Analysis and Comparison of P2P Search Methods

The popularity and bandwidth consumption attributed to current Peer-to-Peer file-sharing applications makes the operation of these distributed systems very important for the Internet community. Efficient object discovery is the first step towards the realization of distributed resource-sharing. In this work, we present a detailed overview of recent and existing search methods for unstructured Peer-to-Peer networks. We analyze the performance of the algorithms relative to various metrics, giving emphasis on the success rate, bandwidth-efficiency and adaptation to dynamic network conditions. Simulation results are used to empirically evaluate the behavior of nine representative schemes under a variety of different environments

A New Method to Store and Retrieve Images

In this paper, we present a method to accelerate the speed of querying and retrieving images in database. First we change the storing method: pixels of an image are saved in Hilbert order instead of Row-wise order using in traditional method. Then after studying the property of Hilbert curve, we give a new algorithm which greatly reduce the data segment number on the disk. Although we have to retrieve more data than necessary, because the speed of sequential reading is much faster than random reading, we have about 10% improvement on the total query time which is showed in our simulation experiments

VIEWCACHE: An incremental pointer-base access method for distributed databases. Part 1: The universal index system design document. Part 2: The universal index system low-level design document. Part 3: User's guide. Part 4: Reference manual. Part 5: UIMS test suite

The goal of the Universal Index System (UIS), is to provide an easy-to-use and reliable interface to many different kinds of database systems. The impetus for this system was to simplify database index management for users, thus encouraging the use of indexes. As the idea grew into an actual system design, the concept of increasing database performance by facilitating the use of time-saving techniques at the user level became a theme for the project. This Final Report describes the Design, the Implementation of UIS, and its Language Interfaces. It also includes the User's Guide and the Reference Manual

APRE: A Replication Method for Unstructured P2P Networks

We present APRE, a replication method for structureless Peer-to-Peer overlays. The goal of our method is to achieve real-time replication of even the most sparsely located content relative to demand. APRE adaptively expands or contracts the replica set of an object in order to improve the sharing process and achieve a low load distribution among the providers. To achieve that, it utilizes search knowledge to identify possible replication targets inside query-intensive areas of the overlay. We present detailed simulation results where APRE exhibits both efficiency and robustness relative to the number of requesters and the respective request rates. The scheme proves particularly useful in the event of flash crowds, managing to quickly adapt to sudden surges in load

Analysis and Comparison of P2P Search Methods

The popularity and bandwidth consumption attributed to current Peer-to-Peer file-sharing applications makes the operation of these distributed systems very important for the Internet community. Efficient object discovery is the first step towards the realization of distributed resource-sharing. In this work, we present a detailed overview of recent and existing search methods for unstructured Peer-to-Peer networks. We analyze the performance of the algorithms relative to various metrics, giving emphasis on the success rate, bandwidth-efficiency and adaptation to dynamic network conditions. Simulation results are used to empirically evaluate the behavior of nine representative schemes under a variety of different environments. (UMIACS-TR-2003-107

Online View Selection for the Web

View materialization has been shown to ameliorate the scalability problem of data-intensive web servers. However, unlike data warehouses which are off-line during updates, most web servers maintain their back-end databases online and perform updates concurrently with user accesses. In such environments, the selection of views to materialize must be performed online; both performance and data freshness should be considered. In this paper, we discuss the Online View Selection problem: select which views to materialize in order to maximize performance while maintaining freshness at acceptable levels. We define Quality of Service and Quality of Data metrics and present OVIS(theta), an adaptive algorithm for the Online View Selection problem. OVIS(theta) evolves the materialization decisions to match the constantly changing access/update patterns on the Web. The algorithm is also able to identify infeasible freshness levels, effectively avoiding saturation at the server. We performed extensive experiments under various workloads, which showed that our online algorithm comes close to the optimal off-line selection algorithm. Also UMIACS-TR-2002-2

The Dwarf Data Cube Eliminates the Highy Dimensionality Curse

The data cube operator encapsulates all possible groupings of a data set and has proved to be an invaluable tool in analyzing vast amounts of data. However its apparent exponential complexity has significantly limited its applicability to low dimensional datasets. Recently the idea of the dwarf data cube model was introduced, and showed that high-dimensional ``dwarf data cubes'' are orders of magnitudes smaller in size than the original data cubes even when they calculate and store every possible aggregation with 100\% precision. In this paper we present a surprising analytical result proving that the size of dwarf cubes grows polynomially with the dimensionality of the data set and, therefore, a full data cube at 100% precision is not inherently cursed by high dimensionality. This striking result of polynomial complexity reformulates the context of cube management and redefines most of the problems associated with data-warehousing and On-Line Analytical Processing. We also develop an efficient algorithm for estimating the size of dwarf data cubes before actually computing them. Finally, we complement our analytical approach with an experimental evaluation using real and synthetic data sets, and demonstrate our results. UMIACS-TR-2003-12

The Implementation and Performance Evaluation of the ADMS Query Optimizer: Integrating Query Result Caching and Matching

In this paper, we describe the design and evaluation of the ADMS optimizer. Capitalizing on a structure called Logical Access Path Schema to model the derivation relationship among cached query results, the optimizer is able to perform query matching coincidently with the optimization and generate more efficient query plans using cached results. The optimizer also features data caching and pointer caching, different cache replacement strategies, and different cache update strategies. An extensive set of experiments were conducted, and the results showed that pointer caching and dynamic cache update strategies substantially speedup query computations and, thus, increase query throughput under situations with fair query correlation and update load. The requirement of the cache space is relatively small and the extra computation overhead introduced by the caching and matching mechanism is more than offset by the time saved in query processing. (Also cross-referenced as UMIACS-TR-93-106