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  • 1
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2010
    In:  IEEE Transactions on Knowledge and Data Engineering Vol. 22, No. 8 ( 2010-08), p. 1142-1157
    In: IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers (IEEE), Vol. 22, No. 8 ( 2010-08), p. 1142-1157
    Type of Medium: Online Resource
    ISSN: 1041-4347
    RVK:
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2010
    detail.hit.zdb_id: 1001468-8
    detail.hit.zdb_id: 2026620-0
    SSG: 24,1
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  • 2
    Online Resource
    Online Resource
    Elsevier BV ; 2017
    In:  Theoretical Computer Science Vol. 683 ( 2017-06), p. 22-30
    In: Theoretical Computer Science, Elsevier BV, Vol. 683 ( 2017-06), p. 22-30
    Type of Medium: Online Resource
    ISSN: 0304-3975
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2017
    detail.hit.zdb_id: 193706-6
    detail.hit.zdb_id: 1466347-8
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  • 3
    Online Resource
    Online Resource
    Elsevier BV ; 2014
    In:  Theoretical Computer Science Vol. 552 ( 2014-10), p. 44-51
    In: Theoretical Computer Science, Elsevier BV, Vol. 552 ( 2014-10), p. 44-51
    Type of Medium: Online Resource
    ISSN: 0304-3975
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2014
    detail.hit.zdb_id: 193706-6
    detail.hit.zdb_id: 1466347-8
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  • 4
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2013
    In:  ACM SIGKDD Explorations Newsletter Vol. 14, No. 2 ( 2013-04-30), p. 59-62
    In: ACM SIGKDD Explorations Newsletter, Association for Computing Machinery (ACM), Vol. 14, No. 2 ( 2013-04-30), p. 59-62
    Abstract: Just as inspecting the source code of programs tells us a lot about the process of programming, inspecting the "source code" of scientific papers informs on the process of scientific writing. We report on our study of the source of tens of thousands of papers from Computer Science and Mathematics.
    Type of Medium: Online Resource
    ISSN: 1931-0145 , 1931-0153
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2013
    detail.hit.zdb_id: 2082223-6
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  • 5
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2012
    In:  ACM SIGMETRICS Performance Evaluation Review Vol. 40, No. 1 ( 2012-06-07), p. 343-354
    In: ACM SIGMETRICS Performance Evaluation Review, Association for Computing Machinery (ACM), Vol. 40, No. 1 ( 2012-06-07), p. 343-354
    Abstract: Random sampling has been proven time and time again to be a powerful tool for working with large data. Queries over the full dataset are replaced by approximate queries over the smaller (and hence easier to store and manipulate) sample. The sample constitutes a flexible summary that supports a wide class of queries. But in many applications, datasets are modified with time, and it is desirable to update samples without requiring access to the full underlying datasets. In this paper, we introduce and analyze novel techniques for sampling over dynamic data, modeled as a stream of modifications to weights associated with each key. While sampling schemes designed for stream applications can often readily accommodate positive updates to the dataset, much less is known for the case of negative updates, where weights are reduced or items deleted altogether. We primarily consider the turnstile model of streams, and extend classic schemes to incorporate negative updates. Perhaps surprisingly, the modifications to handle negative updates turn out to be natural and seamless extensions of the well-known positive update-only algorithms. We show that they produce unbiased estimators, and we relate their performance to the behavior of corresponding algorithms on insert-only streams with different parameters. A careful analysis is necessitated, in order to account for the fact that sampling choices for one key now depend on the choices made for other keys. In practice, our solutions turn out to be efficient and accurate. Compared to recent algorithms for L p sampling which can be applied to this problem, they are significantly more reliable, and dramatically faster.
    Type of Medium: Online Resource
    ISSN: 0163-5999
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2012
    detail.hit.zdb_id: 199353-7
    detail.hit.zdb_id: 2089001-1
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  • 6
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM SIGMOD Record Vol. 50, No. 1 ( 2021-06-15), p. 5-5
    In: ACM SIGMOD Record, Association for Computing Machinery (ACM), Vol. 50, No. 1 ( 2021-06-15), p. 5-5
    Abstract: Over the past two decades the data management community has devoted particular attention to handling data that arrives as a stream of updates. This captures a number of "big data" scenarios, ranging from monitoring networks to processing high volumes of transactions in commerce and finance. This has led to data streams becoming a mainstream data management topic, with many systems offering explicit support for handling such inputs. Within these systems, streaming algorithms are used to approximate various statistical and modeling queries, which would traditionally require random access to the full data to compute exactly.
    Type of Medium: Online Resource
    ISSN: 0163-5808
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 243829-X
    detail.hit.zdb_id: 2051432-3
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  • 7
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM SIGMOD Record Vol. 51, No. 1 ( 2022-05-31), p. 69-76
    In: ACM SIGMOD Record, Association for Computing Machinery (ACM), Vol. 51, No. 1 ( 2022-05-31), p. 69-76
    Abstract: Estimating ranks, quantiles, and distributions over streaming data is a central task in data analysis and monitoring. Given a stream of n items from a data universe equipped with a total order, the task is to compute a sketch (data structure) of size polylogarithmic in n. Given the sketch and a query item y, one should be able to approximate its rank in the stream, i.e., the number of stream elements smaller than or equal to y.
    Type of Medium: Online Resource
    ISSN: 0163-5808
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 243829-X
    detail.hit.zdb_id: 2051432-3
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  • 8
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2011
    In:  ACM Transactions on Algorithms Vol. 7, No. 2 ( 2011-03), p. 1-20
    In: ACM Transactions on Algorithms, Association for Computing Machinery (ACM), Vol. 7, No. 2 ( 2011-03), p. 1-20
    Abstract: Consider the following problem: We have k players each receiving a stream of items, and communicating with a central coordinator. Let the multiset of items received by player i up until time t be A i ( t ). The coordinator's task is to monitor a given function f computed over the union of the inputs ∪ i A i ( t ), continuously at all times t . The goal is to minimize the number of bits communicated between the players and the coordinator. Of interest is the approximate version where the coordinator outputs 1 if f ≥ τ and 0 if f ≤ (1−ϵ)τ. This defines the ( k , f ,τ,ϵ) distributed functional monitoring problem. Functional monitoring problems are fundamental in distributed systems, in particular sensor networks, where we must minimize communication; they also connect to the well-studied streaming model and communication complexity. Yet few formal bounds are known for functional monitoring. We give upper and lower bounds for the ( k , f ,τ,ϵ) problem for some of the basic f 's. In particular, we study the frequency moments F p for p =0,1,2. For F 0 and F 1 , we obtain monitoring algorithms with cost almost the same as algorithms that compute the function for a single instance of time. However, for F 2 the monitoring problem seems to be much harder than computing the function for a single time instance. We give a carefully constructed multiround algorithm that uses “sketch summaries” at multiple levels of details and solves the ( k , F 2 ,τ,ϵ) problem with communication Õ ( k 2 /ϵ + k 3/2 /ϵ 3 ). Our algorithmic techniques are likely to be useful for other functional monitoring problems as well.
    Type of Medium: Online Resource
    ISSN: 1549-6325 , 1549-6333
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2011
    detail.hit.zdb_id: 2198259-4
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  • 9
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2010
    In:  The VLDB Journal Vol. 19, No. 1 ( 2010-2), p. 3-20
    In: The VLDB Journal, Springer Science and Business Media LLC, Vol. 19, No. 1 ( 2010-2), p. 3-20
    Type of Medium: Online Resource
    ISSN: 1066-8888 , 0949-877X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2010
    detail.hit.zdb_id: 1463009-6
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  • 10
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2005
    In:  ACM Transactions on Database Systems Vol. 30, No. 1 ( 2005-03), p. 249-278
    In: ACM Transactions on Database Systems, Association for Computing Machinery (ACM), Vol. 30, No. 1 ( 2005-03), p. 249-278
    Abstract: Most database management systems maintain statistics on the underlying relation. One of the important statistics is that of the “hot items” in the relation: those that appear many times (most frequently, or more than some threshold). For example, end-biased histograms keep the hot items as part of the histogram and are used in selectivity estimation. Hot items are used as simple outliers in data mining, and in anomaly detection in many applications.We present new methods for dynamically determining the hot items at any time in a relation which is undergoing deletion operations as well as inserts. Our methods maintain small space data structures that monitor the transactions on the relation, and, when required, quickly output all hot items without rescanning the relation in the database. With user-specified probability, all hot items are correctly reported. Our methods rely on ideas from “group testing.” They are simple to implement, and have provable quality, space, and time guarantees. Previously known algorithms for this problem that make similar quality and performance guarantees cannot handle deletions, and those that handle deletions cannot make similar guarantees without rescanning the database. Our experiments with real and synthetic data show that our algorithms are accurate in dynamically tracking the hot items independent of the rate of insertions and deletions.
    Type of Medium: Online Resource
    ISSN: 0362-5915 , 1557-4644
    RVK:
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2005
    detail.hit.zdb_id: 196155-X
    detail.hit.zdb_id: 2006335-0
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