History-independence

Indexes are used in database management systems as a mechanism to speed up data retrieval. A good index structure should ensure that the data access through it is efficient and transactional. For one dimensional data, the B+-tree and its variants, such as the Blink-Tree and the π,-tree, are used widely because of their good query performance and concurrency control and recovery. However, structures for multidimensional data, despite the intense research for more than two decades, have not been so successful. Very few of the many proposed structures are implemented in commercial databases. For those that have been used, users are confronted with either poor performance or low concurrency. We proposed a new spatial access method called the Spatial C-Tree. Our approach extends the C-Tree framework, which provides efficient concurrency control and robust recovery mechanism. We have implemented the Spatial C-Tree and compared it with several R-Tree variants. Our performance results show that the Spatial C-Tree performs very well in terms of insertion and exact match queries with various data sets. Our range query performance in some cases is comparable to the R-Tree variants and in other cases are outperformed by the R-Tree variants. We have identified possible improvements over the range query performance. The potential improvement on the range query performance and the high concurrency guarantee make the Spatial C-Tree a promising access method for spatial data.

Many database applications require effective representation of regional objects in high-dimensional spaces. By applying an original query transformation, a recently proposed access method for regional data, called the simple QSF-tree (sQSF-tree), effectively attacks the limitations of traditional spatial access methods in spaces with many dimensions. Nevertheless, sQSF-trees are not immune to all problems associated with high data dimensionality. Based on the analysis of sQSF-trees, this paper presents a new variant of sQSF-trees, called the scalable QSF-tree (cQSF-tree), which relies on a heuristic optimization to reduce the number of false drops into pages that contain no object satisfying the query. By increasing the selectivity of search predicates, cQSF-trees improve the performance of multi-dimensional selections. Experimental evidence shows that cQSF-trees are more scalable than sQSF-trees to the growing data dimensionality. The performance improvements also increase with more skewed data distribution.

The importance of multidimensional index structures to numerous emerging database applications is well established. However, before these index structures can be supported as access methods (AMs) in a “commercial-strength” database management system (DBMS), efficient techniques to provide transactional access to data via the index structure must be developed. Concurrent accesses to data via index structures introduce the problem of protecting ranges specified in the retrieval from phantom insertions and deletions (the phantom problem ). This paper presents a dynamic granular locking approach to phantom protection in Generalized Search Trees(GiSTs), an index structure supporting an extensible set of queries and data types. The granular locking technique offers a high degree of concurrency and has a low lock overhead. Our experiments show that the granular locking technique (1) scales well under various system loads and (2) similar to the B-tree case, provides a significantly more efficient implementation compared to predicate locking for multidimensional AMs as well. Since a wide variety of multidimensional index structures can be implemented using GiST, the developed algorithms provide a general solution to concurrency control in multidimensional AMs. To the best of our knowledge, this paper provides the first such solution based on granular locking.

In previous chapters we have examined various access methods, which have been tailored to be used in a great variety of environment and applications. However, the advancements in optics, VLSI and materials have brought communications and computer science close to each other than ever. New practical and theoretical problems have emerged at the interface between databases and web, such as electronic commerce, mobile communications, searching in heterogeneous sources and the like. In the present chapter we will examine, access methods used in data warehouses and on-line analytical processing, access methods for managing semistructured data and access methods for main memory databases.

An efficient index structure for complex multi-dimensional objects is one of the most challenging requirements in non-traditional applications such as geographic information systems, computer-aided design, and multimedia databases. In this paper we first propose a main memory data structure for complex multi-dimensional objects. Then, we present an extension of the existing multi-dimensional index structure. Among existing multi-dimensional index structures, the popular Ra-tree is selected. The Ra-tree is coupled with the main memory data structure to improve the performance of spatial query processing. An analytical model is developed for our index structure. Experimental results show that the analytical model is accurate, the relative error being below 15%. The performance of our index structure is compared with that of a state-of-the-art index structure by experimental measurements. Our index structure outperforms the state-of-the-art index structure due to its ability to reduce a large amount of storage.

Similarity search is fundamental to store and retrieve large volumes of complex data required by many real world applications. A useful mechanism for such concept is the query-by-similarity. Based on their topological properties, metric similarity functions can be used to index sets of data which can be queried effectively and efficiently by the so-called metric access methods. However, data produced by various application domains and the varying data types handled often lead to missing data, hence, they do not follow the metric similarity requirements. As a consequence, missing data cause distortions in the index structure and yield bias in the query answer. In this paper, we propose the Hollow-tree, a novel access method aimed at successfully retrieving data with missing attribute values. It employs new strategies for indexing and searching data elements, capable of handling the missing data issues when the cause of missingness is ignorable. The indexing strategy is based on a family of distance functions that allow measuring the distance between elements with missing values, along with a set of policies able to organize the elements in the index without causing distortions to its internal structure. The searching strategy employs fractal dimension property of the data to achieve accurate query answer while considering data with missing values part of the response. Results from experiments performed on a variety of real and synthetic data sets showed that, while other metric access methods deteriorate with small amounts of missing values, the Hollow-tree maintains a remarkable performance with almost 100% of precision and recall for range queries and more than 90% for k-nearest neighbor queries, for up to 40% of missing values.

Since 1993, Los Alamos National Laboratory has been developing World Wide Web (WWW) applications to facilitate access to vast quantities of information critical to the successful operation of a nuclear weapons facility. Explorer is a Web based tool that integrates full text search and retrieval technology, custom user interfaces, user friendly navigation tools, extremely large document collections, and data collection and workflow applications. Explorer's first major thrust was to enable quick access to regulatory and policy information used by Department of Energy facilities throughout the country. Today, Explorer users can easily search document collections containing millions of pages of information, scattered across Web sites around the country. Over fifteen large applications containing multiple collections are searchable through Explorer, and the subject areas range from DOE regulations to quality management related resources to technology transfer opportunities. Explorer has succeeded because it provides quick and easy access to stored data across the Web; it saves time and reduces costs in comparison with traditional information distribution, access, and retrieval methods.

STT-RAM (Spin Transfer Torque Random Access Memory) has been extensively researched as a potential replacement of SRAM (Static RAM) as on-chip caches. Prior work has shown that STT-RAM caches can improve performance and reduce power consumption because of its advantages of high density, fast read speed, low standby power, etc. However, under the impact of process variations, using worst-case design can induce significant performance and power overhead in STT-RAM caches. In order to overcome the problem of process variations, we propose to apply the variable-latency access method to STT-RAM caches by introducing a variation-aware LRU (Least Recently Used) policy. Moreover, we show that simply applying traditional variable-latency access method is inefficient due to the read/write asymmetry. First, we demonstrate that a write-oriented data migration is preferred. Second, a block remapping is necessary to prevent some cache sets from being significantly affected by process variations. After using our techniques, the experimental results show that the performance can be improved by 13.8% and power consumption can be reduced by 14.1% compared to a prior approach [3].

To reduce the energy consumption of wireless network communication in the transmission line scenario and improve the service life of the network, this paper proposes an air-ground cooperative access algorithm. The algorithm aims to minimize the communication energy consumption of the base station serving transmission line nodes. Firstly, the user rate contribution weight provided by the air-ground base station is optimized, and then the air-ground coordinated power allocation is realized according to the optimal weight. The simulation results show that the proposed air-ground cooperative access algorithm achieves a reduction in energy consumption of over 50% compared to the traditional single base station access method.

In vehicular ad hoc networks, vehicles compete for channel access through the medium access control protocol to transmit messages. Most traditional medium access methods assign the same probability to all vehicles accessing the channel and ignore the intrinsic attributes of different vehicles. Attributes regarding the position and role of a vehicle are important factors to be considered when a vehicle is competing for the channel. In this paper, we introduce a new notion of a weight factor in a fair medium access game and propose a game model to adjust each node's channel access probability with complete information in the vehicular environment based on the weight factors of vehicles. The weight of a vehicle represents a specific property, such as the distance between vehicles, the vehicle's role, and so on, which can be used to distinguish priorities of different vehicles during channel competition. We also provide instances of relevant applications by using the distance between vehicles and a vehicle's role. Then, we analyze the incomplete information game in which each node cannot acquire the previous channel access probabilities of other nodes. Moreover, we construct a distributed best‐response learning algorithm based on the medium access game model and demonstrate that the game can converge to a unique and stable equilibrium under certain conditions. Furthermore, we investigate some performance indicators such as channel access delay and normalized throughput and perform extensive numerical analyses compared with the IEEE 802.11p protocol and an existing game‐based medium access control algorithm without consideration of weights. The performance comparison of high‐priority nodes and all nodes in a network is also studied. Finally, we analyze the effects of the proportion of high‐priority nodes on the performance of the channel. The performance improvement after introducing the weight factor is verified by these numerical results.

We present an access method designed to provide a single integrated index structure for a versioned timestamped database with a non-deletion policy. Historical data (superceded versions) is stored separately from current data. Our access method is called the Time-Split B-tree. It is an index structure based on Malcolm Easton's Write Once B-tree.

With the prevalence of graph data in a variety of domains, there is an increasing need for a language to query and manipulate graphs with heterogeneous attributes and structures. We present a graph query language (GraphQL) that supports bulk operations on graphs with arbitrary structures and annotated at- tributes. In this language, graphs are the basic unit of information and each query manipulates one or more collections of graphs at a time. The core of GraphQL is a graph algebra extended from the relational algebra in which the selection operator is generalized to graph pattern matching and a composition operator is introduced for rewriting matched graphs. Then, we investigate access methods of the selection operator. Pattern matching over large graphs is challenging due to the NP-completeness of subgraph isomorphism. We address this by a combination of techniques: use of neighborhood subgraphs and pro- files, joint reduction of the search space, and optimization of the search order. Experimental results on real and synthetic large graphs demonstrate that graph specific optimizations outperform an SQL-based implementation by orders of magnitude.

There is more and more commercial and research interest in nearest neighbor objects for location-based search from spatial database. Specially, a spatial keyword query takes a user location and user-supplied keywords as arguments and returns objects that is nearest k objects from user current location and textually relevant to the user required keyword. In these systems, user can type one or more word for required keyword. To find the result that contains at least one required keyword, it is important to support Boolean OR semantic keyword search on spatial database. In this paper we study how to answer such queries efficiently for both kNN query and range query. This paper proposes new index structure that combines K-d tree and inverted file. K-d tree efficiently support for both nearest neighbor and range queries. We also discuss Boolean OR Semantic keyword search for the user’s required keywords.

Frequent pattern mining (FPM) has played an important role in many graph domains, such as bioinformatics and social networks. In this paper, we focus on geo-social graphs, a kind of social network augmented by geographical information. However, in addition to the exponential time complexity of the problem, we face the challenge of efficient subgraph retrieval since we are interested in patterns in a specific region in such a network. For this reason, we formulate the top-k FPM problem in large geo-social networks. Specifically, we devise a novel framework for subgraph retrieval and FPM mining with a series of optimizations. First, we propose a neighboring-aware R-tree (NaR-Tree) index structure to alleviate the challenge of retrieving subgraphs from a large graph. NaR-Tree is a variant of R-tree in which each nonleaf tree node further maintains some edge statistics information for the rectangle related to it. Second, we define the concept of minimum image-based support of edges (MNIE). With the help of the NaR-Tree and MNIE-based pattern extension approach, a mining algorithm that addresses the problem of exponential candidate patterns is proposed. We also present a lazy retrieval strategy to reduce the frequency of subgraph retrieval. Finally, we adopt an edge sampling approach to further accelerate the mining process. Extensive experiments on real-world and synthesized datasets are conducted to demonstrate the effectiveness and efficiency of our solution.

In this paper, a new access method is proposed for current positions of moving objects on road networks in order to efficiently update their positions. In the existing index structures, the connectivity of edges is lost because the intersection points in which three or more edges are split. The proposed index structure preserves the network connectivity, which uses intersection oriented network model by not splitting intersection nodes that three or more edges meet for preserving the connectivity of adjacent road segments. The data node stores not only the positions of moving object but also the connectivity of networks.