Interactive data analysis: the Control project

Hellerstein, Joe; Avnur, Ron; Chou, Annie Y. H.; Hidber, Christian; Olston, Christopher; Raman, V.; Roth, Tali; Haas, Peter J.

doi:10.1109/2.781635

Cited by 154 publications

(77 citation statements)

References 6 publications

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“…Current Cloud environments lack this interactive process, and techniques should be developed to facilitate interactivity and to include analysts in the loop by providing means to reduce their time to insight. Systems and techniques that iteratively refine answers to queries and give users more control of processing are desired [139].…”

Section: Other Challengesmentioning

confidence: 99%

Big Data computing and clouds: Trends and future directions

Assunção

Calheiros

Bianchi

et al. 2015

Journal of Parallel and Distributed Computing

658

318

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This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four important areas of analytics and Big Data, namely (i) data management and supporting architectures; (ii) model development and scoring; (iii) visualisation and user interaction; and (iv) business models. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions.

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Section: Other Challengesmentioning

confidence: 99%

Big Data computing and clouds: Trends and future directions

Assunção

Calheiros

Bianchi

et al. 2015

Journal of Parallel and Distributed Computing

658

318

View full text Add to dashboard Cite

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“…21 An implementation of LESS requires integration with an external sort routine which, in turn, requires at least simplified buffer pool and diskspace managers. Shiprec is implemented to use page-based, nonblocking reads and writes, and doublebuffering.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In [28], a useful categorization of existing skyline algorithms is presented (which follows on the work in [21,24]). They use the criteria enumerated below to characterize the behavior and applicability of the algorithms.…”

mentioning

confidence: 99%

Algorithms and analyses for maximal vector computation

2006

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The maximal vector problem is to identify the maximals over a collection of vectors. This arises in many contexts and, as such, has been well studied. The problem recently gained renewed attention with skyline queries for relational databases and with work to develop skyline algorithms that are external and relationally well behaved.While many algorithms have been proposed, how they perform has been unclear. We study the performance of, and design choices behind, these algorithms. We prove runtime bounds based on the number of vectors n and the dimensionality k. Early algorithms based on divide-and-conquer established seemingly good average and worst-case asymptotic runtimes. In fact, the problem can be solved in O(n) average-case (holding k as fixed). We prove, however, that the performance is quite bad with respect to k. We demonstrate that the more recent skyline algorithms are better behaved, and can also achieve O(kn) averagecase. While k matters for these, in practice, its effect vanishes in the asymptotic. We introduce a new external algorithm, LESS, that is more efficient and better behaved. We evaluate LESS's effectiveness and improvement over the field, and prove that its average-case running time is O(kn).

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“…By limiting the choices of useful ranking functions, we are left with incremental results that are not as well-ranked compared to the results of commercial search engines. To alleviate this shortcoming, we propose the use of midquery relevance feedback [12] that allows users to control and change the order in which the posting list intersections are performed. This leads to potential improvements in user experiences, and may result in earlier query termination.…”

Section: Compromising Results Qualitymentioning

confidence: 99%

On the Feasibility of Peer-to-Peer Web Indexing and Search

Li¹,

Loo

Hellerstein

et al. 2003

Peer-to-Peer Systems II

203

180

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This paper discusses the feasibility of peer-to-peer full-text keyword search of the Web. Two classes of keyword search techniques are in use or have been proposed: flooding of queries over an overlay network (as in Gnutella), and intersection of index lists stored in a distributed hash table. We present a simple feasibility analysis based on the resource constraints and search workload. Our study suggests that the peer-to-peer network does not have enough capacity to make naive use of either of search techniques attractive for Web search. The paper presents a number of existing and novel optimizations for P2P search based on distributed hash tables, estimates their effects on performance, and concludes that in combination these optimizations would bring the problem to within an order of magnitude of feasibility. The paper suggests a number of compromises that might achieve the last order of magnitude. Comments Postprint version. Published in AbstractThis paper discusses the feasibility of peer-to-peer full-text keyword search of the Web. Two classes of keyword search techniques are in use or have been proposed: flooding of queries over an overlay network (as in Gnutella), and intersection of index lists stored in a distributed hash table. We present a simple feasibility analysis based on the resource constraints and search workload. Our study suggests that the peer-to-peer network does not have enough capacity to make naive use of either of search techniques attractive for Web search. The paper presents a number of existing and novel optimizations for P2P search based on distributed hash tables, estimates their effects on performance, and concludes that in combination these optimizations would bring the problem to within an order of magnitude of feasibility. The paper suggests a number of compromises that might achieve the last order of magnitude.

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Interactive data analysis: the Control project

Cited by 154 publications

References 6 publications

Big Data computing and clouds: Trends and future directions

Big Data computing and clouds: Trends and future directions

Algorithms and analyses for maximal vector computation

On the Feasibility of Peer-to-Peer Web Indexing and Search

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