MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the…
Database Systems
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31,710 papers
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There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis 17. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some…
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Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal…
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There has been extensive work in query optimization since the early 70s. It is hard to capture the breadth and depth of this large body of work in a short article. Therefore, I have decided to focus primarily on the optimization of SQL queries in…
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Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and…
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Future users of large data banks must be protected from having to know how the data is organized in the machine (the internal representation). A prompting service which supplies such information is not a satisfactory solution. Activities of users at…
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Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has…
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The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end,…
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Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing,…
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Providing scalable database services is an essential requirement for extending many existing applications of the Cloud platform. Due to the diversity of applications, database services on the Cloud must support large-scale data analytical jobs and…
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Database partitioning is a technique for improving the performance of distributed OLTP databases, since "single partition" transactions that access data on one partition do not need coordination with other partitions. For workloads that are amenable…
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Dremel is a scalable, interactive ad-hoc query system for analy- sis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggrega- tion queries over trillion-row tables in seconds. The…
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There has been a significant amount of excitement and recent work on column-oriented database systems ("column-stores"). These database systems have been shown to perform more than an order of magnitude better than traditional row-oriented database…
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The Deep Web, i.e., content hidden behind HTML forms, has long been acknowledged as a significant gap in search engine coverage. Since it represents a large portion of the structured data on the Web, accessing Deep-Web content has been a…
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Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad application combines computational "vertices" with communication "channels" to form a dataflow graph. Dryad runs the application by…
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MapReduce has been widely used for large-scale data analy- sis in the Cloud. The system is well recognized for its elastic scalability and fine-grained fault tolerance although its per- formance has been noted to be suboptimal in the database…
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Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also…
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MapReduce is a computing paradigm that has gained a lot of at- tention in recent years from industry and research. Unlike paral- lel DBMSs, MapReduce allows non-expert users to run complex analytical tasks over very large data sets on very large…
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Cloud computing promises a number of advantages for the deployment of data-intensive applications. One important promise is reduced cost with a pay-as-you-go business model. Another promise is (virtually) unlimited throughput by adding servers if…
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