Grid Query Research Articles

An Approach for Detecting Local Outliers in Grid Queries

The density local outlier factor algorithm (LOF) needs to calculate the distance matrix for k-nearest neighbor search. The algorithm has high time complexity and is not suitable for the detection of large-scale data sets. A local outlier detection algorithm is proposed based on grid query (LOGD). In the algorithm, the k other data points closest to the data point in the target grid must be in the target grid or in the nearest neighboring grid of the target grid, it is used to improve the neighborhood query operation of the LOF algorithm, the calculation amount of the LOF algorithm is reduced in the neighborhood query. Experimental results show that the proposed LODG algorithm can effectively reduce the time of outlier detection under the condition, the detection accuracy of the original LOF algorithm is basically the same.

Grid query optimizer to improve query processing in grids

The emergence of computational grids, as global computing infrastructures, calls for development of new and advanced database techniques. While there exist algorithms and tools that facilitate database operations in grids, currently query optimization techniques are scant. In this paper, a query optimization technique, Grid Query Optimizer (GQO), that improves overall response time for grid-based query processing is presented. GQO features a resource selection strategy and a generic parallelism processing algorithm to balance optimization cost and query execution. GQO is tested using a simulated grid environment and compared with two other optimization techniques. Experiment results show that GQO provides better-than-average performance and is especially suitable for queries with large search spaces.

Dynamic Query Optimization Approach for Semantic Database Grid

Fundamentally, semantic grid database is about bringing globally distributed databases together in order to coordinate resource sharing and problem solving in which information is given well-defined meaning, and DartGrid II is the implemented database gird system whose goal is to provide a semantic solution for integrating database resources on the Web. Although many algorithms have been proposed for optimizing query-processing in order to minimize costs and/or response time, associated with obtaining the answer to query in a distributed database system, database grid query optimization problem is fundamentally different from traditional distributed query optimization. These differences are shown to be the consequences of autonomy and heterogeneity of database nodes in database grid. Therefore, more challenges have arisen for query optimization in database grid than traditional distributed database. Following this observation, the design of a query optimizer in DartGrid II is presented, and a heuristic, dynamic and parallel query optimization approach to processing query in database grid is proposed. A set of semantic tools supporting relational database integration and semantic-based information browsing has also been implemented to realize the above vision.

ADVANCED TECHNOLOGIES FOR DISTRIBUTED DATABASE SERVICES HYPERINFRASTRUCTURE

HEP collaborations are deploying grid technologies to address petabyte-scale data processing challenges. In addition to file-based event data, HEP data processing requires access to terabytes of non-event data (detector conditions, calibrations, etc.) stored in relational databases. Inadequate for non-event data delivery in these amounts, database access control technologies for grid computing are limited to encrypted message transfers. To overcome these database access limitations one must go beyond the existing grid infrastructure. A proposed hyperinfrastructure of distributed database services implements efficient secure data access methods. We introduce several technologies laying a foundation of a new hyperinfrastructure. We present efficient secure data transfer methods and secure grid query engine technologies federating heterogeneous databases. Lessons learned in a production environment of ATLAS Data Challenges are presented.