research-article

Towards benchmarking stream data warehouses

Authors:

Arian Bär,

Lukasz GolabAuthors Info & Claims

DOLAP '12: Proceedings of the fifteenth international workshop on Data warehousing and OLAP

Pages 105 - 112

https://doi.org/10.1145/2390045.2390062

Published: 02 November 2012 Publication History

Get Access

Abstract

Data management systems are facing two challenges driven by the requirements of emerging data-intensive applications: more data and less time to process the data. Data volumes continue to increase as new sources and data collecting mechanisms appear. At the same time, these sources tend to be highly dynamic and generate data in the form of a stream, which requires quick reaction to newly arrived data. Traditional data warehouses enable scalable data storage and analytics, including the ability to define nested levels of materialized views. However, views are typically refreshed during downtimes---e.g., every night---which does not meet the latency requirements of many applications. Stream data warehousing is a new data management technology that allows nearly-continuous view refresh as new data arrive, which enables seamless integration of real-time monitoring and business intelligence with long-term data mining. In this paper, we argue that a new benchmark is required for stream warehouses, which should focus on measuring the property that determines the utility of these systems, namely how well they can keep up with the incoming data and guarantee the "freshness" of materialized views.

References

[1]

Brad Adelberg, Hector Garcia-Molina, Ben Kao: Applying Update Streams in a Soft Real-Time Database System. SIGMOD Conference 1995: 245--256

Digital Library

Google Scholar

[2]

Mona Ahuja, Cheng Che Chen, Ravi Gottapu, Jorg Hallmann, Waqar Hasan, Richard Johnson, Maciek Kozyrczak, Ramesh Pabbati, Neeta Pandit, Sreenivasulu Pokuri, Krishna Uppala: Peta-scale data warehousing at Yahoo! SIGMOD Conference 2009: 855--862

Digital Library

Google Scholar

[3]

Arvind Arasu, Mitch Cherniack, Eduardo F. Galvez, David Maier, Anurag Maskey, Esther Ryvkina, Michael Stonebraker, Richard Tibbetts: Linear Road: A Stream Data Management Benchmark. VLDB 2004: 480--491

Digital Library

Google Scholar

[4]

Arian Baer, Antonio Barbuzzi, Pietro Michiardi, Fabio Ricciato: Two parallel approaches to network data analysis, 5th Workshop on Large Scale Distributed Systems and Middleware (LADIS), 2011

Google Scholar

[5]

Magdalena Balazinska, YongChul Kwon, Nathan Kuchta, Dennis Lee: Moirae: History-Enhanced Monitoring. CIDR 2007: 375--386

Google Scholar

[6]

MohammadHossein Bateni, Lukasz Golab, MohammadTaghi Hajiaghayi, Howard J. Karloff: Scheduling to Minimize Staleness and Stretch in Real-Time Data Warehouses. Theory Comput. Syst. 49(4): 757--780 (2011)

Digital Library

Google Scholar

[7]

Badrish Chandramouli, Jonathan Goldstein, Songyun Duan: Temporal Analytics on Big Data for Web Advertising. ICDE 2012: 90--101

Digital Library

Google Scholar

[8]

Junghoo Cho, Hector Garcia-Molina: Synchronizing a Database to Improve Freshness. SIGMOD Conference 2000: 117--128

Digital Library

Google Scholar

[9]

Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, Russell Sears: Benchmarking cloud serving systems with YCSB. SoCC 2010: 143--154

Digital Library

Google Scholar

[10]

Jeffrey Dean, Sanjay Ghemawat: MapReduce: a flexible data processing tool. Commun. ACM 53(1): 72--77 (2010)

Digital Library

Google Scholar

[11]

Nathan Folkert, Abhinav Gupta, Andrew Witkowski, Sankar Subramanian, Srikanth Bellamkonda, Shrikanth Shankar, Tolga Bozkaya, Lei Sheng: Optimizing Refresh of a Set of Materialized Views. VLDB 2005: 1043--1054

Digital Library

Google Scholar

[12]

Michael J. Franklin, Sailesh Krishnamurthy, Neil Conway, Alan Li, Alex Russakovsky, Neil Thombre: Continuous Analytics: Rethinking Query Processing in a Network-Effect World. CIDR 2009

Google Scholar

[13]

Lukasz Golab, Theodore Johnson: Consistency in a Stream Warehouse. CIDR 2011: 114--122

Google Scholar

[14]

Lukasz Golab, Theodore Johnson, J. Spencer Seidel, Vladislav Shkapenyuk: Stream warehousing with DataDepot. SIGMOD Conference 2009: 847--854

Digital Library

Google Scholar

[15]

Lukasz Golab, Theodore Johnson, Subhabrata Sen, Jennifer Yates: A Sequence-Oriented Stream Warehouse Paradigm for Network Monitoring Applications. PAM 2012: 53--63

Digital Library

Google Scholar

[16]

Lukasz Golab, Theodore Johnson, Vladislav Shkapenyuk, Scalable Scheduling of Updates in Streaming Data Warehouses. IEEE Trans. Knowl. Data Eng. (TKDE) 24(6):1092--1105

Digital Library

Google Scholar

[17]

Lukasz Golab, M. Tamer Ozsu: Data Stream Management. Morgan & Claypool Publishers, 2010

Digital Library

Google Scholar

[18]

Matteo Golfarelli, Stefano Rizzi: Data Warehouse Testing. IJDWM 7(2): 26--43 (2011)

Digital Library

Google Scholar

[19]

Sailesh Krishnamurthy, Michael J. Franklin, Jeffrey Davis, Daniel Farina, Pasha Golovko, Alan Li, Neil Thombre: Continuous analytics over discontinuous streams. SIGMOD Conference 2010: 1081--1092

Digital Library

Google Scholar

[20]

Wilburt Labio, Ramana Yerneni, Hector Garcia-Molina: Shrinking the Warehouse Update Window. SIGMOD Conference 1999: 383--394

Digital Library

Google Scholar

[21]

AWI Working Group Traffic Archive http://mawi.wide.ad.jp/mawi/

Google Scholar

[22]

Marcelo R. N. Mendes, Pedro Bizarro, Paulo Marques: A Performance Study of Event Processing Systems. TPCTC 2009: 221--236

Digital Library

Google Scholar

[23]

M. Asif Naeem, Gillian Dobbie, Gerald Weber: HYBRIDJOIN for Near-Real-Time Data Warehousing. IJDWM 7(4): 21--42 (2011)

Digital Library

Google Scholar

[24]

M. Asif Naeem, Gillian Dobbie, Gerald Weber, Shafiq Alam: R-MESHJOIN for near-real-time data warehousing. DOLAP 2010: 53--60

Digital Library

Google Scholar

[25]

Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J. Abadi, David J. DeWitt, Samuel Madden, Michael Stonebraker: A comparison of approaches to large-scale data analysis. SIGMOD Conference 2009: 165--178

Digital Library

Google Scholar

[26]

Neoklis Polyzotis, Spiros Skiadopoulos, Panos Vassiliadis, Alkis Simitsis, Nils-Erik Frantzell: Meshing Streaming Updates with Persistent Data in an Active Data Warehouse. IEEE Trans. Knowl. Data Eng. 20(7): 976--991 (2008)

Digital Library

Google Scholar

[27]

Frederick Reiss, Kurt Stockinger, Kesheng Wu, Arie Shoshani, Joseph M. Hellerstein: Enabling Real-Time Querying of Live and Historical Stream Data. SSDBM 2007: 28

Digital Library

Google Scholar

[28]

Alkis Simitsis, Panos Vassiliadis, Umeshwar Dayal, Anastasios Karagiannis, Vasiliki Tziovara: Benchmarking ETL Workflows. TPCTC 2009: 199--220

Digital Library

Google Scholar

[29]

Christian Thomsen, Torben Bach Pedersen, Wolfgang Lehner: RiTE: Providing On-Demand Data for Right-Time Data Warehousing. ICDE 2008: 456--465

Digital Library

Google Scholar

[30]

PC-DS benchmark. Transaction Processing Council,\\ http://www.tpc.org/tpcds/default.asp

Google Scholar

[31]

Kristin Tufte, Jin Li, David Maier, Vassilis Papadimos, Robert L. Bertini, James Rucker: Travel time estimation using NiagaraST and latte. SIGMOD Conference 2007: 1091--1093

Digital Library

Google Scholar

Cited By

View all

Gorawski MPasterak KGorawska AGorawski M(2023)The stream data warehouseFuture Generation Computer Systems10.1016/j.future.2023.01.003142:C(212-227)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.future.2023.01.003
Darmont J(2014)Database Processing BenchmarksEncyclopedia of Information Science and Technology, Third Edition10.4018/978-1-4666-5888-2.ch167(1741-1747)Online publication date: 31-Jul-2014
https://doi.org/10.4018/978-1-4666-5888-2.ch167
Golfarelli MSong IChen XLebanon GWang HZaki M(2012)DOLAP 2012 workshop summaryProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398765(2780-2781)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398765

Index Terms

Towards benchmarking stream data warehouses
1. General and reference
  1. Cross-computing tools and techniques
2. Information systems
  1. Data management systems
    1. Information integration
      1. Data warehouses
  2. Information systems applications
    1. Decision support systems
      1. Data warehouses

Recommendations

Benchmarking data warehouses

Database benchmarks can either help users in comparing the performances of different systems, or help engineers in testing the effect of various design choices. In the field of data warehouses, the Transaction Processing Performance Council's standard ...
Making views self-maintainable for data warehousing
DIS '96: Proceedings of the fourth international conference on on Parallel and distributed information systems

A data warehouse stores materialized views over data from one or more sources in order to provide fast access to the integrated data, regardless of the availability of the data sources. Warehouse views need to be maintained in response to changes to the ...
Designing Data Warehouses for Supply Chain Management
CEC '04: Proceedings of the IEEE International Conference on E-Commerce Technology

Data warehouses are used to support subject-oriented decision-making in a company. When a company joins a supply chain partnership to increase competitiveness, its data warehouse has to be re-designed. The design of data warehouses for supply chain ...

Reviews

Reviewer: Amos O Olagunju

The conduct of business continues to change rapidly as new technologies and techniques evolve. One result of this change is that enterprises and governments sometimes find themselves maintaining historical business data in very large databases without adequate time-sensitive data mining algorithms. A reliable framework for data mining multidimensional graphs of heterogeneous information networks exists for the familiar online analytical processing (OLAP) technology [1]. However, effective business decisions can still benefit from intelligent systems that are capable of processing new real-time transactions by taking advantage of the historical business patterns in data warehouses. How often should an effective data warehouse be refreshed to accommodate new incoming business transactions__?__ How should businesses gauge the effectiveness of continuously updated data warehouses that provide business intelligence__?__ The authors of this paper propose a set of ideas for testing the performance of data warehouses that support almost instantaneous refreshing of table views as new data arrives. The framework for benchmarking data warehouses that restore table views in practically real-time involves two steps. First, the authors assess the latency between the incoming fresh business data and the propagation of this data through the base tables and into views. From this, they compute the "staleness" associated with processing alternative hierarchies of tables and real views of workloads containing different queries and batch window data sizes. This results in a near real-time staleness value for new transactions over a given period, which is the sum of all areas under the staleness value graph lines. The metric for investigating the performance of transaction processing systems for data warehouses is the ratio of total queries multiplied by the gigabyte rate of the transaction processing system, relative to the total load time of transaction data plus its power test consumption and the total execution times for testing different queries for insertion, deletion, and addition operations. The authors comprehensively review the literature to ascertain the credibility of bid metrics for assessing the performance of data warehouses that almost instantaneously update tables and provide real views of incoming transaction data. They use a hypothetical situation to illustrate the computation of the staleness value, and propose future practical and simulation experiments to validate metrics for stream data warehouses. Clearly, the authors open up an interesting, long-overdue discussion on the reliability of the data warehouses that data mining algorithms operate on. Until new prototype systems emerge that can measure the reliability of stream data warehouses, the practical applications of the metrics advocated by the authors will remain doubtful and open to discussion. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

DOLAP '12: Proceedings of the fifteenth international workshop on Data warehousing and OLAP

November 2012

154 pages

ISBN:9781450317214

DOI:10.1145/2390045

General Chair:
Il-Yeol Song
Drexel University, USA
,
Program Chair:
Matteo Golfarelli
University of Bologna, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM'12

Sponsor:

CIKM'12: 21st ACM International Conference on Information and Knowledge Management

November 2, 2012

Hawaii, Maui, USA

Acceptance Rates

Overall Acceptance Rate 29 of 79 submissions, 37%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
471
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Gorawski MPasterak KGorawska AGorawski M(2023)The stream data warehouseFuture Generation Computer Systems10.1016/j.future.2023.01.003142:C(212-227)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.future.2023.01.003
Darmont J(2014)Database Processing BenchmarksEncyclopedia of Information Science and Technology, Third Edition10.4018/978-1-4666-5888-2.ch167(1741-1747)Online publication date: 31-Jul-2014
https://doi.org/10.4018/978-1-4666-5888-2.ch167
Golfarelli MSong IChen XLebanon GWang HZaki M(2012)DOLAP 2012 workshop summaryProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398765(2780-2781)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398765

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Benchmarking data warehouses

Making views self-maintainable for data warehousing

Designing Data Warehouses for Supply Chain Management

Reviews

Access critical reviews of Computing literature here