Online querying of d-dimensional hierarchies

Doka, Katerina; Tsoumakos, Dimitrios; Koziris, Nectarios

doi:10.1016/j.jpdc.2010.10.005

Cited by 9 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table (3) includes the cases and the results. As shown in table (2), it can be noted that in the cases 4, [6][7][8][9][10], 11, [14][15][16][17], 19, [20][21][22][23] and 25, the proposed query optimization approach has lower cost than SQL Server 2012 query optimizer. That is the relative cost of the proposed approach to SQL Server 2012 is between 0.38 (in case 4) and 1.03(in case 5).While such relative cost in the cases13,18, 20 and 24 the proposed query optimizer has a very little increase than SQL Server optimizer by just 0.01.…”

Section: Resultsmentioning

confidence: 99%

“…We have to mention that, the query optimization step, [4] is the most critical step in query processing procedure [22], especially for complex queries, which often contains a large number of join operations [2]. Query optimization has two levels, the first level is the logical optimization and the second one concerns with physical optimization process [21].…”

Section: Optimization Processmentioning

confidence: 99%

See 1 more Smart Citation

An Enhanced Ant Colony-based Approach for Query Optimization

Hanafy¹,

Gadallah²,

Hefny³

2017

IJCA

View full text Add to dashboard Cite

One of the mandatory processes for all those types of applications is the inquiry process of the stored huge amounts of data. Such process is either a predefined or an ad-hoc query. From the logical point of view, the query process depends mainly on many algebraic operations, including selection, projection and joining operations. The most important one of them is the join operation, which represents the key factor of the inquiry process to retrieve the related information from different data tables. Many approaches have been proposed aiming to reduce the cost of join operations. Yet, there is still a need for more query optimizing processes in order to reduce the query response time. This paper proposes an enhanced optimal query processing approach for inner and outer join, where the proposed model exploits an adopted Ant Colony Optimization.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Optimization Processmentioning

confidence: 99%

An Enhanced Ant Colony-based Approach for Query Optimization

Hanafy¹,

Gadallah²,

Hefny³

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…This is not possible for large data warehouses. For peer-to-peer networks, related work includes distributed methods for querying concept hierarchies such as [25], [26], [27], [28]. However, none of these methods provides real-time OLAP functionality.…”

Section: Related Workmentioning

confidence: 99%

A distributed tree data structure for real-time OLAP on cloud architectures

Dehne

Kong

Rau-Chaplin

et al. 2013

2013 IEEE International Conference on Big Data

View full text Add to dashboard Cite

Abstract-In contrast to queries for on-line transaction processing (OLTP) systems that typically access only a small portion of a database, OLAP queries may need to aggregate large portions of a database which often leads to performance issues. In this paper we introduce CR-OLAP, a Cloud based Real-time OLAP system based on a new distributed index structure for OLAP, the distributed PDCR tree, that utilizes a cloud infrastructure consisting of (m + 1) multi-core processors. With increasing database size, CR-OLAP dynamically increases m to maintain performance. Our distributed PDCR tree data structure supports multiple dimension hierarchies and efficient query processing on the elaborate dimension hierarchies which are so central to OLAP systems. It is particularly efficient for complex OLAP queries that need to aggregate large portions of the data warehouse, such as "report the total sales in all stores located in California and New York during the months February-May of all years". We evaluated CR-OLAP on the Amazon EC2 cloud, using the TPC-DS benchmark data set. The tests demonstrate that CR-OLAP scales well with increasing number of processors, even for complex queries. For example, on an Amazon EC2 cloud instance with eight processors, for a TPC-DS OLAP query stream on a data warehouse with 80 million tuples where every OLAP query aggregates more than 50% of the database, CR-OLAP achieved a query latency of 0.3 seconds which can be considered a real time response.

show abstract

“…Some works in the field propose distributed warehousing systems [18,4,2], but the warehouse and its aggregation, update and querying functionality remain centralized. Recently, effort has been made to distribute the data warehouse itself by applying techniques from the field of Peer-to-Peer (P2P) computing [12], but with no a priori consideration for group-by queries. Moreover, none of these approaches deals with the query performance versus variable data availability or load skew.…”

Section: Introductionmentioning

confidence: 99%

Brown Dwarf: A fully-distributed, fault-tolerant data warehousing system

Doka

Tsoumakos

Koziris

2011

Journal of Parallel and Distributed Computing

Self Cite

View full text Add to dashboard Cite

a b s t r a c tIn this paper we present the Brown Dwarf, a distributed data analytics system designed to efficiently store, query and update multidimensional data over commodity network nodes, without the use of any proprietary tool. Brown Dwarf distributes a centralized indexing structure among peers on-the-fly, reducing cube creation and querying times by enforcing parallelization. Analytical queries are naturally performed on-line through cooperating nodes that form an unstructured Peer-to-Peer overlay. Updates are also performed on-line, eliminating the usually costly over-night process. Moreover, the system employs an adaptive replication scheme that adjusts to the workload skew as well as the network churn by expanding or shrinking the units of the distributed data structure. Our system has been thoroughly evaluated on an actual testbed: it manages to accelerate cube creation up and querying up to several tens of times compared to the centralized solution by exploiting the capabilities of the available network nodes working in parallel. It also manages to quickly adapt even after sudden bursts in load and remains unaffected with a considerable fraction of frequent node failures. These advantages are even more apparent for dense and skewed data cubes and workloads.

show abstract

Online querying of d-dimensional hierarchies

Cited by 9 publications

References 35 publications

An Enhanced Ant Colony-based Approach for Query Optimization

An Enhanced Ant Colony-based Approach for Query Optimization

A distributed tree data structure for real-time OLAP on cloud architectures

Brown Dwarf: A fully-distributed, fault-tolerant data warehousing system

Contact Info

Product

Resources

About