Tree Index Structure Research Articles

EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data

With the pervasiveness of smart phones, location-based services (LBS) have received considerable attention and become more popular and vital recently. However, the use of LBS also poses a potential threat to user's location privacy. In this paper, aiming at spatial range query, a popular LBS providing information about points of interest (POIs) within a given distance, we present an efficient and privacy-preserving location-based query solution, called EPLQ. Specifically, to achieve privacy-preserving spatial range query, we propose the first predicate-only encryption scheme for inner product range (IPRE), which can be used to detect whether a position is within a given circular area in a privacy-preserving way. To reduce query latency, we further design a privacy-preserving tree index structure in EPLQ. Detailed security analysis confirms the security properties of EPLQ. In addition, extensive experiments are conducted, and the results demonstrate that EPLQ is very efficient in privacy-preserving spatial range query over outsourced encrypted data. In particular, for a mobile LBS user using an Android phone, around 0.9 s is needed to generate a query, and it also only requires a commodity workstation, which plays the role of the cloud in our experiments, a few seconds to search POIs.

PB<sup><bold>+</bold></sup>-Tree: PCM-Aware B<sup><bold>+</bold></sup>-Tree

Phase change memory (PCM) is non-volatile memory that is byte-addressable. It is two to four times denser than DRAM, orders of magnitude better than NAND Flash memory in read latency, and 10 times better than NAND Flash memory in write endurance. However, it still limits the number of write operations to at most $10^6$ times per PCM cell. To extend its lifetime, it is necessary to evenly distribute write operations over all the memory cells. Up to now, the $\mathrm{B^{+}}$ -Tree index structure has been used to quickly locate a search key in a relational database management system (RDBMS). All the record keys in each node are sorted and packed upon insertion in, and deletion from, the $\mathrm{B^{+}}$ -Tree. In addition, a counter keeps track of the number of valid keys in the $\mathrm{B^{+}}$ -Tree. Consequently, a $\mathrm{B^{+}}$ -Tree algorithm results in a large number of write operations, which deteriorates the endurance of PCM. This restricts the usage of PCM on a database server and deteriorates performance of database servers. In this paper, we propose a novel PCM-aware $\mathrm{B^{+}}$ -Tree index structure, called $\mathrm{PB^{+}}$ -Tree, to provide wear-leveling in PCM. According to our experiment results, $\mathrm{PB^{+}}$ -Tree is much faster than the existing $\mathrm{B^{+}}$ -Tree algorithms for PCM and NAND Flash memory with versatile workloads. More importantly, our scheme also greatly reduces the number of write operations compared to other $\mathrm{B^{+}}$ -Tree algorithms. All of these results suggest that $\mathrm{PB^{+}}$ -Tree is the $\mathrm{B^{+}}$ -Tree algorithm best fitted to PCM.

Block-Based Multi-Version B$^+$-Tree for Flash-Based Embedded Database Systems

In this paper, we propose a novel multi-version B <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$^+$</tex> <mathgraphic fileref="chang-ieq2-2308213.gif" graphicformat="GIF"/></formula> -tree index structure, called block-based multi-version B <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$^+$</tex> <mathgraphic fileref="chang-ieq3-2308213.gif" graphicformat="GIF"/></formula> -tree ( BbMVBT), for indexing multi-versions of data items in an embedded multi-version database (EMVDB ) on flash memory. An EMVDB needs to support streams of update transactions and version-range queries to access different versions of data items maintained in the database. In BbMVBT, the index is divided into two levels. At the higher level, a multi-version index is maintained for keeping successive versions of each data item. These versions are allocated consecutively in a version block. At the lower level, a version array is used to search for a specific data version within a version block. With the reduced index structure of BbMVBT, the overhead for managing the index in processing update operations can be greatly reduced. At the same time, BbMVBT can also greatly reduce the number of accesses to the index in processing version-range queries. To ensure sufficient free blocks for creating version blocks for efficient execution of BbMVBT, in this paper, we also discuss how to perform garbage collection using the purging-range queries for reclaiming “old” versions of data items and their associated entries in the index nodes. Analysis of the performance of BbMVBT is presented and verified with performance studies using both synthetic and real workloads. The performance results illustrate that BbMVBT can significantly improve the read and write performance to the multi-version index as compared with MVBT even though the sizes of the version blocks are not large.

An Adaptive Endurance-Aware ${B^+}$-Tree for Flash Memory Storage Systems

This work is motivated by the strong demand for flash-friendly index designs to resolve reliability and performance concerns for data manipulation over flash memory. In comparison to previous work, we propose and explore the impact of hot-data access, sibling-link updates, and different workload types to a tree index structure over flash memory. In particular, a flash-friendly <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${B^ + }$</tex> </formula> -tree, referred to as an Adaptive Durable <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${B^ + }$</tex> </formula> -tree, is proposed to not only improve the endurance but also the performance of a tree index structure. The capability of the proposed methodology and index design is evaluated through a series of experiments, in which significant improvement on endurance was achieved in comparison to previous reports on the subject.

A Spatial Indexing Scheme for Location Based Service Queries in a Single Wireless Broadcast Channel

The rapid advancement of mobile devices and wireless network technology has accelerated the development of applications in the mobile computing field. In particular, LBS (Location-Based Services) are one of the fastest growing areas in mobile applications. In a wireless data broadcasting environment, a server can effectively provide LBS to a large population of mobile clients. The two most typical types of LBS queries are window and kNN (k-Nearest Neighbors) queries. Previously proposed window and kNN query processing schemes in a single wireless broadcast channel environment tend to access much unnecessary index information and data objects due to their low filtering powers. In this paper, we propose a new spatial index called HMI (Hilbert curve-based MBR filtering Index) for the efficient processing of window and kNN queries in a single wireless broadcast channel. HMI is a tree-structured index whose construction is based on the Hilbert curve and MBR (Minimum Bounding Rectangle). By combining an MBR structure with the Hilbert curve order, HMI obtains the advantages of both structures. We present an in-depth experimental analysis of our method by comparing it with current existing schemes. Our performance analysis shows that HMI significantly decreases the average access and tuning times of kNN queries over current existing schemes. HMI also gives better average tuning times and comparable average access times of window queries over current existing schemes.

An analysis of deterring and facilitating factors in trade service business -on the basis of customs broker task-

During a decade, e-Commerce environments supporting real-time transaction processing have been getting larger. In telecommunication and financial environments, research and building for main memory database systems have been doing to support real-time transaction processing. A research on indexing for fast transaction support focuses on reducing cache misses or reducing memory access latency when cache misses happen. In the paper, we propose a prefetch method for tree index structures to reduce memory access latency. We present a prefetch-efficient pCST-tree and show superiority of the proposed tree by experiments.

The Adjustment of Texture Mapping Based on Image-Entropy

Mass terrain in four fork tree index structure Xia, draws Shi needs for effective of texture map, viewpoint of moved and texture retrieved Shi scene dynamic update directly effect to displayed efficiency, used macro points block match micro-texture map of method, effective to reduced has data of frequency refresh; introduced image entropy reflect image color distribution of space features, to improve original terrain texture, to out a more meet actual of terrain draws method; experimental indicates that in mass terrain texture map process in the, Increases the level of authenticity and real-time terrain rendering

High volumes of event stream indexing and efficient multi-keyword searching for cloud monitoring

In the Cloud computing environment, enterprise applications can produce high volumes of data, which need to be effectively analyzed for administrators to understand system behaviors. Many run-time data analysis tools can give the most up-to-date knowledge of the system to administrators. However, when troubleshooting a problem in depth, the offline data analysis functionality is necessarily required to get the complete knowledge for system diagnosis. In this paper, we propose a composite tree index structure for the Run-Time Correlation Engine framework to achieve efficient event indexing and searching tasks. The framework has been previously proposed as an automatic tool for easing many run-time data analysis tasks. By integrating the data indexing solution to the framework, we are able to further enhance the tool to perform offline data analysis tasks to provide a more sophisticated monitoring service in the Cloud computing environment.

An efficient B+-tree design for main-memory database systems with strong access locality

This paper is motivated by the strong demands of many main-memory database applications with strong locality in data access, such as front-end logistical systems. We propose to adopt an auxiliary-tree approach with an tree-merging algorithm to efficiently handle bursty data insertions with keys in a small range and avoid significant overheads in tree rebalancing. A range-based deletion algorithm is then proposed to process data deletions with strong access locality in a batch fashion. The capability of the proposed approach is evaluated by a series of experiments with a wide range of workloads and a variety of locality patterns, where different tree index structures are compared in terms of the performance and memory space requirements.

Tridex: A lightweight triple index for relational database-based Semantic Web data management

► We analyzed the essential features of a triple database from the viewpoint of RDB. ► We investigated the fundamental aspects of a RDB-based triple data model. ► We developed a lightweight B+-tree triple index structure, named Tridex. ► Tridex is beneficial in reduced index tree size and less data redundancy. ► Tridex achieves high performance and scalability, compared to baseline approaches. As more data are provided in Semantic Web, processing large amounts of data with triple-format triple-data, and interlinking the applications with triples, have become important for a variety of applications. A challenge is to index a triple database efficiently. Most existing triple indexing techniques suffer from data duplication and the oversized index tree problem. We analyzed the drawbacks of existing triple indexing methods from the viewpoint of the consistency and effectiveness of a triple database. We also considered the issues that need to be addressed to build a triple index for the management of relational database (RDB)-based triple data. Tridex is a lightweight B+-tree index structure designed to facilitate efficient processing of RDB-based triple databases. Tridex is beneficial in reduced index tree size and less data redundancy. We conducted an experimental evaluation to demonstrate the superiority of Tridex compared to existing triple index structures.

A Kind of Index Technology Based on Binary Tree Efficient Space

Space index is one of the key technologies of spatial database, and also one of the biggest problems with puzzling GIS workers. So , how to build a more effective spatial index structure ,which has been the most realistic, most urgent, also to the forefront of research subject to GIS domain and graphics processing. This paper introduces several typical spatial index methods in the e GIS domain and graphics processing fields, and puts forward a binary tree index structure generated space grid step by step. The average time complexity of the index structure is the same with the quad tree index structure, but the maximum time complexity is reduced a third than the quadtree index structure, that is, the search efficiency improving a third.

K-NN query algorithm based on PB-tree with the parallel lines division

Abstract Spatial index and query are enabling techniques for achieving the vision of the Internet of Things. K-NN is an algorithm which is used widely in spatial database. Traditional query algorithms use R-tree as the index structure and improve the query efficiency by using the measurement distance and pruning strategy. Based on the study of previous algorithms, this paper proposes a novel K-NN query algorithm based on PB-tree with the parallel lines division. PB-tree index is different from the traditional R-tree index, where PB-tree adopts parallel lines to divide the spatial region and uses parallel lines as the parent node. It is similar to the binary tree index structure and requires to query three small portions nearest to the queried object in each K-NN query. Therefore, the search range is narrowed and the query efficiency is enhanced. Experiments show that PB-tree is better than the traditional R-tree from the aspect of query performance. PB-tree can avoid the deficiency of a large number of overlap and coverage among odes in R-tree and multiple index paths when searching data objects, and hence PB-tree can find K-NN objects meeting the conditions quickly and efficiently in large data sets.

Generic and efficient framework for search trees on flash memory storage systems

Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the c...

J2ME And Mobile Database Design

J2ME is a development platform for mobile devices and has been introduced by Sun Micro-Systems Inc. In 1999 for programming limited devices such as phones, PDAs and other small devices. But this architecture does not support APIs for data persistence management and relational database because of its limitations. This paper presents a base for local relational database and data persistence management for J2ME based applications and can be used in any database aware application in J2ME platform. In this paper and implementation of this database system mobile device and J2ME limitations have been considered. Also, the B+tree indexing structure has been implemented in this project that allows us fast insertion, deletion, range queries, search, backup and restore mechanism for RMS based databases.

Optimal Pivot Selection Method Based on the Partition and the Pruning Effect for Metric Space Indexes

This paper proposes a new method to reduce the cost of nearest neighbor searches in metric spaces. Many similarity search indexes recursively divide a region into subregions by using pivots, and construct a tree-structured index. Most of recently developed indexes focus on pruning objects and do not pay much attention to the tree balancing. As a result, indexes having imbalanced tree-structure may be constructed and the search cost is degraded. We propose a similarity search index called the Partitioning Capacity (PC) Tree. It selects the optimal pivot in terms of the PC that quantifies the balance of the regions partitioned by a pivot as well as the estimated effectiveness of the search pruning by the pivot. As a result, PCTree reduces the search cost for various data distributions. We experimentally compared PCTree with four indexes using synthetic data and five real datasets. The experimental results shows that the PCTree successfully reduces the search cost.

Efficient and effective similarity search over probabilistic data based on earth mover's distance

Probabilistic data is coming as a new deluge along with the technical advances on geographical tracking, multimedia processing, sensor network and RFID. While similarity search is an important functionality supporting the manipulation of probabilistic data, it raises new challenges to traditional relational database. The problem stems from the limited effectiveness of the distance metric supported by the existing database system. On the other hand, some complicated distance operators have proven their values for better distinguishing ability in the probabilistic domain. In this paper, we discuss the similarity search problem with the Earth Mover's Distance , which is the most successful distance metric on probabilistic histograms and an expensive operator with cubic complexity. We present a new database approach to answer range queries and k-nearest neighbor queries on probabilistic data, on the basis of Earth Mover's Distance. Our solution utilizes the primal-dual theory in linear programming and deploys B + tree index structures for effective candidate pruning. Extensive experiments show that our proposal dramatically improves the scalability of probabilistic databases.

Efficient continuous skyline computation

In a number of emerging streaming applications, the data values that are produced have an associated time interval for which they are valid. A useful computation over such streaming data is to produce a continuous and valid skyline summary. Previous work on skyline algorithms have only focused on evaluating skylines over static data sets, and there are no known algorithms for skyline computation in the continuous setting. In this paper, we introduce the continuous time-interval skyline operator, which continuously computes the current skyline over a data stream. We present a new algorithm called LookOut for evaluating such queries efficiently, and empirically demonstrate the scalability of this algorithm. In addition, we also examine the effect of the underlying spatial index structure when evaluating skylines. Whereas previous work on skyline computations have only considered using the R ∗-tree index structure, we show that for skyline computations using an underlying quadtree has significant performance benefits over an R ∗-tree index.

Fast Rebuilding B+-Trees for Index Recovery

Rebuilding an index is an essential step for database recovery. Fast recovery of the index is a necessary condition for fast database recovery. The B+-Tree is the most popular index structure in database systems. In this paper, we present a fast B+-Tree rebuilding algorithm called Max-PL. The main idea of Max-PL is that at first it constructs a B+-Tree index structure using the pre-stored max keys of the leaf nodes, and then inserts the keys and data pointers into the index. The algorithm employs a pipelining mechanism for reading the data records and inserting the keys into the index. It also exploits parallelisms in several phases to boost the overall performance. We analyze the time complexity and space requirement of the algorithm, and perform the experimental study to compare its performance to other B+-Trees rebuilding algorithms; Sequential Insertion and Batch-Construction. The results show that our algorithm runs on average at least 670% faster than Sequential Insertion and 200% faster than Batch-Construction.

An indexing scheme for energy-efficient processing of content-based retrieval queries on a wireless data stream

Wireless data broadcasting is a popular data delivery approach in mobile computing environments, where the broadcasting servers usually adopt indexing schemes for mobile clients to energy-efficiently access data on a wireless broadcast stream. However, conventional indexing schemes use primary key attribute values to construct tree structures. Therefore, these schemes do not support content-based retrieval queries such as partial-match queries and range-queries. This paper proposes an indexing method that supports content-based retrieval queries on a wireless data stream. The method uses a tree-structured index, called B2V-Tree, which is composed of bit-vectors that are generated from data records through multi-attribute hashing. Through analysis and experiments, the effectiveness of the proposed method is shown.

Structural Periodic Measures for Time-Series Data

This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity detection and we introduce new periodic distance measures for time-series sequences. We combine these new measures with an effective metric tree index structure for efficiently answering k-Nearest-Neighbor queries. The goal of these tools and techniques are to assist in detecting, monitoring and visualizing structural periodic changes. It is our belief that these methods can be directly applicable in the manufacturing industry for preventive maintenance and in the medical sciences for accurate classification and anomaly detection.