Time-Decaying Bloom Filters for Data Streams with Skewed Distributions

Cheng, Kai; Xiang, Liming; Iwaihara, Mizuho

doi:10.1109/ride.2005.15

Cited by 35 publications

(23 citation statements)

References 12 publications

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“…In this section, we first revisit the Time-decaying Bloom Filter (TBF) construction introduced in [5]. Its windowbased operation exhibits two notable shortcomings: i) a bias occurring when results are read at arbitrary time instants, and, perhaps more crucial in our application' scenario, ii) severe performance limitations posed by the need to periodically decrement all bins at once.…”

Section: On-demand Time-decaying Bloom Filtermentioning

confidence: 99%

“…For sake of clarity we present such construction independently of [5]. Specifically, we first derive it for a single (exact, per-flow) counter case, we then extend it to a Bloom-type filter construction, and we finally discuss the emerging issues and limitations.…”

Section: Window-based Tbfmentioning

confidence: 99%

“…It is straightforward to generalize the approach presented above to the multiple counters' Counting Bloom filter's case, thus obtaining the Time-decaying Bloom filter construction introduced in [5]. Addition of a new quantity is performed exactly as in the case of a traditional CBF, whereas all the CBF bins are periodically reduced by a factor α at the end of each monitoring window W .…”

Section: Window-based Tbf Constructionmentioning

confidence: 99%

“…A necessary first step is to extend the EWMA filtering from the discrete step-based operation provided by (5), to the continuous time case. This can be accomplished by taking the limit for W → 0 in (5).…”

Section: Single Counter Casementioning

confidence: 99%

“…called Time-decaying Bloom filter [5], devised to implement an Exponentially Weighted Moving Average (EWMA) filtering of (rate) measurements. We show that its windowbased operation yields two important shortcomings in terms of both accuracy (results being biased when sampled at arbitrary instants of time) and performance (suffering from the need to periodically update all the filter's counters at once).…”

Section: Introductionmentioning

confidence: 99%

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On-demand time-decaying bloom filters for telemarketer detection

Bianchi

d’Heureuse²,

Niccolini³

2011

SIGCOMM Comput. Commun. Rev.

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Several traffic monitoring applications may benefit from the availability of efficient mechanisms for approximately tracking smoothed time averages rather than raw counts. This paper provides two contributions in this direction. First, our analysis of Time-decaying Bloom filters, formerly proposed data structures devised to perform approximate Exponentially Weighted Moving Averages on streaming data, reveals two major shortcomings: biased estimation when measurements are read in arbitrary time instants, and slow operation resulting from the need to periodically update all the filter's counters at once. We thus propose a new construction, called On-demand Time-decaying Bloom filter, which relies on a continuous-time operation to overcome the accuracy/performance limitations of the original window-based approach. Second, we show how this new technique can be exploited in the design of high performance stream-based monitoring applications, by developing VoIPSTREAM, a proof-of-concept real-time analysis version of a formerly proposed system for telemarketing call detection. Our validation results, carried out over real telephony data, show how VoIPSTREAM closely mimics the feature extraction process and traffic analysis techniques implemented in the offline system, at a significantly higher processing speed, and without requiring any storage of per-user call detail records.

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Section: On-demand Time-decaying Bloom Filtermentioning

confidence: 99%

Section: Window-based Tbfmentioning

confidence: 99%

Section: Window-based Tbf Constructionmentioning

confidence: 99%

Section: Single Counter Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On-demand time-decaying bloom filters for telemarketer detection

Bianchi

d’Heureuse²,

Niccolini³

2011

SIGCOMM Comput. Commun. Rev.

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CoFeed: privacy‐preserving Web search recommendation based on collaborative aggregation of interest feedback

et al. 2011

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SUMMARY Search engines essentially rely on the structure of the graph of hyperlinks. Although accurate for the main trend, this is not effective when some query is ambiguous. Leveraging semantic information by the mean of interest matching allows proposing complementary results that are tailored to the user's expectations. This paper proposes a collaborative search companion system, CoFeed, that collects user search queries and that considers feedback to build user‐centric and document‐centric profiling information. Over time, the system constructs ranked collections of elements that maintain the required information diversity and enhance the user search experience by presenting additional results tailored to the user's interest space. This collaborative search companion requires a supporting architecture adapted to large user populations generating high request loads. To that end, it integrates mechanisms for ensuring scalability and load balancing of the service under varying loads and user interest distributions. Moreover, collecting the recommendation data poses the problem of users’ privacy, and the bias one peer can induce to the system by sending fake recommendations. To that end, CoFeed ensures both publisher anonymity and rate limitation. With the former, the origin of the data is never known by the server that processes it, even if several servers collude to spy on some user. The latter, combined with decoupled authentication, allows to minimize the influence of cheating peers sending fake recommendations. Experiments with a deployed prototype highlight the efficiency of the system by analyzing improvement in search relevance, computational cost, scalability and load balancing. Copyright © 2011 John Wiley & Sons, Ltd.

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