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Querying Combined Cloud-Based and Relational Databases
2011 (English)Conference paper (Refereed)
Abstract [en]

An increasing amount of data is stored in cloud repositories, which provide high availability, accessibility, and scalability. However, for security reasons enterprises often need to store the core proprietary data in their own relational databases, while common data to be widely available can be stored in a cloud data repository. For example, the subsidiaries of a global enterprise are located in different geographic places where each subsidiary is likely to maintain its own local database. In such a scenario, data integration among the local databases and the cloud-based data is inevitable. We have developed a system called BigIntegrator to enable general queries that combine data in cloud-based data stores with relational databases. We present the design and working principle of the system. A scenario of querying data from both kinds of data sources is used as illustration. The system is general and extensible to integrate data from different kinds of data sources. A particular challenge being addressed is the limited query capabilities of cloud data stores. BigIntegrator utilizes knowledge of those limitations to produce efficient query execution.

Place, publisher, year, edition, pages
2011. 330-335 p.
Keyword [en]
cloud data repository; relational database; data integration; Bigtable;
National Category
Computer and Information Science
URN: urn:nbn:se:uu:diva-275026DOI: 10.1109/CSC.2011.6138543OAI: oai:DiVA.org:uu-275026DiVA: diva2:898476
2011 International Conference on Cloud and Service Computing (CSC)
Available from: 2016-01-28 Created: 2016-01-28 Last updated: 2016-03-09
In thesis
1. Scalable Queries over Log Database Collections
Open this publication in new window or tab >>Scalable Queries over Log Database Collections
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In industrial settings, machines such as trucks, hydraulic pumps, etc. are widely distributed at different geographic locations where sensors on machines produce large volumes of data. The data produced is stored locally in autonomous databases called log databases. The collection of log databases is dynamically changing when new sites are dynamically added or removed from the federation.

In this application context, an efficient way to search and analyze passed behavior of products in use is desired. To enable scalable queries over collections of distributed and autonomous log databases we developed the FLOQ (Fused LOg database Query processor) system, which provides a global view of the working status of all machines on the sites through a meta-database integrating the dynamic log database collection. A particular challenge in this scenario is a scalable way to process numerical queries that identify anomalies by joining data from the meta-database with data selected from the collection of distributed and autonomous log databases. The Thesis describes the architecture of FLOQ. In particular different strategies to execute numerical queries over log database collections are investigated. FLOQ allows both the meta-database and the log databases to be stored in multiple formats using different kinds of data managers. FLOQ provides general and extensible mechanisms for efficient processing of queries over different kinds of distributed data sources.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 51 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1343
National Category
Computer Science
Research subject
Computer Science with specialization in Database Technology
urn:nbn:se:uu:diva-275044 (URN)978-91-554-9472-8 (ISBN)
Public defence
2016-03-30, 2446, Department of Information Technology, Polacksbacken (Lägerhyddsvägen 2), Uppsala, 13:00 (English)
Available from: 2016-03-03 Created: 2016-01-28 Last updated: 2016-03-03Bibliographically approved

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Zhu, Minpeng
Computer and Information Science

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