Systematic Review of Indexing Spatial Skyline Queries for Decision Support

Residing in the data age, researchers inferred that huge amount of geo-tagged data is available and identified the importance of Spatial Skyline queries. Spatial or geographic location in conjunction with textual relevance plays a key role in searching Point of Interest (POI) of the user. Efficient indexing techniques like R-Tree, Quad Tree, Z-order curve and variants of these trees are widely available in terms of spatial context. Inverted file is the popular indexing technique for textual data. As Spatial skyline query aims at analyzing both spatial and skyline dominance, there is a necessity for a hybrid indexing technique. This article presents the review of spatial skyline queries evaluation that include a range of indexing techniques which concentrates on disk access, I/O time, CPU time. The investigation and analysis of studies related to skyline queries based upon the indexing model and research gaps are presented in this review.

External-memory Dictionaries in the Affine and PDAM Models

Storage devices have complex performance profiles, including costs to initiate IOs (e.g., seek times in hard drives), parallelism and bank conflicts (in SSDs), costs to transfer data, and firmware-internal operations. The Disk-access Machine (DAM) model simplifies reality by assuming that storage devices transfer data in blocks of size B and that all transfers have unit cost. Despite its simplifications, the DAM model is reasonably accurate. In fact, if B is set to the half-bandwidth point, where the latency and bandwidth of the hardware are equal, then the DAM approximates the IO cost on any hardware to within a factor of 2. Furthermore, the DAM model explains the popularity of B-trees in the 1970s and the current popularity of B ɛ -trees and log-structured merge trees. But it fails to explain why some B-trees use small nodes, whereas all B ɛ -trees use large nodes. In a DAM, all IOs, and hence all nodes, are the same size. In this article, we show that the affine and PDAM models, which are small refinements of the DAM model, yield a surprisingly large improvement in predictability without sacrificing ease of use. We present benchmarks on a large collection of storage devices showing that the affine and PDAM models give good approximations of the performance characteristics of hard drives and SSDs, respectively. We show that the affine model explains node-size choices in B-trees and B ɛ -trees. Furthermore, the models predict that B-trees are highly sensitive to variations in the node size, whereas B ɛ -trees are much less sensitive. These predictions are born out empirically. Finally, we show that in both the affine and PDAM models, it pays to organize data structures to exploit varying IO size. In the affine model, B ɛ -trees can be optimized so that all operations are simultaneously optimal, even up to lower-order terms. In the PDAM model, B ɛ -trees (or B-trees) can be organized so that both sequential and concurrent workloads are handled efficiently. We conclude that the DAM model is useful as a first cut when designing or analyzing an algorithm or data structure but the affine and PDAM models enable the algorithm designer to optimize parameter choices and fill in design details.

Optimization of correlate subquery based on distributed database

Subquery is widely used in database. It can be divided into related subquery and non-related subquery according to whether it is dependent on the table of the parent query. For related subqueries, it is necessary to take a tuple from the parent query before executing the subquery, that is, the content of the subquery needs to be repeatedly operated. Disk access costs of this strategy is very big, in the distributed database, because of data communication overhead, in the parent query yuan set is too low efficiency, therefore, for the class sub queries, on the basis of the optimization of the existing query strategy, combining with the characteristics of distributed database, put forward by the subquery on to join queries, eliminate redundant clauses in the subquery, eliminate accumulation function method based on distributed database query optimization strategy, and the effectiveness of the present optimization strategy is verified by experiment.

Searching using B/B+ Tree in Database Management System

This is a research-based project and the basic point motivating this project is learning and implementing algorithms that reduce time and space complexity. In the first part of the project, we reduce the time taken to search a given record by using a B/B+ tree rather than indexing and traditional sequential access. It is concluded that disk-access times are much slower than main memory access times. Typical seek times and rotational delays are of the order of 5 to 6 milliseconds and typical data transfer rates are of the range of 5 to 10 million bytes per second and therefore, main memory access times are likely to be at least 4 or 5 orders of magnitude faster than disk access on any given system. Therefore, the objective is to minimize the number of disk accesses, and thus, this project is concerned with techniques for achieving that objective i.e. techniques for arranging the data on a disk so that any required piece of data, say some specific record, can be located in a few I/O’s as possible. In the second part of the project, Dynamic Programming problems were solved with Recursion, Recursion With Storage, Iteration with Storage, Iteration with Smaller Storage. The problems which have been solved in these 4 variations are Fibonacci, Count Maze Path, Count Board Path, and Longest Common Subsequence. All 4 variations are an improvement over one another and thus time and space complexity are reduced significantly as we go from Recursion to Iteration with Smaller Storage.

Memory and I/O optimized rectilinear steiner minimum tree routing for VLSI

As the size of devices are scaling down at rapid pace, the interconnect delay play a major part in performance of IC chips. Therefore minimizing delay and wire length is the most desired objective. FLUTE (Fast Look-Up table) presented a fast and accurate RSMT (Rectilinear Steiner Minimum Tree) construction for both smaller and higher degree net. FLUTE presented an optimization technique that reduces time complexity for RSMT construction for both smaller and larger degree nets. However for larger degree net this technique induces memory overhead, as it does not consider the memory requirement in constructing RSMT. Since availability of memory is very less and is expensive, it is desired to utilize memory more efficiently which in turn results in reducing I/O time (i.e. reduce the number of I/O disk access). The proposed work presents a Memory Optimized RSMT (MORSMT) construction in order to address the memory overhead for larger degree net. The depth-first search and divide and conquer approach is adopted to build a Memory optimized tree. Experiments are conducted to evaluate the performance of proposed approach over existing model for varied benchmarks in terms of computation time, memory overhead and wire length. The experimental results show that the proposed model is scalable and efficient.

Memory and I/O Optimized Rectilinear Steiner Minimum Tree Routing For VLSI

As the size of devices are scaling down at rapid pace, the interconnect delay play a major part in performance of IC chips. Therefore minimizing delay and wire length is the most desired objective. FLUTE (Fast Look-Up table) presented a fast and accurate RSMT (Rectilinear Steiner Minimum Tree) construction for both smaller and higher degree net. In this paper, FLUTE presented an optimization technique that reduces time complexity for RSMT construction for both smaller and larger degree nets. However for larger degree net this technique induces memory overhead, as it does not consider the memory requirement in constructing RSMT. Since availability of memory is very less and is expensive, it is desired to utilize memory more efficiently which in turn results in reducing I/O time (i.e. reduce the number of I/O disk access). The proposed work presents a Memory Optimized RSMT (MORSMT) construction in order to address the memory overhead for larger degree net. The depth-first search and divide and conquer approach is adopted to build a Memory optimized tree. Experiments are conducted to evaluate the performance of proposed approach over existing model for varied benchmarks in term of computation time, memory overhead and wire length. The experimental results show that the proposed model is scalable and efficient.

Cost-Based Query Optimization in Centralized Relational Databases

Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use. In this paper, cost is considered by counting number of disk accesses for each query plan because disk access tends to be the dominant cost in query processing for centralized relational databases.

IMPLEMENTATION AND EVALUATION OF RUNTIME DATA DECLUSTERING METHOD OVER SAN-CONNECTED PC CLUSTER

In this paper, a PC cluster connected with Storage Area Network (SAN) is built and evaluated. In the case of SAN­connected cluster, each node can access all shared disks directly without LAN; thus, SAN­connected clusters achieve better performance than LAN­connected clusters for disk access operations. However, if a lot of nodes access the same­shared disk simultaneously, application performance degrades due to I/O­bottleneck. A runtime data declustering method, in which data is declustered to several other disks dynamically during the execution of application, is proposed to resolve this problem. Parallel data mining is implemented and evaluated on the SAN­connected PC cluster. This application requires iterative scans of a shared disk, which degrade execution performance severely due to I/O­bottleneck. The runtime data declustering method is applied to this case. According to the results of experiments, the proposed method prevents performance degradation caused by shared disk bottleneck in SAN­connected clusters.