An adaptive peer-to-peer network for distributed caching of OLAP results
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Efficient OLAP query processing in distributed data warehouses
Information Systems - Special issue: Best papers from EDBT 2002
A Distributed OLAP Infrastructure for E-Commerce
COOPIS '99 Proceedings of the Fourth IECIS International Conference on Cooperative Information Systems
The DC-Tree: A Fully Dynamic Index Structure for Data Warehouses
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Peer-to-peer information retrieval using self-organizing semantic overlay networks
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
QC-trees: an efficient summary structure for semantic OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Querying the internet with PIER
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Enhancing P2P file-sharing with an internet-scale query processor
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
WebContent: efficient P2P Warehousing of web data
Proceedings of the VLDB Endowment
Dwarfs in the rearview mirror: how big are they really?
Proceedings of the VLDB Endowment
GrouPeer: Dynamic clustering of P2P databases
Information Systems
P2P OLAP: Data model, implementation and case study
Information Systems
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Hive: a warehousing solution over a map-reduce framework
Proceedings of the VLDB Endowment
HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads
Proceedings of the VLDB Endowment
Dremel: interactive analysis of web-scale datasets
Proceedings of the VLDB Endowment
Online querying of d-dimensional hierarchies
Journal of Parallel and Distributed Computing
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In 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.