a large number of experimental comparison tests, it was proved that the hybrid index structure based on 3D multi-level adaptive grid-R+ tree proposed in this paper had good performance in both index structure construction and query in the case of massive data sets or uneven data distribution.

The broadcasting of spatial data together with an index structure is an effective way of disseminating data in a wireless mobile environment. Mobile clients requesting data tune into a continuous broadcast only when spatial data of interest and relevance is available on the channel and thus minimize their power consumption. A mobile client experiences latency (time elapsed from requesting to receiving data) and tuning time (the amount of time spent listening to the channel). This paper studies the execution of spatial queries on broadcasted tree-based spatial index structures. The focus is on queries that require a partial traversal of the spatial index, not only a single-path root-to-leaf search. We present techniques for processing spatial queries while mobile clients are listening to a broadcast of the tree. Our algorithms can handle clients with limited memory, trees broadcast with a certain degree of replication of index nodes, and algorithms executed at the clients may employ different data structures. Experimental work on R*-trees shows that these techniques lead to different tuning times and different latencies. Our solutions also lead to efficient methods for starting the execution of a query in the middle of a broadcast cycle. Spatial query processing in a multiple channel environment is also addressed.