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Data access

About: Data access is a research topic. Over the lifetime, 13141 publications have been published within this topic receiving 172859 citations. The topic is also known as: Data access.


Papers
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Proceedings ArticleDOI
12 Jun 2011
TL;DR: This work proposes DIVGEN, an efficient algorithm for diversity-aware search, which achieves significant performance improvements via novel data access primitives, and devise the first low-overhead data access prioritization scheme with theoretical quality guarantees, and good performance in practice.
Abstract: Typical approaches of ranking information in response to a user's query that return the most relevant results ignore important factors contributing to user satisfaction; for instance, the contents of a result document may be redundant given the results already examined. Motivated by emerging applications, in this work we study the problem of Diversity-Aware Search, the essence of which is ranking search results based on both their relevance, as well as their dissimilarity to other results reported.Diversity-Aware Search is generally a hard problem, and even tractable instances thereof cannot be efficiently solved by adapting existing approaches. We propose DIVGEN, an efficient algorithm for diversity-aware search, which achieves significant performance improvements via novel data access primitives. Although selecting the optimal schedule of data accesses is a hard problem, we devise the first low-overhead data access prioritization scheme with theoretical quality guarantees, and good performance in practice. A comprehensive evaluation on real and synthetic large-scale corpora demonstrates the efficiency and effectiveness of our approach.

123 citations

Journal ArticleDOI
TL;DR: The network and threat models of the authentication schemes for cloud-driven IoT-based big data environment, which provides important real-time event processing in critical scenarios like surveillance and monitoring of an industrial plant, are discussed.

122 citations

Patent
09 Oct 2003
TL;DR: In this paper, a system is described for translating access requests between a format suitable for a relational database and a different format used by an application, when the system receives a data access request from the application, the system accesses a mapping catalog and translates the request based on the mapping catalog.
Abstract: A system is disclosed for translating access requests between a format suitable for a relational database and a different format used by an application. When the system receives a data access request from the application, the system accesses a mapping catalog and translates the request based on the mapping catalog. The translated request is then provided to the relational database.

122 citations

Patent
19 Dec 2001
TL;DR: The Data Socket client as discussed by the authors allows the user or program to access any data source available on the user's machine as well as data anywhere on a network, such as a LAN, WAN or the Internet.
Abstract: A Data Socket client and associated applications and/or tools which provide programs with access to data from various sources and having various types or formats, wherein the access is provided invisibly to the user. The Data Socket client allows the user or program to access any data source available on the user's machine as well as data anywhere on a network, such as a LAN, WAN or the Internet. In the preferred embodiment, the Data Socket client addresses data sources or I/O sources using a URL (uniform resource locator), much the way that a URL is used to address web pages anywhere in the world. The present invention also includes new Data Socket URLs which allow the user to access I/O sources.

121 citations

Proceedings ArticleDOI
06 Jun 2010
TL;DR: In DataPath, queries do not request data, and data are automatically pushed onto processors, where they are then processed by any interested computation, making for a very lean and fast database system.
Abstract: Since the 1970's, database systems have been "compute-centric". When a computation needs the data, it requests the data, and the data are pulled through the system. We believe that this is problematic for two reasons. First, requests for data naturally incur high latency as the data are pulled through the memory hierarchy, and second, it makes it difficult or impossible for multiple queries or operations that are interested in the same data to amortize the bandwidth and latency costs associated with their data access. In this paper, we describe a purely-push based, research prototype database system called DataPath. DataPath is "data-centric". In DataPath, queries do not request data. Instead, data are automatically pushed onto processors, where they are then processed by any interested computation. We show experimentally on a multi-terabyte benchmark that this basic design principle makes for a very lean and fast database system.

121 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202351
2022125
2021403
2020721
2019906
2018816